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value distribution</title> <meta name="description" content="Search results for: extreme value distribution"> <meta name="keywords" content="extreme value distribution"> <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 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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="extreme value distribution"> <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> 5873</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: extreme value distribution</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5873</span> An Extension of the Generalized Extreme Value Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20Provost">Serge Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdous%20Saboor"> Abdous Saboor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A q-analogue of the generalized extreme value distribution which includes the Gumbel distribution is introduced. The additional parameter q allows for increased modeling flexibility. The resulting distribution can have a finite, semi-infinite or infinite support. It can also produce several types of hazard rate functions. The model parameters are determined by making use of the method of maximum likelihood. It will be shown that it compares favourably to three related distributions in connection with the modeling of a certain hydrological data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title="extreme value theory">extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20value%20distribution" title=" generalized extreme value distribution"> generalized extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit%20statistics" title=" goodness-of-fit statistics"> goodness-of-fit statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=Gumbel%20distribution" title=" Gumbel distribution"> Gumbel distribution</a> </p> <a href="https://publications.waset.org/abstracts/72656/an-extension-of-the-generalized-extreme-value-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72656.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">349</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">5872</span> Modeling of Maximum Rainfall Using Poisson-Generalized Pareto Distribution in Kigali, Rwanda</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Iyamuremye">Emmanuel Iyamuremye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extreme rainfall events have caused significant damage to agriculture, ecology, and infrastructure, disruption of human activities, injury, and loss of life. They also have significant social, economic, and environmental consequences because they considerably damage urban as well as rural areas. Early detection of extreme maximum rainfall helps to implement strategies and measures, before they occur, hence mitigating the consequences. Extreme value theory has been used widely in modeling extreme rainfall and in various disciplines, such as financial markets, the insurance industry, failure cases. Climatic extremes have been analyzed by using either generalized extreme value (GEV) or generalized Pareto (GP) distributions, which provides evidence of the importance of modeling extreme rainfall from different regions of the world. In this paper, we focused on Peak Over Thresholds approach, where the Poisson-generalized Pareto distribution is considered as the proper distribution for the study of the exceedances. This research also considers the use of the generalized Pareto (GP) distribution with a Poisson model for arrivals to describe peaks over a threshold. The research used statistical techniques to fit models that used to predict extreme rainfall in Kigali. The results indicate that the proposed Poisson-GP distribution provides a better fit to maximum monthly rainfall data. Further, the Poisson-GP models are able to estimate various return levels. The research also found a slow increase in return levels for maximum monthly rainfall for higher return periods, and further, the intervals are increasingly wider as the return period is increasing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exceedances" title="exceedances">exceedances</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20Pareto%20distribution" title=" generalized Pareto distribution"> generalized Pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20generalized%20Pareto%20distribution" title=" Poisson generalized Pareto distribution"> Poisson generalized Pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/127379/modeling-of-maximum-rainfall-using-poisson-generalized-pareto-distribution-in-kigali-rwanda" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127379.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">5871</span> Evaluation of Best-Fit Probability Distribution for Prediction of Extreme Hydrologic Phenomena</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Hamidi%20Machekposhti">Karim Hamidi Machekposhti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Sedghi"> Hossein Sedghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The probability distributions are the best method for forecasting of extreme hydrologic phenomena such as rainfall and flood flows. In this research, in order to determine suitable probability distribution for estimating of annual extreme rainfall and flood flows (discharge) series with different return periods, precipitation with 40 and discharge with 58 years time period had been collected from Karkheh River at Iran. After homogeneity and adequacy tests, data have been analyzed by Stormwater Management and Design Aid (SMADA) software and residual sum of squares (R.S.S). The best probability distribution was Log Pearson Type III with R.S.S value (145.91) and value (13.67) for peak discharge and Log Pearson Type III with R.S.S values (141.08) and (8.95) for maximum discharge in Jelogir Majin and Pole Zal stations, respectively. The best distribution for maximum precipitation in Jelogir Majin and Pole Zal stations was Log