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forecasting</title> <meta name="description" content="Search results for: earthquake forecasting"> <meta name="keywords" content="earthquake forecasting"> <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 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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="earthquake forecasting"> <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> 1256</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: earthquake forecasting</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1256</span> The Problems of Current Earth Coordinate System for Earthquake Forecasting Using Single Layer Hierarchical Graph Neuron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benny%20Benyamin%20Nasution">Benny Benyamin Nasution</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahmat%20Widia%20Sembiring"> Rahmat Widia Sembiring</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Rahman%20Dalimunthe"> Abdul Rahman Dalimunthe</a>, <a href="https://publications.waset.org/abstracts/search?q=Nursiah%20Mustari"> Nursiah Mustari</a>, <a href="https://publications.waset.org/abstracts/search?q=Nisfan%20Bahri"> Nisfan Bahri</a>, <a href="https://publications.waset.org/abstracts/search?q=Berta%20br%20Ginting"> Berta br Ginting</a>, <a href="https://publications.waset.org/abstracts/search?q=Riadil%20Akhir%20Lubis"> Riadil Akhir Lubis</a>, <a href="https://publications.waset.org/abstracts/search?q=Rita%20Tavip%20Megawati"> Rita Tavip Megawati</a>, <a href="https://publications.waset.org/abstracts/search?q=Indri%20Dithisari"> Indri Dithisari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The earth coordinate system is an important part of an attempt for earthquake forecasting, such as the one using Single Layer Hierarchical Graph Neuron (SLHGN). However, there are a number of problems that need to be worked out before the coordinate system can be utilized for the forecaster. One example of those is that SLHGN requires that the focused area of an earthquake must be constructed in a grid-like form. In fact, within the current earth coordinate system, the same longitude-difference would produce different distances. This can be observed at the distance on the Equator compared to distance at both poles. To deal with such a problem, a coordinate system has been developed, so that it can be used to support the ongoing earthquake forecasting using SLHGN. Two important issues have been developed in this system: 1) each location is not represented through two-value (longitude and latitude), but only a single value, 2) the conversion of the earth coordinate system to the x-y cartesian system requires no angular formulas, which is therefore fast. The accuracy and the performance have not been measured yet, since earthquake data is difficult to obtain. However, the characteristics of the SLHGN results show a very promising answer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20graph%20neuron" title="hierarchical graph neuron">hierarchical graph neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20hierarchical%20graph%20neuron" title=" multidimensional hierarchical graph neuron"> multidimensional hierarchical graph neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20layer%20hierarchical%20graph%20neuron" title=" single layer hierarchical graph neuron"> single layer hierarchical graph neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20disaster%20forecasting" title=" natural disaster forecasting"> natural disaster forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake%20forecasting" title=" earthquake forecasting"> earthquake forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=earth%20coordinate%20system" title=" earth coordinate system"> earth coordinate system</a> </p> <a href="https://publications.waset.org/abstracts/118471/the-problems-of-current-earth-coordinate-system-for-earthquake-forecasting-using-single-layer-hierarchical-graph-neuron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118471.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">216</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">1255</span> Earthquake Identification to Predict Tsunami in Andalas Island, Indonesia Using Back Propagation Method and Fuzzy TOPSIS Decision Seconder</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhamad%20Aris%20Burhanudin">Muhamad Aris Burhanudin</a>, <a href="https://publications.waset.org/abstracts/search?q=Angga%20Firmansyas"> Angga Firmansyas</a>, <a href="https://publications.waset.org/abstracts/search?q=Bagus%20Jaya%20Santosa"> Bagus Jaya Santosa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earthquakes are natural hazard that can trigger the most dangerous hazard, tsunami. 26 December 2004, a giant earthquake occurred in north-west Andalas Island. It made giant tsunami which crushed Sumatra, Bangladesh, India, Sri Lanka, Malaysia and Singapore. More than twenty thousand people dead. The occurrence of earthquake and tsunami can not be avoided. But this hazard can be mitigated by earthquake forecasting. Early preparation is the key factor to reduce its damages and consequences. We aim to investigate quantitatively on pattern of earthquake. Then, we can know the trend. We study about earthquake which has happened in Andalas island, Indonesia one last decade. Andalas is island which has high seismicity, more than a thousand event occur in a year. It is because Andalas island is in tectonic subduction zone of Hindia sea plate and Eurasia plate. A tsunami forecasting is needed to mitigation action. Thus, a Tsunami Forecasting Method is presented in this work. Neutral Network has used widely in many research to estimate earthquake and it is convinced that by using Backpropagation Method, earthquake can be predicted. At first, ANN is trained to predict Tsunami 26 December 2004 by using earthquake data before it. Then after we get trained ANN, we apply to predict the next earthquake. Not all earthquake will trigger Tsunami, there are some characteristics of earthquake that can cause Tsunami. Wrong decision can cause other problem in the society. Then, we need a method to reduce possibility of wrong decision. Fuzzy TOPSIS is a statistical method that is widely used to be decision seconder referring to given parameters. Fuzzy TOPSIS method can make the best decision whether it cause Tsunami or not. This work combines earthquake prediction using neural network method and using Fuzzy TOPSIS to determine the decision that the earthquake triggers Tsunami wave or not. Neural Network model is capable to capture non-linear relationship and Fuzzy TOPSIS is capable to determine the best decision better than other statistical method in tsunami prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earthquake" title="earthquake">earthquake</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20TOPSIS" title=" fuzzy TOPSIS"> fuzzy TOPSIS</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=tsunami" title=" tsunami"> tsunami</a> </p> <a href="https://publications.waset.org/abstracts/29246/earthquake-identification-to-predict-tsunami-in-andalas-island-indonesia-using-back-propagation-method-and-fuzzy-topsis-decision-seconder" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29246.