CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: uncertainty of model predictions</title> <meta name="description" content="Search results for: uncertainty of model predictions"> <meta name="keywords" content="uncertainty of model predictions"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="uncertainty of model predictions" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="uncertainty of model predictions"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 17629</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: uncertainty of model predictions</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17629</span> Uncertainty Estimation in Neural Networks through Transfer Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20James">Ashish James</a>, <a href="https://publications.waset.org/abstracts/search?q=Anusha%20James"> Anusha James</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20estimation" title="uncertainty estimation">uncertainty estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/153501/uncertainty-estimation-in-neural-networks-through-transfer-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153501.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17628</span> Metrology-Inspired Methods to Assess the Biases of Artificial Intelligence Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Laimouche">Belkacem Laimouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=metrology" title=" metrology"> metrology</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20uncertainty" title=" measurement uncertainty"> measurement uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20error" title=" prediction error"> prediction error</a>, <a href="https://publications.waset.org/abstracts/search?q=bias" title=" bias"> bias</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20algorithms" title=" machine learning algorithms"> machine learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20models" title=" probabilistic models"> probabilistic models</a>, <a href="https://publications.waset.org/abstracts/search?q=interlaboratory%20comparison" title=" interlaboratory comparison"> interlaboratory comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20reliability" title=" data reliability"> data reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20of%20bias%20impact%20on%20predictions" title=" measurement of bias impact on predictions"> measurement of bias impact on predictions</a>, <a href="https://publications.waset.org/abstracts/search?q=improvement%20of%20model%20accuracy%20and%20reliability" title=" improvement of model accuracy and reliability"> improvement of model accuracy and reliability</a> </p> <a href="https://publications.waset.org/abstracts/167404/metrology-inspired-methods-to-assess-the-biases-of-artificial-intelligence-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167404.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17627</span> Artificial Neural Network to Predict the Optimum Performance of Air Conditioners under Environmental Conditions in Saudi Arabia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amr%20Sadek">Amr Sadek</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelrahaman%20Al-Qahtany"> Abdelrahaman Al-Qahtany</a>, <a href="https://publications.waset.org/abstracts/search?q=Turkey%20Salem%20Al-Qahtany"> Turkey Salem Al-Qahtany</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions" title=" uncertainty of model predictions"> uncertainty of model predictions</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency%20of%20air%20conditioners" title=" efficiency of air conditioners"> efficiency of air conditioners</a>, <a href="https://publications.waset.org/abstracts/search?q=cooling%20and%20heating%20capacities" title=" cooling and heating capacities"> cooling and heating capacities</a> </p> <a href="https://publications.waset.org/abstracts/172795/artificial-neural-network-to-predict-the-optimum-performance-of-air-conditioners-under-environmental-conditions-in-saudi-arabia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17626</span> Statistical Classification, Downscaling and Uncertainty Assessment for Global Climate Model Outputs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Queen%20Suraajini%20Rajendran">Queen Suraajini Rajendran</a>, <a href="https://publications.waset.org/abstracts/search?q=Sai%20Hung%20Cheung"> Sai Hung Cheung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20downscaling" title="statistical downscaling">statistical downscaling</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20climate%20model" title=" global climate model"> global climate model</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title=" climate change"> climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/18056/statistical-classification-downscaling-and-uncertainty-assessment-for-global-climate-model-outputs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18056.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">368</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17625</span> Determination of Measurement Uncertainty of the Diagnostic Meteorological Model CALMET</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nina%20Miklav%C4%8Di%C4%8D">Nina Miklavčič</a>, <a href="https://publications.waset.org/abstracts/search?q=Ur%C5%A1ka%20Kugovnik"> Urška Kugovnik</a>, <a href="https://publications.waset.org/abstracts/search?q=Natalia%20Galkina"> Natalia Galkina</a>, <a href="https://publications.waset.org/abstracts/search?q=Primo%C5%BE%20Ribari%C4%8D"> Primož Ribarič</a>, <a href="https://publications.waset.org/abstracts/search?q=Rudi%20Von%C4%8Dina"> Rudi Vončina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, the need for weather predictions is deeply rooted in the everyday life of people as well as it is in industry. The forecasts influence final decision-making processes in multiple areas, from agriculture and prevention of natural disasters to air traffic regulations and solutions on a national level for health, security, and economic problems. Namely, in Slovenia, alongside other existing forms of application, weather forecasts are adopted for the prognosis of electrical current transmission through powerlines. Meteorological parameters are one of the key factors which need to be considered in estimations of the reliable supply of electrical energy to consumers. And like for any other measured value, the knowledge about measurement uncertainty is also critical for the secure and reliable supply of energy. The estimation of measurement uncertainty grants us a more accurate interpretation of data, a better quality of the end results, and even a possibility of improvement of weather forecast models. In the article, we focused on the estimation of measurement uncertainty of the diagnostic microscale meteorological model CALMET. For the purposes of our research, we used a network of meteorological stations spread in the area of our interest, which enables a side-by-side comparison of measured meteorological values with the values calculated with the help of CALMET and the measurement uncertainty estimation as a final result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertancy" title="uncertancy">uncertancy</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20model" title=" meteorological model"> meteorological model</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20measurment" title=" meteorological measurment"> meteorological measurment</a>, <a href="https://publications.waset.org/abstracts/search?q=CALMET" title=" CALMET"> CALMET</a> </p> <a href="https://publications.waset.org/abstracts/171084/determination-of-measurement-uncertainty-of-the-diagnostic-meteorological-model-calmet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171084.