Pearson Type III distribution with R.S.S values (1.74&1.90) and then Pearson Type III distribution with R.S.S values (1.53&1.69). Overall, the Log Pearson Type III distributions are acceptable distribution types for representing statistics of extreme hydrologic phenomena in Karkheh River at Iran with the Pearson Type III distribution as a potential alternative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20River" title="Karkheh River">Karkheh River</a>, <a href="https://publications.waset.org/abstracts/search?q=Log%20Pearson%20Type%20III" title=" Log Pearson Type III"> Log Pearson Type III</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20distribution" title=" probability distribution"> probability distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20sum%20of%20squares" title=" residual sum of squares"> residual sum of squares</a> </p> <a href="https://publications.waset.org/abstracts/97779/evaluation-of-best-fit-probability-distribution-for-prediction-of-extreme-hydrologic-phenomena" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97779.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">197</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">5870</span> A Bayesian Model with Improved Prior in Extreme Value Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eva%20L.%20Sanju%C3%A1n">Eva L. Sanjuán</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacinto%20Mart%C3%ADn"> Jacinto Martín</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Isabel%20Parra"> M. Isabel Parra</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20M.%20Pizarro"> Mario M. Pizarro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Extreme Value Theory, inference estimation for the parameters of the distribution is made employing a small part of the observation values. When block maxima values are taken, many data are discarded. We developed a new Bayesian inference model to seize all the information provided by the data, introducing informative priors and using the relations between baseline and limit parameters. Firstly, we studied the accuracy of the new model for three baseline distributions that lead to a Gumbel extreme distribution: Exponential, Normal and Gumbel. Secondly, we considered mixtures of Normal variables, to simulate practical situations when data do not adjust to pure distributions, because of perturbations (noise). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20inference" title="bayesian inference">bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=Gumbel%20distribution" title=" Gumbel distribution"> Gumbel distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=highly%20informative%20prior" title=" highly informative prior"> highly informative prior</a> </p> <a href="https://publications.waset.org/abstracts/141776/a-bayesian-model-with-improved-prior-in-extreme-value-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141776.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">198</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">5869</span> Estimating The Population Mean by Using Stratified Double Extreme Ranked Set Sample</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20I.%20Syam">Mahmoud I. Syam</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamarulzaman%20Ibrahim"> Kamarulzaman Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20I.%20Al-Omari"> Amer I. Al-Omari </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stratified double extreme ranked set sampling (SDERSS) method is introduced and considered for estimating the population mean. The SDERSS is compared with the simple random sampling (SRS), stratified ranked set sampling (SRSS) and stratified simple set sampling (SSRS). It is shown that the SDERSS estimator is an unbiased of the population mean and more efficient than the estimators using SRS, SRSS and SSRS when the underlying distribution of the variable of interest is symmetric or asymmetric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=double%20extreme%20ranked%20set%20sampling" title="double extreme ranked set sampling">double extreme ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20ranked%20set%20sampling" title=" extreme ranked set sampling"> extreme ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=ranked%20set%20sampling" title=" ranked set sampling"> ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=stratified%20double%20extreme%20ranked%20set%20sampling" title=" stratified double extreme ranked set sampling"> stratified double extreme ranked set sampling</a> </p> <a href="https://publications.waset.org/abstracts/25207/estimating-the-population-mean-by-using-stratified-double-extreme-ranked-set-sample" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25207.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">456</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">5868</span> Extreme Temperature Forecast in Mbonge, Cameroon Through Return Level Analysis of the Generalized Extreme Value (GEV) Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nkongho%20Ayuketang%20Arreyndip">Nkongho Ayuketang Arreyndip</a>, <a href="https://publications.waset.org/abstracts/search?q=Ebobenow%20Joseph"> Ebobenow Joseph</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, temperature extremes are forecast by employing the block maxima method of the generalized extreme value (GEV) distribution to analyse temperature data from the Cameroon Development Corporation (CDC). By considering two sets of data (raw data and simulated data) and two (stationary and non-stationary) models of the GEV distribution, return levels analysis is carried out and it was found that in the stationary model, the return values are constant over time with the raw data, while in the simulated data the return values show an increasing trend with an upper bound. In the non-stationary model, the return levels of both the raw data and simulated data show an increasing trend with an upper bound. This clearly shows that although temperatures in the tropics show a sign of increase in the future, there is a maximum temperature at which there is no exceedance. The results of this paper are very vital in agricultural and environmental research. <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=generalized%20extreme%20value%20%28GEV%29" title=" generalized extreme value (GEV)"> generalized extreme value (GEV)</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorology" title=" meteorology"> meteorology</a>, <a href="https://publications.waset.org/abstracts/search?q=return%20level" title=" return level"> return level</a> </p> <a href="https://publications.waset.org/abstracts/34244/extreme-temperature-forecast-in-mbonge-cameroon-through-return-level-analysis-of-the-generalized-extreme-value-gev-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34244.