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">496</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">1254</span> A New Model for Production Forecasting in ERP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20F.%20Wong">S. F. Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20I.%20Ho"> W. I. Ho</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Lin"> B. Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Q.%20Huang"> Q. Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ERP has been used in many enterprises for management, the accuracy of the production forecasting module is vital to the decision making of the enterprise, and the profit is affected directly. Therefore, enhancing the accuracy of the production forecasting module can also increase the efficiency and profitability. To deal with a lot of data, a suitable, reliable and accurate statistics model is necessary. LSSVM and Grey System are two main models to be studied in this paper, and a case study is used to demonstrate how the combination model is effective to the result of forecasting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ERP" title="ERP">ERP</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20system" title=" grey system"> grey system</a>, <a href="https://publications.waset.org/abstracts/search?q=LSSVM" title=" LSSVM"> LSSVM</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20forecasting" title=" production forecasting"> production forecasting</a> </p> <a href="https://publications.waset.org/abstracts/3348/a-new-model-for-production-forecasting-in-erp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3348.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">463</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">1253</span> Applications of Out-of-Sequence Thrust Movement for Earthquake Mitigation: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajkumar%20Ghosh">Rajkumar Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study presents an overview of the many uses and approaches for estimating out-of-sequence thrust movement in earthquake mitigation. The study investigates how knowing and forecasting thrust movement during seismic occurrences might assist to effective earthquake mitigation measures. The review begins by discussing out-of-sequence thrust movement and its importance in earthquake mitigation strategies. It explores how typical techniques of estimating thrust movement may not capture the full complexity of seismic occurrences and emphasizes the benefits of include out-of-sequence data in the analysis. A thorough review of existing research and studies on out-of-sequence thrust movement estimates for earthquake mitigation. The study demonstrates how to estimate out-of-sequence thrust movement using multiple data sources such as GPS measurements, satellite imagery, and seismic recordings. The study also examines the use of out-of-sequence thrust movement estimates in earthquake mitigation measures. It investigates how precise calculation of thrust movement may help improve structural design, analyse infrastructure risk, and develop early warning systems. The potential advantages of using out-of-sequence data in these applications to improve the efficiency of earthquake mitigation techniques. The difficulties and limits of estimating out-of-sequence thrust movement for earthquake mitigation. It addresses data quality difficulties, modelling uncertainties, and computational complications. To address these obstacles and increase the accuracy and reliability of out-of-sequence thrust movement estimates, the authors recommend topics for additional study and improvement. The study is a helpful resource for seismic monitoring and earthquake risk assessment researchers, engineers, and policymakers, supporting innovations in earthquake mitigation measures based on a better knowledge of thrust movement dynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earthquake%20mitigation" title="earthquake mitigation">earthquake mitigation</a>, <a href="https://publications.waset.org/abstracts/search?q=out-of-sequence%20thrust" title=" out-of-sequence thrust"> out-of-sequence thrust</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20imagery" title=" satellite imagery"> satellite imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20recordings" title=" seismic recordings"> seismic recordings</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS%20measurements" title=" GPS measurements"> GPS measurements</a> </p> <a href="https://publications.waset.org/abstracts/168985/applications-of-out-of-sequence-thrust-movement-for-earthquake-mitigation-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168985.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">85</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">1252</span> Electricity Demand Modeling and Forecasting in Singapore</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xian%20Li">Xian Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing-Guo%20Wang"> Qing-Guo Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiangshuai%20Huang"> Jiangshuai Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jidong%20Liu"> Jidong Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming%20Yu"> Ming Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tan%20Kok%20Poh"> Tan Kok Poh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20industry" title="power industry">power industry</a>, <a href="https://publications.waset.org/abstracts/search?q=electricity%20demand" title=" electricity demand"> electricity demand</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/13471/electricity-demand-modeling-and-forecasting-in-singapore" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13471.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">640</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">1251</span> Load Forecasting in Short-Term Including Meteorological Variables for Balearic Islands Paper</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carolina%20Senabre">Carolina Senabre</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergio%20Valero"> Sergio Valero</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20Lopez"> Miguel Lopez</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Gabaldon"> Antonio Gabaldon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comprehensive survey of the short-term load forecasting (STLF). Since the behavior of consumers and producers continue changing as new technologies, it is an ongoing process, and moreover, new policies become available. The results of a research study for the Spanish Transport System Operator (REE) is presented in this paper. It is presented the improvement of the forecasting accuracy in the Balearic Islands considering the introduction of meteorological variables, such as temperature to reduce forecasting error. Variables analyzed for the forecasting in terms of overall accuracy are cloudiness, solar radiation, and wind velocity. It has also been analyzed the type of days to be considered in the research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=short-term%20load%20forecasting" title="short-term load forecasting">short-term load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20demand" title=" power demand"> power demand</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=load%20forecasting" title=" load forecasting"> load forecasting</a> </p> <a href="https://publications.waset.org/abstracts/107890/load-forecasting-in-short-term-including-meteorological-variables-for-balearic-islands-paper" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107890.