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">81</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17624</span> Uncertainty in Building Energy Performance Analysis at Different Stages of the Building’s Lifecycle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elham%20Delzendeh">Elham Delzendeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Song%20Wu"> Song Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Al-Adhami"> Mustafa Al-Adhami</a>, <a href="https://publications.waset.org/abstracts/search?q=Rima%20Alaaeddine"> Rima Alaaeddine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the last 15 years, prediction of energy consumption has become a common practice and necessity at different stages of the building’s lifecycle, particularly, at the design and post-occupancy stages for planning and maintenance purposes. This is due to the ever-growing response of governments to address sustainability and reduction of CO₂ emission in the building sector. However, there is a level of uncertainty in the estimation of energy consumption in buildings. The accuracy of energy consumption predictions is directly related to the precision of the initial inputs used in the energy assessment process. In this study, multiple cases of large non-residential buildings at design, construction, and post-occupancy stages are investigated. The energy consumption process and inputs, and the actual and predicted energy consumption of the cases are analysed. The findings of this study have pointed out and evidenced various parameters that cause uncertainty in the prediction of energy consumption in buildings such as modelling, location data, and occupant behaviour. In addition, unavailability and insufficiency of energy-consumption-related inputs at different stages of the building’s lifecycle are classified and categorized. Understanding the roots of uncertainty in building energy analysis will help energy modellers and energy simulation software developers reach more accurate energy consumption predictions in buildings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20lifecycle" title="building lifecycle">building lifecycle</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency" title=" efficiency"> efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20analysis" title=" energy analysis"> energy analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20performance" title=" energy performance"> energy performance</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/111629/uncertainty-in-building-energy-performance-analysis-at-different-stages-of-the-buildings-lifecycle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111629.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17623</span> Epistemic Uncertainty Analysis of Queue with Vacations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Baya%20Takhedmit">Baya Takhedmit</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Abbas"> Karim Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofiane%20Ouazine"> Sofiane Ouazine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The vacations queues are often employed to model many real situations such as computer systems, communication networks, manufacturing and production systems, transportation systems and so forth. These queueing models are solved at fixed parameters values. However, the parameter values themselves are determined from a finite number of observations and hence have uncertainty associated with them (epistemic uncertainty). In this paper, we consider the M/G/1/N queue with server vacation and exhaustive discipline where we assume that the vacation parameter values have uncertainty. We use the Taylor series expansions approach to estimate the expectation and variance of model output, due to epistemic uncertainties in the model input parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epistemic%20uncertainty" title="epistemic uncertainty">epistemic uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=M%2FG%2F1%2FN%20queue%20with%20vacations" title=" M/G/1/N queue with vacations"> M/G/1/N queue with vacations</a>, <a href="https://publications.waset.org/abstracts/search?q=non-parametric%20sensitivity%20analysis" title=" non-parametric sensitivity analysis"> non-parametric sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Taylor%20series%20expansion" title=" Taylor series expansion"> Taylor series expansion</a> </p> <a href="https://publications.waset.org/abstracts/63375/epistemic-uncertainty-analysis-of-queue-with-vacations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63375.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">433</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17622</span> Uncertainty in Risk Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mueller%20Jann">Mueller Jann</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoffmann%20Christian%20Hugo"> Hoffmann Christian Hugo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conventional quantitative risk management in banking is a risk factor of its own, because it rests on assumptions such as independence and availability of data which do not hold when rare events of extreme consequences are involved. There is a growing recognition of the need for alternative risk measures that do not make these assumptions. We propose a novel method for modeling the risk associated with investment products, in particular derivatives, by using a formal language for specifying financial contracts. Expressions in this language are interpreted in the category of values annotated with (a formal representation of) uncertainty. The choice of uncertainty formalism thus becomes a parameter of the model, so it can be adapted to the particular application and it is not constrained to classical probabilities. We demonstrate our approach using a simple logic-based uncertainty model and a case study in which we assess the risk of counter party default in a portfolio of collateralized loans. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=risk%20model" title="risk model">risk model</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20monad" title=" uncertainty monad"> uncertainty monad</a>, <a href="https://publications.waset.org/abstracts/search?q=derivatives" title=" derivatives"> derivatives</a>, <a href="https://publications.waset.org/abstracts/search?q=contract%20algebra" title=" contract algebra"> contract algebra</a> </p> <a href="https://publications.waset.org/abstracts/28143/uncertainty-in-risk-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28143.