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">478</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">5867</span> Parameter Estimation of Gumbel Distribution with Maximum-Likelihood Based on Broyden Fletcher Goldfarb Shanno Quasi-Newton</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dewi%20Retno%20Sari%20Saputro">Dewi Retno Sari Saputro</a>, <a href="https://publications.waset.org/abstracts/search?q=Purnami%20Widyaningsih"> Purnami Widyaningsih</a>, <a href="https://publications.waset.org/abstracts/search?q=Hendrika%20Handayani"> Hendrika Handayani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extreme data on an observation can occur due to unusual circumstances in the observation. The data can provide important information that can’t be provided by other data so that its existence needs to be further investigated. The method for obtaining extreme data is one of them using maxima block method. The distribution of extreme data sets taken with the maxima block method is called the distribution of extreme values. Distribution of extreme values is Gumbel distribution with two parameters. The parameter estimation of Gumbel distribution with maximum likelihood method (ML) is difficult to determine its exact value so that it is necessary to solve the approach. The purpose of this study was to determine the parameter estimation of Gumbel distribution with quasi-Newton BFGS method. The quasi-Newton BFGS method is a numerical method used for nonlinear function optimization without constraint so that the method can be used for parameter estimation from Gumbel distribution whose distribution function is in the form of exponential doubel function. The quasi-New BFGS method is a development of the Newton method. The Newton method uses the second derivative to calculate the parameter value changes on each iteration. Newton's method is then modified with the addition of a step length to provide a guarantee of convergence when the second derivative requires complex calculations. In the quasi-Newton BFGS method, Newton's method is modified by updating both derivatives on each iteration. The parameter estimation of the Gumbel distribution by a numerical approach using the quasi-Newton BFGS method is done by calculating the parameter values that make the distribution function maximum. In this method, we need gradient vector and hessian matrix. This research is a theory research and application by studying several journals and textbooks. The results of this study obtained the quasi-Newton BFGS algorithm and estimation of Gumbel distribution parameters. The estimation method is then applied to daily rainfall data in Purworejo District to estimate the distribution parameters. This indicates that the high rainfall that occurred in Purworejo District decreased its intensity and the range of rainfall that occurred decreased. <p class="card-text"><strong>Keywords:</strong> <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=Gumbel%20distribution" title=" Gumbel distribution"> Gumbel distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=broyden%20fletcher%20goldfarb%20shanno%20%28BFGS%29quasi%20newton" title=" broyden fletcher goldfarb shanno (BFGS)quasi newton "> broyden fletcher goldfarb shanno (BFGS)quasi newton </a> </p> <a href="https://publications.waset.org/abstracts/73714/parameter-estimation-of-gumbel-distribution-with-maximum-likelihood-based-on-broyden-fletcher-goldfarb-shanno-quasi-newton" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73714.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">324</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">5866</span> Generalized Extreme Value Regression with Binary Dependent Variable: An Application for Predicting Meteorological Drought Probabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retius%20Chifurira">Retius Chifurira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20value%20distribution" title="generalized extreme value distribution">generalized extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20linear%20model" title=" general linear model"> general linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20annual%20rainfall" title=" mean annual rainfall"> mean annual rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20drought%20probabilities" title=" meteorological drought probabilities"> meteorological drought probabilities</a> </p> <a href="https://publications.waset.org/abstracts/99321/generalized-extreme-value-regression-with-binary-dependent-variable-an-application-for-predicting-meteorological-drought-probabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99321.