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">190</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">1250</span> Early Detection of Major Earthquakes Using Broadband Accelerometers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umberto%20Cerasani">Umberto Cerasani</a>, <a href="https://publications.waset.org/abstracts/search?q=Luca%20Cerasani"> Luca Cerasani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Methods for earthquakes forecasting have been intensively investigated in the last decades, but there is still no universal solution agreed by seismologists. Rock failure is most often preceded by a tiny elastic movement in the failure area and by the appearance of micro-cracks. These micro-cracks could be detected at the soil surface and represent useful earth-quakes precursors. The aim of this study was to verify whether tiny raw acceleration signals (in the 10⁻¹ to 10⁻⁴ cm/s² range) prior to the arrival of main primary-waves could be exploitable and related to earthquakes magnitude. Mathematical tools such as Fast Fourier Transform (FFT), moving average and wavelets have been applied on raw acceleration data available on the ITACA web site, and the study focused on one of the most unpredictable earth-quakes, i.e., the August 24th, 2016 at 01H36 one that occurred in the central Italy area. It appeared that these tiny acceleration signals preceding main P-waves have different patterns both on frequency and time domains for high magnitude earthquakes compared to lower ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earthquake" title="earthquake">earthquake</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerometer" title=" accelerometer"> accelerometer</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake%20forecasting" title=" earthquake forecasting"> earthquake forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=seism" title=" seism"> seism</a> </p> <a href="https://publications.waset.org/abstracts/109727/early-detection-of-major-earthquakes-using-broadband-accelerometers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109727.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1249</span> Dynamics of Understanding Earthquake Precursors-A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarada%20Nivedita%20Bhuyan">Sarada Nivedita Bhuyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earthquake is the sudden, rapid movement of the earth’s crust and is the natural means of releasing stress. Tectonic plates play a major role for earthquakes as tectonic plates are the crust of the planet. The boundary lines of tectonic plates are usually known as fault lines. To understand an earthquake before its occurrence, different types of earthquake precursors are studied by different researchers. Surface temperature, strange cloud cover, earth’s electric field, geomagnetic phenomena, ground water level, active faults, ionospheric anomalies, tectonic movements are taken as parameters for earthquake study by different researchers. In this paper we tried to gather complete and helpful information of earthquake precursors which have been studied until now. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earthquake%20precursors" title="earthquake precursors">earthquake precursors</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake" title=" earthquake"> earthquake</a>, <a href="https://publications.waset.org/abstracts/search?q=tectonic%20plates" title=" tectonic plates"> tectonic plates</a>, <a href="https://publications.waset.org/abstracts/search?q=fault" title=" fault"> fault</a> </p> <a href="https://publications.waset.org/abstracts/37407/dynamics-of-understanding-earthquake-precursors-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37407.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">380</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">1248</span> Fuzzy Time Series Forecasting Based on Fuzzy Logical Relationships, PSO Technique, and Automatic Clustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20M.%20Kamrul%20Islam">A. K. M. Kamrul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhamid%20Bouchachia"> Abdelhamid Bouchachia</a>, <a href="https://publications.waset.org/abstracts/search?q=Suang%20Cang"> Suang Cang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongnian%20Yu"> Hongnian Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20time%20series%20%28fts%29" title="fuzzy time series (fts)">fuzzy time series (fts)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithm" title=" clustering algorithm"> clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20forecasting%20model" title=" hybrid forecasting model"> hybrid forecasting model</a> </p> <a href="https://publications.waset.org/abstracts/51515/fuzzy-time-series-forecasting-based-on-fuzzy-logical-relationships-pso-technique-and-automatic-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51515.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">250</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">1247</span> Collaborative Planning and Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neha%20Asthana">Neha Asthana</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishal%20Krishna%20Prasad"> Vishal Krishna Prasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collaborative planning and forecasting are the innovative and systematic approaches towards productive integration and assimilation of data synergized into information. The changing and variable market dynamics have persuaded global business chains to incorporate collaborative planning and forecasting as an imperative tool. Thus, it is essential for the supply chains to constantly improvise, update its nature, and mould as per changing global environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20transfer" title="information transfer">information transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title=" supply chain management"> supply chain management</a> </p> <a href="https://publications.waset.org/abstracts/7060/collaborative-planning-and-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7060.