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">576</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17621</span> Good Practices for Model Structure Development and Managing Structural Uncertainty in Decision Making </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Afzali">Hossein Afzali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Increasingly, decision analytic models are used to inform decisions about whether or not to publicly fund new health technologies. It is well noted that the accuracy of model predictions is strongly influenced by the appropriateness of model structuring. However, there is relatively inadequate methodological guidance surrounding this issue in guidelines developed by national funding bodies such as the Australian Pharmaceutical Benefits Advisory Committee (PBAC) and The National Institute for Health and Care Excellence (NICE) in the UK. This presentation aims to discuss issues around model structuring within decision making with a focus on (1) the need for a transparent and evidence-based model structuring process to inform the most appropriate set of structural aspects as the base case analysis; (2) the need to characterise structural uncertainty (If there exist alternative plausible structural assumptions (or judgements), there is a need to appropriately characterise the related structural uncertainty). The presentation will provide an opportunity to share ideas and experiences on how the guidelines developed by national funding bodies address the above issues and identify areas for further improvements. First, a review and analysis of the literature and guidelines developed by PBAC and NICE will be provided. Then, it will be discussed how the issues around model structuring (including structural uncertainty) are not handled and justified in a systematic way within the decision-making process, its potential impact on the quality of public funding decisions, and how it should be presented in submissions to national funding bodies. This presentation represents a contribution to the good modelling practice within the decision-making process. Although the presentation focuses on the PBAC and NICE guidelines, the discussion can be applied more widely to many other national funding bodies that use economic evaluation to inform funding decisions but do not transparently address model structuring issues e.g. the Medical Services Advisory Committee (MSAC) in Australia or the Canadian Agency for Drugs and Technologies in Health. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision-making%20process" title="decision-making process">decision-making process</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20evaluation" title=" economic evaluation"> economic evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=good%20modelling%20practice" title=" good modelling practice"> good modelling practice</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20uncertainty" title=" structural uncertainty"> structural uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/82246/good-practices-for-model-structure-development-and-managing-structural-uncertainty-in-decision-making" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82246.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17620</span> Uncertainty and Optimization Analysis Using PETREL RE</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ankur%20Sachan">Ankur Sachan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ability to make quick yet intelligent and value-added decisions to develop new fields has always been of great significance. In situations where the capital expenses and subsurface risk are high, carefully analyzing the inherent uncertainties in the reservoir and how they impact the predicted hydrocarbon accumulation and production becomes a daunting task. The problem is compounded in offshore environments, especially in the presence of heavy oils and disconnected sands where the margin for error is small. Uncertainty refers to the degree to which the data set may be in error or stray from the predicted values. To understand and quantify the uncertainties in reservoir model is important when estimating the reserves. Uncertainty parameters can be geophysical, geological, petrophysical etc. Identification of these parameters is necessary to carry out the uncertainty analysis. With so many uncertainties working at different scales, it becomes essential to have a consistent and efficient way of incorporating them into our analysis. Ranking the uncertainties based on their impact on reserves helps to prioritize/ guide future data gathering and uncertainty reduction efforts. Assigning probabilistic ranges to key uncertainties also enables the computation of probabilistic reserves. With this in mind, this paper, with the help the uncertainty and optimization process in petrel RE shows how the most influential uncertainties can be determined efficiently and how much impact so they have on the reservoir model thus helping in determining a cost effective and accurate model of the reservoir. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title="uncertainty">uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=reservoir%20model" title=" reservoir model"> reservoir model</a>, <a href="https://publications.waset.org/abstracts/search?q=parameters" title=" parameters"> parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20analysis" title=" optimization analysis"> optimization analysis</a> </p> <a href="https://publications.waset.org/abstracts/21057/uncertainty-and-optimization-analysis-using-petrel-re" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21057.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">652</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17619</span> Reliability Based Topology Optimization: An Efficient Method for Material Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Jalalpour">Mehdi Jalalpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mazdak%20Tootkaboni"> Mazdak Tootkaboni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a computationally efficient method for reliability-based topology optimization under material properties uncertainty, which is assumed to be lognormally distributed and correlated within the domain. Computational efficiency is achieved through estimating the response statistics with stochastic perturbation of second order, using these statistics to fit an appropriate distribution that follows the empirical distribution of the response, and employing an efficient gradient-based optimizer. The proposed algorithm is utilized for design of new structures and the changes in the optimized topology is discussed for various levels of target reliability and correlation strength. Predictions were verified thorough comparison with results obtained using Monte Carlo simulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=material%20uncertainty" title="material uncertainty">material uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20perturbation" title=" stochastic perturbation"> stochastic perturbation</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20reliability" title=" structural reliability"> structural reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=topology%20optimization" title=" topology optimization"> topology optimization</a> </p> <a href="https://publications.waset.org/abstracts/24499/reliability-based-topology-optimization-an-efficient-method-for-material-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24499.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">605</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17618</span> A Robust Optimization Model for Multi-Objective Closed-Loop Supply Chain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Y.