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">200</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">5865</span> A Comparative Study of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Extreme Value Theory (EVT) Model in Modeling Value-at-Risk (VaR)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Longqing%20Li">Longqing Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper addresses the inefficiency of the classical model in measuring the Value-at-Risk (VaR) using a normal distribution or a Student’s t distribution. Specifically, the paper focuses on the one day ahead Value-at-Risk (VaR) of major stock market’s daily returns in US, UK, China and Hong Kong in the most recent ten years under 95% confidence level. To improve the predictable power and search for the best performing model, the paper proposes using two leading alternatives, Extreme Value Theory (EVT) and a family of GARCH models, and compares the relative performance. The main contribution could be summarized in two aspects. First, the paper extends the GARCH family model by incorporating EGARCH and TGARCH to shed light on the difference between each in estimating one day ahead Value-at-Risk (VaR). Second, to account for the non-normality in the distribution of financial markets, the paper applies Generalized Error Distribution (GED), instead of the normal distribution, to govern the innovation term. A dynamic back-testing procedure is employed to assess the performance of each model, a family of GARCH and the conditional EVT. The conclusion is that Exponential GARCH yields the best estimate in out-of-sample one day ahead Value-at-Risk (VaR) forecasting. Moreover, the discrepancy of performance between the GARCH and the conditional EVT is indistinguishable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Value-at-Risk" title="Value-at-Risk">Value-at-Risk</a>, <a href="https://publications.waset.org/abstracts/search?q=Extreme%20Value%20Theory" title=" Extreme Value Theory"> Extreme Value Theory</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20EVT" title=" conditional EVT"> conditional EVT</a>, <a href="https://publications.waset.org/abstracts/search?q=backtesting" title=" backtesting"> backtesting</a> </p> <a href="https://publications.waset.org/abstracts/49589/a-comparative-study-of-generalized-autoregressive-conditional-heteroskedasticity-garch-and-extreme-value-theory-evt-model-in-modeling-value-at-risk-var" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49589.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">321</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">5864</span> Modelling Operational Risk Using Extreme Value Theory and Skew t-Copulas via Bayesian Inference </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Betty%20Johanna%20Garzon%20Rozo">Betty Johanna Garzon Rozo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Crook"> Jonathan Crook</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Moreira"> Fernando Moreira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Operational risk losses are heavy tailed and are likely to be asymmetric and extremely dependent among business lines/event types. We propose a new methodology to assess, in a multivariate way, the asymmetry and extreme dependence between severity distributions, and to calculate the capital for Operational Risk. This methodology simultaneously uses (i) several parametric distributions and an alternative mix distribution (the Lognormal for the body of losses and the Generalized Pareto Distribution for the tail) via extreme value theory using SAS®, (ii) the multivariate skew t-copula applied for the first time for operational losses and (iii) Bayesian theory to estimate new n-dimensional skew t-copula models via Markov chain Monte Carlo (MCMC) simulation. This paper analyses a newly operational loss data set, SAS Global Operational Risk Data [SAS OpRisk], to model operational risk at international financial institutions. All the severity models are constructed in SAS® 9.2. We implement the procedure PROC SEVERITY and PROC NLMIXED. This paper focuses in describing this implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=operational%20risk" title="operational risk">operational risk</a>, <a href="https://publications.waset.org/abstracts/search?q=loss%20distribution%20approach" title=" loss distribution approach"> loss distribution approach</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=copulas" title=" copulas"> copulas</a> </p> <a href="https://publications.waset.org/abstracts/19385/modelling-operational-risk-using-extreme-value-theory-and-skew-t-copulas-via-bayesian-inference" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19385.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">603</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">5863</span> Extreme Rainfall Frequency Analysis For Meteorological Sub-Division 4 Of India Using L-Moments. </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arti%20Devi">Arti Devi</a>, <a href="https://publications.waset.org/abstracts/search?q=Parthasarthi%20Choudhury"> Parthasarthi Choudhury</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extreme rainfall frequency analysis for Meteorological Sub-Division 4 of India was analysed using L-moments approach. Serial Correlation and Mann Kendall tests were conducted for checking serially independent and stationarity of the observations. The discordancy measure for the sites was conducted to detect the discordant sites. The regional homogeneity was tested by comparing with 500 generated homogeneous regions using a 4 parameter Kappa distribution. The best fit distribution was selected based on ZDIST statistics and L-moments ratio diagram from the five extreme value distributions GPD, GLO, GEV, P3 and LP3. The LN3 distribution was selected and regional rainfall frequency relationship was established using index-rainfall procedure. A regional mean rainfall relationship was developed using multiple linear regression with latitude and longitude of the sites as variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=L-moments" title="L-moments">L-moments</a>, <a href="https://publications.waset.org/abstracts/search?q=ZDIST%20statistics" title=" ZDIST statistics"> ZDIST statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=serial%20correlation" title=" serial correlation"> serial correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=Mann%20Kendall%20test" title=" Mann Kendall test"> Mann Kendall test</a> </p> <a href="https://publications.waset.org/abstracts/3033/extreme-rainfall-frequency-analysis-for-meteorological-sub-division-4-of-india-using-l-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3033.