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">436</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">1246</span> Lee-Carter Mortality Forecasting Method with Dynamic Normal Inverse Gaussian Mortality Index </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Funda%20Kul">Funda Kul</a>, <a href="https://publications.waset.org/abstracts/search?q=%C4%B0smail%20G%C3%BCr"> İsmail Gür</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pension scheme providers have to price mortality risk by accurate mortality forecasting method. There are many mortality-forecasting methods constructed and used in literature. The Lee-Carter model is the first model to consider stochastic improvement trends in life expectancy. It is still precisely used. Mortality forecasting is done by mortality index in the Lee-Carter model. It is assumed that mortality index fits ARIMA time series model. In this paper, we propose and use dynamic normal inverse gaussian distribution to modeling mortality indes in the Lee-Carter model. Using population mortality data for Italy, France, and Turkey, the model is forecasting capability is investigated, and a comparative analysis with other models is ensured by some well-known benchmarking criterions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mortality" title="mortality">mortality</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=lee-carter%20model" title=" lee-carter model"> lee-carter model</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20inverse%20gaussian%20distribution" title=" normal inverse gaussian distribution"> normal inverse gaussian distribution</a> </p> <a href="https://publications.waset.org/abstracts/39750/lee-carter-mortality-forecasting-method-with-dynamic-normal-inverse-gaussian-mortality-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39750.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">1245</span> Complex Network Analysis of Seismicity and Applications to Short-Term Earthquake Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kahlil%20Fredrick%20Cui">Kahlil Fredrick Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Marissa%20Pastor"> Marissa Pastor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earthquakes are complex phenomena, exhibiting complex correlations in space, time, and magnitude. Recently, the concept of complex networks has been used to shed light on the statistical and dynamical characteristics of regional seismicity. In this work, we study the relationships and interactions of seismic regions in Chile, Japan, and the Philippines through weighted and directed complex network analysis. Geographical areas are digitized into cells of fixed dimensions which in turn become the nodes of the network when an earthquake has occurred therein. Nodes are linked if a correlation exists between them as determined and measured by a correlation metric. The networks are found to be scale-free, exhibiting power-law behavior in the distributions of their different centrality measures: the in- and out-degree and the in- and out-strength. The evidence is also found of preferential interaction between seismically active regions through their degree-degree correlations suggesting that seismicity is dictated by the activity of a few active regions. The importance of a seismic region to the overall seismicity is measured using a generalized centrality metric taken to be an indicator of its activity or passivity. The spatial distribution of earthquake activity indicates the areas where strong earthquakes have occurred in the past while the passivity distribution points toward the likely locations an earthquake would occur whenever another one happens elsewhere. Finally, we propose a method that would project the location of the next possible earthquake using the generalized centralities coupled with correlations calculated between the latest earthquakes and a geographical point in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complex%20networks" title="complex networks">complex networks</a>, <a href="https://publications.waset.org/abstracts/search?q=correlations" title=" correlations"> correlations</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake" title=" earthquake"> earthquake</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard%20assessment" title=" hazard assessment"> hazard assessment</a> </p> <a href="https://publications.waset.org/abstracts/58357/complex-network-analysis-of-seismicity-and-applications-to-short-term-earthquake-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58357.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">212</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">1244</span> Forecasting Amman Stock Market Data Using a Hybrid Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Awajan">Ahmad Awajan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadam%20Al%20Wadi"> Sadam Al Wadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) is used to forecast Amman stock market data. First, the data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Then, all components are forecasted by HW technique. Finally, forecasting values are aggregated together to get the forecasting value of stock market data. Empirical results showed that the EMD- HW outperform individual forecasting models. The strength of this EMD-HW lies in its ability to forecast non-stationary and non- linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy comparing with eight existing forecasting methods based on the five forecast error measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Holt-Winter%20method" title="Holt-Winter method">Holt-Winter method</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/122857/forecasting-amman-stock-market-data-using-a-hybrid-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122857.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">129</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">1243</span> Comparison of Applicability of Time Series Forecasting Models VAR, ARCH and ARMA in Management Science: Study Based on Empirical Analysis of Time Series Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Tariq">Muhammad Tariq</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammad%20Tahir"> Hammad Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Fawwad%20Mahmood%20Butt"> Fawwad Mahmood Butt </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: This study attempts to examine the best forecasting methodologies in the time series. The time series forecasting models such as VAR, ARCH and the ARMA are considered for the analysis. Methodology: The Bench Marks or the parameters such as Adjusted R square, F-stats, Durban Watson, and Direction of the roots have been critically and empirically analyzed. The empirical analysis consists of time series data of Consumer Price Index and Closing Stock Price. Findings: The results show that the VAR model performed better in comparison to other models. Both the reliability and significance of VAR model is highly appreciable. In contrary to it, the ARCH model showed very poor results for forecasting. However, the results of ARMA model appeared double standards i.e. the AR roots showed that model is stationary and that of MA roots showed that the model is invertible. Therefore, the forecasting would remain doubtful if it made on the bases of ARMA model. It has been concluded that VAR model provides best forecasting results. Practical Implications: This paper provides empirical evidences for the application of time series forecasting model. This paper therefore provides the base for the application of best time series forecasting model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting" title="forecasting">forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=auto%20regression" title=" auto regression"> auto regression</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH" title=" ARCH"> ARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=ARMA" title=" ARMA"> ARMA</a> </p> <a href="https://publications.waset.org/abstracts/45124/comparison-of-applicability-of-time-series-forecasting-models-var-arch-and-arma-in-management-science-study-based-on-empirical-analysis-of-time-series-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45124.