%20Badiee">Mohammad Y. Badiee</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Golestani"> Saeed Golestani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mir%20Saman%20Pishvaee"> Mir Saman Pishvaee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years consumers and governments have been pushing companies to design their activities in such a way as to reduce negative environmental impacts by producing renewable product or threat free disposal policy more and more. It is therefore important to focus more accurate to the optimization of various aspect of total supply chain. Modeling a supply chain can be a challenging process due to the fact that there are a large number of factors that need to be considered in the model. The use of multi-objective optimization can lead to overcome those problems since more information is used when designing the model. Uncertainty is inevitable in real world. Considering uncertainty on parameters in addition to use multi-objectives are ways to give more flexibility to the decision making process since the process can take into account much more constraints and requirements. In this paper we demonstrate a stochastic scenario based robust model to cope with uncertainty in a closed-loop multi-objective supply chain. By applying the proposed model in a real world case, the power of proposed model in handling data uncertainty is shown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title="supply chain management">supply chain management</a>, <a href="https://publications.waset.org/abstracts/search?q=closed-loop%20supply%20chain" title=" closed-loop supply chain"> closed-loop supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=goal%20programming" title=" goal programming"> goal programming</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20optimization" title=" robust optimization"> robust optimization</a> </p> <a href="https://publications.waset.org/abstracts/39139/a-robust-optimization-model-for-multi-objective-closed-loop-supply-chain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39139.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17617</span> Mind Your Product-Market Strategy on Selecting Marketing Inputs: An Uncertainty Approach in Indian Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Susmita%20Ghosh">Susmita Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Bhaskar%20Bhowmick"> Bhaskar Bhowmick</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Market is an important factor for start-ups to look into during decision-making in product development and related areas. Emerging country markets are more uncertain in terms of information availability and institutional supports. The literature review of market uncertainty reveals the need for identifying factors representing the market uncertainty. This paper identifies factors for market uncertainty using Exploratory Factor Analysis (EFA) and confirms the number of factor retention using an alternative factor retention criterion, ‘Parallel Analysis’. 500 entrepreneurs, engaged in start-ups from all over India participated in the study. This paper concludes with the factor structure of ‘market uncertainty’ having dimensions of uncertainty in industry orientation, uncertainty in customer orientation and uncertainty in marketing orientation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title="uncertainty">uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=market" title=" market"> market</a>, <a href="https://publications.waset.org/abstracts/search?q=orientation" title=" orientation"> orientation</a>, <a href="https://publications.waset.org/abstracts/search?q=competitor" title=" competitor"> competitor</a>, <a href="https://publications.waset.org/abstracts/search?q=demand" title=" demand "> demand </a> </p> <a href="https://publications.waset.org/abstracts/24877/mind-your-product-market-strategy-on-selecting-marketing-inputs-an-uncertainty-approach-in-indian-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24877.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">590</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17616</span> Establishment of the Regression Uncertainty of the Critical Heat Flux Power Correlation for an Advanced Fuel Bundle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Q.%20Yuan">L. Q. Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Yang"> J. Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Siddiqui"> A. Siddiqui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new regression uncertainty analysis methodology was applied to determine the uncertainties of the critical heat flux (CHF) power correlation for an advanced 43-element bundle design, which was developed by Canadian Nuclear Laboratories (CNL) to achieve improved economics, resource utilization and energy sustainability. The new methodology is considered more appropriate than the traditional methodology in the assessment of the experimental uncertainty associated with regressions. The methodology was first assessed using both the Monte Carlo Method (MCM) and the Taylor Series Method (TSM) for a simple linear regression model, and then extended successfully to a non-linear CHF power regression model (CHF power as a function of inlet temperature, outlet pressure and mass flow rate). The regression uncertainty assessed by MCM agrees well with that by TSM. An equation to evaluate the CHF power regression uncertainty was developed and expressed as a function of independent variables that determine the CHF power. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CHF%20experiment" title="CHF experiment">CHF experiment</a>, <a href="https://publications.waset.org/abstracts/search?q=CHF%20correlation" title=" CHF correlation"> CHF correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20uncertainty" title=" regression uncertainty"> regression uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20Method" title=" Monte Carlo Method"> Monte Carlo Method</a>, <a href="https://publications.waset.org/abstracts/search?q=Taylor%20Series%20Method" title=" Taylor Series Method"> Taylor Series Method</a> </p> <a href="https://publications.waset.org/abstracts/77556/establishment-of-the-regression-uncertainty-of-the-critical-heat-flux-power-correlation-for-an-advanced-fuel-bundle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77556.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17615</span> Modeling Stream Flow with Prediction Uncertainty by Using SWAT Hydrologic and RBNN Neural Network Models for Agricultural Watershed in India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ajai%20Singh">Ajai Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SWAT" title="SWAT">SWAT</a>, <a href="https://publications.waset.org/abstracts/search?q=RBNN" title=" RBNN"> RBNN</a>, <a href="https://publications.waset.org/abstracts/search?q=SUFI%202" title=" SUFI 2"> SUFI 2</a>, <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20technique" title=" bootstrap technique"> bootstrap technique</a>, <a href="https://publications.waset.org/abstracts/search?q=stream%20flow" title=" stream flow"> stream flow</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/21788/modeling-stream-flow-with-prediction-uncertainty-by-using-swat-hydrologic-and-rbnn-neural-network-models-for-agricultural-watershed-in-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21788.