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">441</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">5862</span> Climate Change and Extreme Weather: Understanding Interconnections and Implications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Johnstone%20Walubengo%20Wangusi">Johnstone Walubengo Wangusi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate change is undeniably altering the frequency, intensity, and geographic distribution of extreme weather events worldwide. In this paper, we explore the complex interconnections between climate change and extreme weather phenomena, drawing upon research from atmospheric science, geology, and climatology. We examine the underlying mechanisms driving these changes, the impacts on natural ecosystems and human societies, and strategies for adaptation and mitigation. By synthesizing insights from interdisciplinary research, this paper aims to provide a comprehensive understanding of the multifaceted relationship between climate change and extreme weather, informing efforts to address the challenges posed by a changing climate. <p class="card-text"><strong>Keywords:</strong> <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=extreme%20weather" title=" extreme weather"> extreme weather</a>, <a href="https://publications.waset.org/abstracts/search?q=atmospheric%20science" title=" atmospheric science"> atmospheric science</a>, <a href="https://publications.waset.org/abstracts/search?q=geology" title=" geology"> geology</a>, <a href="https://publications.waset.org/abstracts/search?q=climatology" title=" climatology"> climatology</a>, <a href="https://publications.waset.org/abstracts/search?q=impacts" title=" impacts"> impacts</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptation" title=" adaptation"> adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=mitigation" title=" mitigation"> mitigation</a> </p> <a href="https://publications.waset.org/abstracts/184530/climate-change-and-extreme-weather-understanding-interconnections-and-implications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184530.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">64</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">5861</span> Extreme Value Modelling of Ghana Stock Exchange Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kwabena%20Asare">Kwabena Asare</a>, <a href="https://publications.waset.org/abstracts/search?q=Ezekiel%20N.%20N.%20Nortey"> Ezekiel N. N. Nortey</a>, <a href="https://publications.waset.org/abstracts/search?q=Felix%20O.%20Mettle"> Felix O. Mettle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana Stock Exchange All-Shares indices (2000-2010) by applying the Extreme Value Theory to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic terms present in the returns series, before EVT method was applied. The Peak Over Threshold (POT) approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model’s goodness of fit was assessed graphically using Q-Q, P-P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the Value at Risk (VaR) and Expected Shortfall (ES) risk measures at some high quantiles, based on the fitted GPD model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title="extreme value theory">extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=expected%20shortfall" title=" expected shortfall"> expected shortfall</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20pareto%20distribution" title=" generalized pareto distribution"> generalized pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20over%20threshold" title=" peak over threshold"> peak over threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20at%20risk" title=" value at risk"> value at risk</a> </p> <a href="https://publications.waset.org/abstracts/35743/extreme-value-modelling-of-ghana-stock-exchange-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35743.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">557</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">5860</span> Statistical Modelling of Maximum Temperature in Rwanda Using Extreme Value Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Iyamuremye">Emmanuel Iyamuremye</a>, <a href="https://publications.waset.org/abstracts/search?q=Edouard%20Singirankabo"> Edouard Singirankabo</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexis%20Habineza"> Alexis Habineza</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunvirusaba%20Nelson"> Yunvirusaba Nelson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Temperature is one of the most important climatic factors for crop production. However, severe temperatures cause drought, feverish and cold spells that have various consequences for human life, agriculture, and the environment in general. It is necessary to provide reliable information related to the incidents and the probability of such extreme events occurring. In the 21st century, the world faces a huge number of threats, especially from climate change, due to global warming and environmental degradation. The rise in temperature has a direct effect on the decrease in rainfall. This has an impact on crop growth and development, which in turn decreases crop yield and quality. Countries that are heavily dependent on agriculture use to suffer a lot and need to take preventive steps to overcome these challenges. The main objective of this study is to model the statistical behaviour of extreme maximum temperature values in Rwanda. To achieve such an objective, the daily temperature data spanned the period from January 2000 to December 2017 recorded at nine weather stations collected from the Rwanda Meteorological Agency were used. The two methods, namely the block maxima (BM) method and the Peaks Over Threshold (POT), were applied to model and analyse extreme temperature. Model parameters were estimated, while the extreme temperature return periods and