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">348</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">1242</span> A Brief Overview of Seven Churches in Van Province</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eylem%20G%C3%BCzel">Eylem Güzel</a>, <a href="https://publications.waset.org/abstracts/search?q=Soner%20Guler"> Soner Guler</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Gulen"> Mustafa Gulen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Van province which has a very rich historical heritage is located in eastern part of Turkey, between Lake Van and the Iranian border. Many civilizations prevailing in Van until today have built up many historical structures such as castles, mosques, churches, bridges, baths, etc. In 2011, a devastating earthquake with magnitude 7.2 Mw, epicenter in Tabanlı Village, occurred in Van, where a large part of the city locates in the first-degree earthquake zone. As a result of this earthquake, 644 people were killed; a lot of reinforced, unreinforced and historical structures were badly damaged. Many historical structures damaged due to this earthquake have been restored. In this study, the damages observed in Seven churches (Yedi Kilise) after 2011 Van earthquake is evaluated with regard to architecture and civil engineering perspective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earthquake" title="earthquake">earthquake</a>, <a href="https://publications.waset.org/abstracts/search?q=historical%20structures" title=" historical structures"> historical structures</a>, <a href="https://publications.waset.org/abstracts/search?q=Van%20province" title=" Van province"> Van province</a>, <a href="https://publications.waset.org/abstracts/search?q=church" title=" church"> church</a> </p> <a href="https://publications.waset.org/abstracts/21338/a-brief-overview-of-seven-churches-in-van-province" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21338.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">546</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">1241</span> pscmsForecasting: A Python Web Service for Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Andrianakis">Ioannis Andrianakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasileios%20Gkatas"> Vasileios Gkatas</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikos%20Eleftheriadis"> Nikos Eleftheriadis</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexios%20Ellinidis"> Alexios Ellinidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Ermioni%20Avramidou"> Ermioni Avramidou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> pscmsForecasting is an open-source web service that implements a variety of time series forecasting algorithms and exposes them to the user via the ubiquitous HTTP protocol. It allows developers to enhance their applications by adding time series forecasting functionalities through an intuitive and easy-to-use interface. This paper provides some background on time series forecasting and gives details about the implemented algorithms, aiming to enhance the end user’s understanding of the underlying methods before incorporating them into their applications. A detailed description of the web service’s interface and its various parameterizations is also provided. Being an open-source project, pcsmsForecasting can also be easily modified and tailored to the specific needs of each application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series" title="time series">time series</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service" title=" web service"> web service</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source" title=" open source"> open source</a> </p> <a href="https://publications.waset.org/abstracts/170621/pscmsforecasting-a-python-web-service-for-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170621.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">84</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">1240</span> Earthquake Forecasting Procedure Due to Diurnal Stress Transfer by the Core to the Crust</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Gholibeigian">Hassan Gholibeigian</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazem%20Gholibeigian"> Kazem Gholibeigian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, our goal is determination of loading versus time in crust. For this goal, we present a computational procedure to propose a cumulative strain energy time profile which can be used to predict the approximate location and time of the next major earthquake (M > 4.5) along a specific fault, which we believe, is more accurate than many of the methods presently in use. In the coming pages, after a short review of the research works presently going on in the area of earthquake analysis and prediction, earthquake mechanisms in both the jerk and sequence earthquake direction is discussed, then our computational procedure is presented using differential equations of equilibrium which govern the nonlinear dynamic response of a system of finite elements, modified with an extra term to account for the jerk produced during the quake. We then employ Von Mises developed model for the stress strain relationship in our calculations, modified with the addition of an extra term to account for thermal effects. For calculation of the strain energy the idea of Pulsating Mantle Hypothesis (PMH) is used. This hypothesis, in brief, states that the mantle is under diurnal cyclic pulsating loads due to unbalanced gravitational attraction of the sun and the moon. A brief discussion is done on the Denali fault as a case study. The cumulative strain energy is then graphically represented versus time. At the end, based on some hypothetic earthquake data, the final results are verified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pulsating%20mantle%20hypothesis" title="pulsating mantle hypothesis">pulsating mantle hypothesis</a>, <a href="https://publications.waset.org/abstracts/search?q=inner%20core%E2%80%99s%20dislocation" title=" inner core’s dislocation"> inner core’s dislocation</a>, <a href="https://publications.waset.org/abstracts/search?q=outer%20core%E2%80%99s%20bulge" title=" outer core’s bulge"> outer core’s bulge</a>, <a href="https://publications.waset.org/abstracts/search?q=constitutive%20model" title=" constitutive model"> constitutive model</a>, <a href="https://publications.waset.org/abstracts/search?q=transient%20hydro-magneto-thermo-mechanical%20load" title=" transient hydro-magneto-thermo-mechanical load"> transient hydro-magneto-thermo-mechanical load</a>, <a href="https://publications.waset.org/abstracts/search?q=diurnal%20stress" title=" diurnal stress"> diurnal stress</a>, <a href="https://publications.waset.org/abstracts/search?q=jerk" title=" jerk"> jerk</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20behaviour" title=" fault behaviour"> fault behaviour</a> </p> <a href="https://publications.waset.org/abstracts/3206/earthquake-forecasting-procedure-due-to-diurnal-stress-transfer-by-the-core-to-the-crust" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3206.