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">370</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17614</span> A Comparative Study of Sampling-Based Uncertainty Propagation with First Order Error Analysis and Percentile-Based Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Gulam%20Kibria">M. Gulam Kibria</a>, <a href="https://publications.waset.org/abstracts/search?q=Shourav%20Ahmed"> Shourav Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kais%20Zaman"> Kais Zaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In system analysis, the information on the uncertain input variables cause uncertainty in the system responses. Different probabilistic approaches for uncertainty representation and propagation in such cases exist in the literature. Different uncertainty representation approaches result in different outputs. Some of the approaches might result in a better estimation of system response than the other approaches. The NASA Langley Multidisciplinary Uncertainty Quantification Challenge (MUQC) has posed challenges about uncertainty quantification. Subproblem A, the uncertainty characterization subproblem, of the challenge posed is addressed in this study. In this subproblem, the challenge is to gather knowledge about unknown model inputs which have inherent aleatory and epistemic uncertainties in them with responses (output) of the given computational model. We use two different methodologies to approach the problem. In the first methodology we use sampling-based uncertainty propagation with first order error analysis. In the other approach we place emphasis on the use of Percentile-Based Optimization (PBO). The NASA Langley MUQC’s subproblem A is developed in such a way that both aleatory and epistemic uncertainties need to be managed. The challenge problem classifies each uncertain parameter as belonging to one the following three types: (i) An aleatory uncertainty modeled as a random variable. It has a fixed functional form and known coefficients. This uncertainty cannot be reduced. (ii) An epistemic uncertainty modeled as a fixed but poorly known physical quantity that lies within a given interval. This uncertainty is reducible. (iii) A parameter might be aleatory but sufficient data might not be available to adequately model it as a single random variable. For example, the parameters of a normal variable, e.g., the mean and standard deviation, might not be precisely known but could be assumed to lie within some intervals. It results in a distributional p-box having the physical parameter with an aleatory uncertainty, but the parameters prescribing its mathematical model are subjected to epistemic uncertainties. Each of the parameters of the random variable is an unknown element of a known interval. This uncertainty is reducible. From the study, it is observed that due to practical limitations or computational expense, the sampling is not exhaustive in sampling-based methodology. That is why the sampling-based methodology has high probability of underestimating the output bounds. Therefore, an optimization-based strategy to convert uncertainty described by interval data into a probabilistic framework is necessary. This is achieved in this study by using PBO. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aleatory%20uncertainty" title="aleatory uncertainty">aleatory uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=epistemic%20uncertainty" title=" epistemic uncertainty"> epistemic uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=first%20order%20error%20analysis" title=" first order error analysis"> first order error analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20quantification" title=" uncertainty quantification"> uncertainty quantification</a>, <a href="https://publications.waset.org/abstracts/search?q=percentile-based%20optimization" title=" percentile-based optimization"> percentile-based optimization</a> </p> <a href="https://publications.waset.org/abstracts/90749/a-comparative-study-of-sampling-based-uncertainty-propagation-with-first-order-error-analysis-and-percentile-based-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90749.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">240</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17613</span> Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watcharin%20Sangma">Watcharin Sangma</a>, <a href="https://publications.waset.org/abstracts/search?q=Onsiri%20Chanmuang"> Onsiri Chanmuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pitsanu%20Tongkhow"> Pitsanu Tongkhow</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting%20model" title="forecasting model">forecasting model</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20demand%20uncertainty" title=" steel demand uncertainty"> steel demand uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20Bayesian%20framework" title=" hierarchical Bayesian framework"> hierarchical Bayesian framework</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20smoothing%20method" title=" exponential smoothing method"> exponential smoothing method</a> </p> <a href="https://publications.waset.org/abstracts/10196/forecasting-models-for-steel-demand-uncertainty-using-bayesian-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10196.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">350</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17612</span> Parameter Estimation with Uncertainty and Sensitivity Analysis for the SARS Outbreak in Hong Kong</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afia%20Naheed">Afia Naheed</a>, <a href="https://publications.waset.org/abstracts/search?q=Manmohan%20Singh"> Manmohan Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Lucy"> David Lucy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is based on a mathematical as well as statistical study of an SEIJTR deterministic model for the interpretation of transmission of severe acute respiratory syndrome (SARS). Based on the SARS epidemic in 2003, the parameters are estimated using Runge-Kutta (Dormand-Prince pairs) and least squares methods. Possible graphical and numerical techniques are used to validate the estimates. Then effect of the model parameters on the dynamics of the disease is examined using sensitivity and uncertainty analysis. Sensitivity and uncertainty analytical techniques are used in order to analyze the affect of the uncertainty in the obtained parameter estimates and to determine which parameters have the largest impact on controlling the disease dynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=infectious%20disease" title="infectious disease">infectious disease</a>, <a href="https://publications.waset.org/abstracts/search?q=severe%20acute%20respiratory%20syndrome%20%28SARS%29" title=" severe acute respiratory syndrome (SARS)"> severe acute respiratory syndrome (SARS)</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20analysis" title=" uncertainty analysis"> uncertainty analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Runge-Kutta%20methods" title=" Runge-Kutta methods"> Runge-Kutta methods</a>, <a href="https://publications.waset.org/abstracts/search?q=Levenberg-Marquardt%20method" title=" Levenberg-Marquardt method"> Levenberg-Marquardt method</a> </p> <a href="https://publications.waset.org/abstracts/8087/parameter-estimation-with-uncertainty-and-sensitivity-analysis-for-the-sars-outbreak-in-hong-kong" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8087.