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">276</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">1239</span> Forecasting Stock Prices Based on the Residual Income Valuation Model: Evidence from a Time-Series Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chen-Yin%20Kuo">Chen-Yin Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yung-Hsin%20Lee"> Yung-Hsin Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=residual%20income%20valuation%20model" title="residual income valuation model">residual income valuation model</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20error%20correction%20model" title=" vector error correction model"> vector error correction model</a>, <a href="https://publications.waset.org/abstracts/search?q=out%20of%20sample%20forecasting" title=" out of sample forecasting"> out of sample forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20accuracy" title=" forecasting accuracy"> forecasting accuracy</a> </p> <a href="https://publications.waset.org/abstracts/1668/forecasting-stock-prices-based-on-the-residual-income-valuation-model-evidence-from-a-time-series-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1668.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">316</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">1238</span> Attribute Index and Classification Method of Earthquake Damage Photographs of Engineering Structure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ming%20Lu">Ming Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaojun%20Li"> Xiaojun Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bodi%20Lu"> Bodi Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Juehui%20Xing"> Juehui Xing</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earthquake damage phenomenon of each large earthquake gives comprehensive and profound real test to the dynamic performance and failure mechanism of different engineering structures. Cognitive engineering structure characteristics through seismic damage phenomenon are often far superior to expensive shaking table experiments. After the earthquake, people will record a variety of different types of engineering damage photos. However, a large number of earthquake damage photographs lack sufficient information and reduce their using value. To improve the research value and the use efficiency of engineering seismic damage photographs, this paper objects to explore and show seismic damage background information, which includes the earthquake magnitude, earthquake intensity, and the damaged structure characteristics. From the research requirement in earthquake engineering field, the authors use the 2008 China Wenchuan M8.0 earthquake photographs, and provide four kinds of attribute indexes and classification, which are seismic information, structure types, earthquake damage parts and disaster causation factors. The final object is to set up an engineering structural seismic damage database based on these four attribute indicators and classification, and eventually build a website providing seismic damage photographs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attribute%20index" title="attribute index">attribute index</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20method" title=" classification method"> classification method</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake%20damage%20picture" title=" earthquake damage picture"> earthquake damage picture</a>, <a href="https://publications.waset.org/abstracts/search?q=engineering%20structure" title=" engineering structure"> engineering structure</a> </p> <a href="https://publications.waset.org/abstracts/66126/attribute-index-and-classification-method-of-earthquake-damage-photographs-of-engineering-structure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66126.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">765</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">1237</span> Two Day Ahead Short Term Load Forecasting Neural Network Based</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Firas%20M.%20Tuaimah">Firas M. Tuaimah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=short-term%20load%20forecasting" title="short-term load forecasting">short-term load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation%20learning" title=" back propagation learning"> back propagation learning</a>, <a href="https://publications.waset.org/abstracts/search?q=hourly%20load%20demand" title=" hourly load demand"> hourly load demand</a> </p> <a href="https://publications.waset.org/abstracts/7878/two-day-ahead-short-term-load-forecasting-neural-network-based" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7878.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">464</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">1236</span> Forecasting Future Demand for Energy Efficient Vehicles: A Review of Methodological Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20I.%20Tselentis">Dimitrios I. Tselentis</a>, <a href="https://publications.waset.org/abstracts/search?q=Simon%20P.%20Washington"> Simon P. Washington</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Considerable literature has been focused over the last few decades on forecasting the consumer demand of Energy Efficient Vehicles (EEVs). These methodological issues range from how to capture recent purchase decisions in revealed choice studies and how to set up experiments in stated preference (SP) studies, and choice of analysis method for analyzing such data. This paper reviews the plethora of published studies on the field of forecasting demand of EEVs since 1980, and provides a review and annotated bibliography of that literature as it pertains to this particular demand forecasting problem. This detailed review addresses the literature not only to Transportation studies, but specifically to the problem and methodologies around forecasting to the time horizons of planning studies which may represent 10 to 20 year forecasts. The objectives of the paper are to identify where existing gaps in literature exist and to articulate where promising methodologies might guide longer term forecasting. One of the key findings of this review is that there are many common techniques used both in the field of new product demand forecasting and the field of predicting future demand for EEV. Apart from SP and RP methods, some of these new techniques that have emerged in the literature in the last few decades are survey related approaches, product diffusion models, time-series modelling, computational intelligence models and other holistic approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=demand%20forecasting" title="demand forecasting">demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=Energy%20Efficient%20Vehicles%20%28EEVs%29" title=" Energy Efficient Vehicles (EEVs)"> Energy Efficient Vehicles (EEVs)</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20methodologies%20review" title=" forecasting methodologies review"> forecasting methodologies review</a>, <a href="https://publications.waset.org/abstracts/search?q=methodological%20approaches" title=" methodological approaches"> methodological approaches</a> </p> <a href="https://publications.waset.org/abstracts/15014/forecasting-future-demand-for-energy-efficient-vehicles-a-review-of-methodological-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15014.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">489</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">1235</span> Forecasting of Grape Juice Flavor by Using Support Vector Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ren-Jieh%20Kuo">Ren-Jieh Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun-Shou%20Huang"> Chun-Shou Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flavor%20forecasting" title="flavor forecasting">flavor forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Regression" title=" Support Vector Regression"> Support Vector Regression</a>, <a href="https://publications.waset.org/abstracts/search?q=China" title=" China"> China</a> </p> <a href="https://publications.waset.org/abstracts/21311/forecasting-of-grape-juice-flavor-by-using-support-vector-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21311.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">493</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">1234</span> Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Anowarul%20Haque">S. M. Anowarul Haque</a>, <a href="https://publications.waset.org/abstracts/search?q=Md.%20Asiful%20Islam"> Md. Asiful Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=load%20forecasting" title="load forecasting">load forecasting</a>, <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=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/133841/artificial-neural-network-based-short-term-load-forecasting-for-mymensingh-area-of-bangladesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133841.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">171</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">1233</span> Experiential Learning in an Earthquake Engineering Course Using Online Tools and Shake Table Exercises</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andres%20Winston%20Oreta">Andres Winston Oreta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Experiential Learning (ELE) is a strategy for enhancing the teaching and learning of courses especially in civil engineering. This paper presents the adaption of the ELE framework in the delivery of various course requirements in an earthquake engineering course. Examples of how ELE is integrated using online tools and hands-on laboratory technology to address the course learning outcomes on earthquake engineering are presented. Student feedback shows that ELE using online tools and technology strengthens students’ understanding and intuition of seismic design and earthquake engineering concepts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earthquake%20engineering" title="earthquake engineering">earthquake engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=experiential%20learning" title=" experiential learning"> experiential learning</a>, <a href="https://publications.waset.org/abstracts/search?q=shake%20table" title=" shake table"> shake table</a>, <a href="https://publications.waset.org/abstracts/search?q=online" title=" online"> online</a>, <a href="https://publications.waset.org/abstracts/search?q=internet" title=" internet"> internet</a>, <a href="https://publications.waset.org/abstracts/search?q=civil%20engineering" title=" civil engineering"> civil engineering</a> </p> <a href="https://publications.waset.org/abstracts/189318/experiential-learning-in-an-earthquake-engineering-course-using-online-tools-and-shake-table-exercises" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189318.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">1232</span> A Case Study of Typhoon Tracks: Insights from the Interaction between Typhoon Hinnamnor and Ocean Currents in 2022</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei-Kuo%20Soong">Wei-Kuo Soong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The forecasting of typhoon tracks remains a formidable challenge, primarily attributable to the paucity of observational data in the open sea and the intricate influence of weather systems at varying scales. This study investigates the case of Typhoon Hinnamnor in 2022, examining its trajectory and intensity fluctuations in relation to the interaction with a concurrent tropical cyclone and sea surface temperatures (SST). Utilizing the Weather Research and Forecasting Model (WRF), to simulate and analyze the interaction between Typhoon Hinnamnor and its environmental factors, shedding light on the mechanisms driving typhoon development and enhancing forecasting capabilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=typhoon" title="typhoon">typhoon</a>, <a href="https://publications.waset.org/abstracts/search?q=sea%20surface%20temperature" title=" sea surface temperature"> sea surface temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=WRF" title=" WRF"> WRF</a> </p> <a href="https://publications.waset.org/abstracts/176526/a-case-study-of-typhoon-tracks-insights-from-the-interaction-between-typhoon-hinnamnor-and-ocean-currents-in-2022" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176526.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">52</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">1231</span> Applying Arima Data Mining Techniques to ERP to Generate Sales Demand Forecasting: A Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghaleb%20Y.