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">361</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17611</span> A Deep Learning Based Integrated Model For Spatial Flood Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinayaka%20Gude%20Divya%20Sampath">Vinayaka Gude Divya Sampath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster%20management" title=" disaster management"> disaster management</a>, <a href="https://publications.waset.org/abstracts/search?q=flood%20prediction" title=" flood prediction"> flood prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20flooding" title=" urban flooding"> urban flooding</a> </p> <a href="https://publications.waset.org/abstracts/129566/a-deep-learning-based-integrated-model-for-spatial-flood-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129566.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17610</span> Wind Power Forecast Error Simulation Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Josip%20Vasilj">Josip Vasilj</a>, <a href="https://publications.waset.org/abstracts/search?q=Petar%20Sarajcev"> Petar Sarajcev</a>, <a href="https://publications.waset.org/abstracts/search?q=Damir%20Jakus"> Damir Jakus</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the major difficulties introduced with wind power penetration is the inherent uncertainty in production originating from uncertain wind conditions. This uncertainty impacts many different aspects of power system operation, especially the balancing power requirements. For this reason, in power system development planing, it is necessary to evaluate the potential uncertainty in future wind power generation. For this purpose, simulation models are required, reproducing the performance of wind power forecasts. This paper presents a wind power forecast error simulation models which are based on the stochastic process simulation. Proposed models capture the most important statistical parameters recognized in wind power forecast error time series. Furthermore, two distinct models are presented based on data availability. First model uses wind speed measurements on potential or existing wind power plant locations, while the seconds model uses statistical distribution of wind speeds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20power" title="wind power">wind power</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20process" title=" stochastic process"> stochastic process</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/17977/wind-power-forecast-error-simulation-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17977.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17609</span> Constructing a Probabilistic Ontology from a DBLP Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emna%20Hlel">Emna Hlel</a>, <a href="https://publications.waset.org/abstracts/search?q=Salma%20Jamousi"> Salma Jamousi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelmajid%20Ben%20Hamadou"> Abdelmajid Ben Hamadou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Every model for knowledge representation to model real-world applications must be able to cope with the effects of uncertain phenomena. One of main defects of classical ontology is its inability to represent and reason with uncertainty. To remedy this defect, we try to propose a method to construct probabilistic ontology for integrating uncertain information in an ontology modeling a set of basic publications DBLP (Digital Bibliography & Library Project) using a probabilistic model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classical%20ontology" title="classical ontology">classical ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20ontology" title=" probabilistic ontology"> probabilistic ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title=" Bayesian network"> Bayesian network</a> </p> <a href="https://publications.waset.org/abstracts/24225/constructing-a-probabilistic-ontology-from-a-dblp-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24225.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17608</span> Improving Predictions of Coastal Benthic Invertebrate Occurrence and Density Using a Multi-Scalar Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stephanie%20Watson">Stephanie Watson</a>, <a href="https://publications.waset.org/abstracts/search?q=Fabrice%20Stephenson"> Fabrice Stephenson</a>, <a href="https://publications.waset.org/abstracts/search?q=Conrad%20Pilditch"> Conrad Pilditch</a>, <a href="https://publications.waset.org/abstracts/search?q=Carolyn%20Lundquist"> Carolyn Lundquist</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benthic%20ecology" title="Benthic ecology">Benthic ecology</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20modelling" title=" spatial modelling"> spatial modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scalar%20modelling" title=" multi-scalar modelling"> multi-scalar modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=marine%20conservation." title=" marine conservation."> marine conservation.</a> </p> <a href="https://publications.waset.org/abstracts/156434/improving-predictions-of-coastal-benthic-invertebrate-occurrence-and-density-using-a-multi-scalar-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156434.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">77</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17607</span> Examining the Dynamics of FDI Inflows in Both BRICS and G7 Economies: Dissecting the Influence of Geopolitical Risk versus Economic Policy Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adelakun%20O.%20Johnson">Adelakun O. Johnson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quest to mitigate the probable adverse effects of geopolitical risk on FDI inflows tends to result in more frequent changes in economic policies and, as a result, heightened policy uncertainty. In this regard, we extend the literature on the dynamics of FDI inflows to include the hypothesis of the possibility of geopolitical risk escalating the adverse effects of economic policy uncertainty on FDI inflows. To test the robustness of this hypothesis, we use the cases of different economic groups characterized by different levels of economic development and varying degrees of FDI confidence. Employing an ARDL-based dynamic panel data model that accounts for both non-stationarity and heterogeneity effects, we show result that suggests GPR and EPU retard the inflows of FDI in both economies but mainly in the short-run situation. In the long run, however, higher EPU not attributed to GPR is likely to boost the inflows of FDI rather than retarding, at least in the case of the G7 economy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FDI%20inflows" title="FDI inflows">FDI inflows</a>, <a href="https://publications.waset.org/abstracts/search?q=geopolitical%20risk" title=" geopolitical risk"> geopolitical risk</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20policy%20uncertainty" title=" economic policy uncertainty"> economic policy uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=panel%20ARDL%20model" title=" panel ARDL model"> panel ARDL model</a> </p> <a href="https://publications.waset.org/abstracts/190114/examining-the-dynamics-of-fdi-inflows-in-both-brics-and-g7-economies-dissecting-the-influence-of-geopolitical-risk-versus-economic-policy-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190114.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">24</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17606</span> Consideration of Uncertainty in Engineering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Mohammadi">A. Mohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Moghimi"> M. Moghimi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mohammadi"> S. Mohammadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Engineers need computational methods which could provide solutions less sensitive to the environmental effects, so the techniques should be used which take the uncertainty to account to control and minimize the risk associated with design and operation. In order to consider uncertainty in engineering problem, the optimization problem should be solved for a suitable range of the each uncertain input variable instead of just one estimated point. Using deterministic optimization problem, a large computational burden is required to consider every possible and probable combination of uncertain input variables. Several methods have been reported in the literature to deal with problems under uncertainty. In this paper, different methods presented and analyzed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title="uncertainty">uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulated" title=" Monte Carlo simulated"> Monte Carlo simulated</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20programming" title=" stochastic programming"> stochastic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=scenario%20method" title=" scenario method"> scenario method</a> </p> <a href="https://publications.waset.org/abstracts/7164/consideration-of-uncertainty-in-engineering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7164.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">414</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17605</span> Application of Harris Hawks Optimization Metaheuristic Algorithm and Random Forest Machine Learning Method for Long-Term Production Scheduling Problem under Uncertainty in Open-Pit Mines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamyar%20Tolouei">Kamyar Tolouei</a>, <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Moosavi"> Ehsan Moosavi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grade%20uncertainty" title="grade uncertainty">grade uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20algorithms" title=" metaheuristic algorithms"> metaheuristic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=open-pit%20mine" title=" open-pit mine"> open-pit mine</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20scheduling%20optimization" title=" production scheduling optimization"> production scheduling optimization</a> </p> <a href="https://publications.waset.org/abstracts/146657/application-of-harris-hawks-optimization-metaheuristic-algorithm-and-random-forest-machine-learning-method-for-long-term-production-scheduling-problem-under-uncertainty-in-open-pit-mines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146657.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17604</span> Simulation of Optimal Runoff Hydrograph Using Ensemble of Radar Rainfall and Blending of Runoffs Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Myungjin%20Lee">Myungjin Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Daegun%20Han"> Daegun Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Jongsung%20Kim"> Jongsung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soojun%20Kim"> Soojun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hung%20Soo%20Kim"> Hung Soo Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, the localized heavy rainfall and typhoons are frequently occurred due to the climate change and the damage is becoming bigger. Therefore, we may need a more accurate prediction of the rainfall and runoff. However, the gauge rainfall has the limited accuracy in space. Radar rainfall is better than gauge rainfall for the explanation of the spatial variability of rainfall but it is mostly underestimated with the uncertainty involved. Therefore, the ensemble of radar rainfall was simulated using error structure to overcome the uncertainty and gauge rainfall. The simulated ensemble was used as the input data of the rainfall-runoff models for obtaining the ensemble of runoff hydrographs. The previous studies discussed about the accuracy of the rainfall-runoff model. Even if the same input data such as rainfall is used for the runoff analysis using the models in the same basin, the models can have different results because of the uncertainty involved in the models. Therefore, we used two models of the SSARR model which is the lumped model, and the Vflo model which is a distributed model and tried to simulate the optimum runoff considering the uncertainty of each rainfall-runoff model. The study basin is located in Han river basin and we obtained one integrated runoff hydrograph which is an optimum runoff hydrograph using the blending methods such as Multi-Model Super Ensemble (MMSE), Simple Model Average (SMA), Mean Square Error (MSE). From this study, we could confirm the accuracy of rainfall and rainfall-runoff model using ensemble scenario and various rainfall-runoff model and we can use this result to study flood control measure due to climate change. Acknowledgements: This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B083066-05). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radar%20rainfall%20ensemble" title="radar rainfall ensemble">radar rainfall ensemble</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall-runoff%20models" title=" rainfall-runoff models"> rainfall-runoff models</a>, <a href="https://publications.waset.org/abstracts/search?q=blending%20method" title=" blending method"> blending method</a>, <a href="https://publications.waset.org/abstracts/search?q=optimum%20runoff%20hydrograph" title=" optimum runoff hydrograph"> optimum runoff hydrograph</a> </p> <a href="https://publications.waset.org/abstracts/76203/simulation-of-optimal-runoff-hydrograph-using-ensemble-of-radar-rainfall-and-blending-of-runoffs-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76203.