%20Abbasi">Ghaleb Y. Abbasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Israa%20Abu%20Rumman"> Israa Abu Rumman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper modeled sales history archived from 2012 to 2015 bulked in monthly bins for five products for a medical supply company in Jordan. The sales forecasts and extracted consistent patterns in the sales demand history from the Enterprise Resource Planning (ERP) system were used to predict future forecasting and generate sales demand forecasting using time series analysis statistical technique called Auto Regressive Integrated Moving Average (ARIMA). This was used to model and estimate realistic sales demand patterns and predict future forecasting to decide the best models for five products. Analysis revealed that the current replenishment system indicated inventory overstocking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA%20models" title="ARIMA models">ARIMA models</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20demand%20forecasting" title=" sales demand forecasting"> sales demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=R%20code" title=" R code"> R code</a> </p> <a href="https://publications.waset.org/abstracts/64117/applying-arima-data-mining-techniques-to-erp-to-generate-sales-demand-forecasting-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64117.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">385</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">1230</span> Electric Load Forecasting Based on Artificial Neural Network for Iraqi Power System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afaneen%20Anwer">Afaneen Anwer</a>, <a href="https://publications.waset.org/abstracts/search?q=Samara%20M.%20Kamil"> Samara M. Kamil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Load Forecast required prediction accuracy based on optimal operation and maintenance. A good accuracy is the basis of economic dispatch, unit commitment, and system reliability. A good load forecasting system fulfilled fast speed, automatic bad data detection, and ability to access the system automatically to get the needed data. In this paper, the formulation of the load forecasting is discussed and the solution is obtained by using artificial neural network method. A MATLAB environment has been used to solve the load forecasting schedule of Iraqi super grid network considering the daily load for three years. The obtained results showed a good accuracy in predicting the forecasted load. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=load%20forecasting" title="load forecasting">load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=back-propagation%20algorithm" title=" back-propagation algorithm"> back-propagation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Iraqi%20power%20system" title=" Iraqi power system"> Iraqi power system</a> </p> <a href="https://publications.waset.org/abstracts/19308/electric-load-forecasting-based-on-artificial-neural-network-for-iraqi-power-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19308.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">583</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">1229</span> The Simultaneous Effect of Horizontal and Vertical Earthquake Components on the Seismic Response of Buckling-Restrained Braced Frame</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Shokrollahi">Mahdi Shokrollahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past years, much research has been conducted on the vulnerability of structures to earthquakes, which only horizontal components of the earthquake were considered in their seismic analysis and vertical earthquake acceleration especially in near-fault area was less considered. The investigation of the mappings shows that vertical earthquake acceleration can be significantly closer to the maximum horizontal earthquake acceleration, and even exceeds it in some cases. This study has compared the behavior of different members of three steel moment frame with a buckling-restrained brace (BRB), one time only by considering the horizontal component and again by considering simultaneously the horizontal and vertical components under the three mappings of the near-fault area and the effect of vertical acceleration on structural responses is investigated. Finally, according to the results, the vertical component of the earthquake has a greater effect on the axial force of the columns and the vertical displacement of the middle of the beams of the different classes and less on the lateral displacement of the classes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vertical%20earthquake%20acceleration" title="vertical earthquake acceleration">vertical earthquake acceleration</a>, <a href="https://publications.waset.org/abstracts/search?q=near-fault%20area" title=" near-fault area"> near-fault area</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20frame" title=" steel frame"> steel frame</a>, <a href="https://publications.waset.org/abstracts/search?q=horizontal%20and%20vertical%20component%20of%20earthquake" title=" horizontal and vertical component of earthquake"> horizontal and vertical component of earthquake</a>, <a href="https://publications.waset.org/abstracts/search?q=buckling-restrained%20brace" title=" buckling-restrained brace"> buckling-restrained brace</a> </p> <a href="https://publications.waset.org/abstracts/91326/the-simultaneous-effect-of-horizontal-and-vertical-earthquake-components-on-the-seismic-response-of-buckling-restrained-braced-frame" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91326.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">179</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">1228</span> Improving Forecasting Demand for Maintenance Spare Parts: Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulaziz%20Afandi">Abdulaziz Afandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20demand" title=" forecasting demand"> forecasting demand</a>, <a href="https://publications.waset.org/abstracts/search?q=inventory%20management" title=" inventory management"> inventory management</a> </p> <a href="https://publications.waset.org/abstracts/148574/improving-forecasting-demand-for-maintenance-spare-parts-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148574.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">127</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">1227</span> Electricity Price Forecasting: A Comparative Analysis with Shallow-ANN and DNN</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faz%C4%B1l%20G%C3%B6kg%C3%B6z">Fazıl Gökgöz</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahrettin%20Filiz"> Fahrettin Filiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409. <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=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20price%20forecasting" title=" energy price forecasting"> energy price forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=turkey" title=" turkey"> turkey</a> </p> <a href="https://publications.waset.org/abstracts/91064/electricity-price-forecasting-a-comparative-analysis-with-shallow-ann-and-dnn" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91064.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">292</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=earthquake%20forecasting&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=earthquake%20forecasting&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=earthquake%20forecasting&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=earthquake%20forecasting&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=earthquake%20forecasting&page=6">6</a></li> <li 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