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17603</span> Agile Supply Chains and Its Dependency on Air Transport Mode: A Case Study in Amazon</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabiana%20Lucena%20Oliveira">Fabiana Lucena Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Aristides%20da%20Rocha%20Oliveira%20Junior"> Aristides da Rocha Oliveira Junior</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article discusses the dependence on air transport mode of agile supply chains. The agile supply chains are the result of the analysis of the uncertainty supply chain model, which ranks the supply chain, according to the respective product. Thus, understanding the Uncertainty Model and life cycle of products considered standard and innovative is critical to understanding these. The innovative character in the intersection of supply chains arising from the uncertainty model with its most appropriate transport mode. Consider here the variables availability, security and freight as determinants for choosing these modes. Therefore, the research problem is: How agile supply chains maintains logistics competitiveness, as these are dependent on air transport mode? A case study in Manaus Industrial Pole (MIP), an agglomeration model that includes six hundred industries from different backgrounds and billings, located in the Brazilian Amazon. The sample of companies surveyed include those companies whose products are classified in agile supply chains , as innovative and therefore live with the variable uncertainty in the demand for inputs or the supply of finished products. The results confirm the hypothesis that the dependency level of air transport mode is greater than fifty percent. It follows then, that maintain agile supply chain away from suppliers base is expensive (1) , and continuity analysis needs to be remade on each twenty four months (2) , consider that additional freight, handling and storage as members of the logistics costs (3) , and the comparison with the upcoming agile supply chains the world need to consider the location effect (4). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20model" title="uncertainty model">uncertainty model</a>, <a href="https://publications.waset.org/abstracts/search?q=air%20transport%20mode" title=" air transport mode"> air transport mode</a>, <a href="https://publications.waset.org/abstracts/search?q=competitiveness" title=" competitiveness"> competitiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=logistics" title=" logistics "> logistics </a> </p> <a href="https://publications.waset.org/abstracts/26173/agile-supply-chains-and-its-dependency-on-air-transport-mode-a-case-study-in-amazon" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26173.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">511</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17602</span> Application of Smplify-X Algorithm with Enhanced Gender Classifier in 3D Human Pose Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiahe%20Liu">Jiahe Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyang%20Yu"> Hongyang Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Miao%20Luo"> Miao Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Qian"> Feng Qian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SMPLX" title="SMPLX">SMPLX</a>, <a href="https://publications.waset.org/abstracts/search?q=mobileNet" title=" mobileNet"> mobileNet</a>, <a href="https://publications.waset.org/abstracts/search?q=gender%20classification" title=" gender classification"> gender classification</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20human%20reconstruction" title=" 3D human reconstruction"> 3D human reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/183520/application-of-smplify-x-algorithm-with-enhanced-gender-classifier-in-3d-human-pose-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183520.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">99</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17601</span> Competition and Cooperation of Prosumers in Cournot Games with Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yong-Heng%20Shi">Yong-Heng Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Hao"> Peng Hao</a>, <a href="https://publications.waset.org/abstracts/search?q=Bai-Chen%20Xie"> Bai-Chen Xie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solar prosumers are playing increasingly prominent roles in the power system. However, its uncertainty affects the outcomes and functions of the power market, especially in the asymmetric information environment. Therefore, an important issue is how to take effective measures to reduce the impact of uncertainty on market equilibrium. We propose a two-level stochastic differential game model to explore the Cournot decision problem of prosumers. In particular, we study the impact of punishment and cooperation mechanisms on the efficiency of the Cournot game in which prosumers face uncertainty. The results show that under the penalty mechanism of fixed and variable rates, producers and consumers tend to take conservative actions to hedge risks, and the variable rates mechanism is more reasonable. Compared with non-cooperative situations, prosumers can improve the efficiency of the game through cooperation, which we attribute to the superposition of market power and uncertainty reduction. In addition, the market environment of asymmetric information intensifies the role of uncertainty. It reduces social welfare but increases the income of prosumers. For regulators, promoting alliances is an effective measure to realize the integration, optimization, and stable grid connection of producers and consumers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cournot%20games" title="Cournot games">Cournot games</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20market" title=" power market"> power market</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=prosumer%20cooperation" title=" prosumer cooperation"> prosumer cooperation</a> </p> <a href="https://publications.waset.org/abstracts/163232/competition-and-cooperation-of-prosumers-in-cournot-games-with-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163232.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">107</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17600</span> Asymmetries in Monetary Policy Response: The Role of Uncertainty in the Case of Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elias%20Udeaja">Elias Udeaja</a>, <a href="https://publications.waset.org/abstracts/search?q=Elijah%20Udoh"> Elijah Udoh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Exploring an extended SVAR model (SVAR-X), we use the case of Nigeria to hypothesize for the role of uncertainty as the underlying source of asymmetries in the response of monetary policy to output and inflation. Deciphered the empirical finding is the potential of monetary policy exhibiting greater sensitive to shocks due to output growth than they do to shocks due to inflation in recession periods, while the reverse appears to be the case for a contractionary monetary policy. We also find the asymmetric preference in the response of monetary policy to changes in output and inflation as relatively more pronounced when we control for uncertainty as the underlying source of asymmetries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymmetry%20response" title="asymmetry response">asymmetry response</a>, <a href="https://publications.waset.org/abstracts/search?q=developing%20economies" title=" developing economies"> developing economies</a>, <a href="https://publications.waset.org/abstracts/search?q=monetary%20policy%20shocks" title=" monetary policy shocks"> monetary policy shocks</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/124497/asymmetries-in-monetary-policy-response-the-role-of-uncertainty-in-the-case-of-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124497.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=587">587</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=588">588</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=uncertainty%20of%20model%20predictions&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>