confidence intervals were predicted. The model fit suggests Gumbel and Beta distributions to be the most appropriate models for the annual maximum of daily temperature. The results show that the temperature will continue to increase, as shown by estimated return levels. <p class="card-text"><strong>Keywords:</strong> <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=global%20warming" title=" global warming"> global warming</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=rwanda" title=" rwanda"> rwanda</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature" title=" temperature"> temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=generalised%20extreme%20value%20distribution" title=" generalised extreme value distribution"> generalised extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=generalised%20pareto%20distribution" title=" generalised pareto distribution"> generalised pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/132786/statistical-modelling-of-maximum-temperature-in-rwanda-using-extreme-value-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132786.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">183</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">5859</span> Frequency Analysis Using Multiple Parameter Probability Distributions for Rainfall to Determine Suitable Probability Distribution in Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tasir%20Khan">Tasir Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yejuan%20Wang"> Yejuan Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of extreme rainfall events is very important for flood management in river basins and the design of water conservancy infrastructure. Evaluation of quantiles of annual maximum rainfall (AMRF) is required in different environmental fields, agriculture operations, renewable energy sources, climatology, and the design of different structures. Therefore, the annual maximum rainfall (AMRF) was performed at different stations in Pakistan. Multiple probability distributions, log normal (LN), generalized extreme value (GEV), Gumbel (max), and Pearson type3 (P3) were used to find out the most appropriate distributions in different stations. The L moments method was used to evaluate the distribution parameters. Anderson darling test, Kolmogorov- Smirnov test, and chi-square test showed that two distributions, namely GUM (max) and LN, were the best appropriate distributions. The quantile estimate of a multi-parameter PD offers extreme rainfall through a specific location and is therefore important for decision-makers and planners who design and construct different structures. This result provides an indication of these multi-parameter distribution consequences for the study of sites and peak flow prediction and the design of hydrological maps. Therefore, this discovery can support hydraulic structure and flood management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RAMSE" title="RAMSE">RAMSE</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20frequency%20analysis" title=" multiple frequency analysis"> multiple frequency analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=annual%20maximum%20rainfall" title=" annual maximum rainfall"> annual maximum rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=L-moments" title=" L-moments"> L-moments</a> </p> <a href="https://publications.waset.org/abstracts/161973/frequency-analysis-using-multiple-parameter-probability-distributions-for-rainfall-to-determine-suitable-probability-distribution-in-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161973.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">5858</span> Analysis of the Statistical Characterization of Significant Wave Data Exceedances for Designing Offshore Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rui%20Teixeira">Rui Teixeira</a>, <a href="https://publications.waset.org/abstracts/search?q=Alan%20O%E2%80%99Connor"> Alan O’Connor</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Nogal"> Maria Nogal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statistical theory of extreme events is progressively a topic of growing interest in all the fields of science and engineering. The changes currently experienced by the world, economic and environmental, emphasized the importance of dealing with extreme occurrences with improved accuracy. When it comes to the design of offshore structures, particularly offshore wind turbines, the importance of efficiently characterizing extreme events is of major relevance. Extreme events are commonly characterized by extreme values theory. As an alternative, the accurate modeling of the tails of statistical distributions and the characterization of the low occurrence events can be achieved with the application of the Peak-Over-Threshold (POT) methodology. The POT methodology allows for a more refined fit of the statistical distribution by truncating the data with a minimum value of a predefined threshold u. For mathematically approximating the tail of the empirical statistical distribution the Generalised Pareto is widely used. Although, in the case of the exceedances of significant wave data (H_s) the 2 parameters Weibull and the Exponential distribution, which is a specific case of the Generalised Pareto distribution, are frequently used as an alternative. The Generalized Pareto, despite the existence of practical cases where it is applied, is not completely recognized as the adequate solution to model exceedances over a certain threshold u. References that set the Generalised Pareto distribution as a secondary solution in the case of significant wave data can be identified in the literature. In this framework, the current study intends to tackle the discussion of the application of statistical models to characterize exceedances of wave data. Comparison of the application of the Generalised Pareto, the 2 parameters Weibull and the Exponential distribution are presented for different values of the threshold u. Real wave data obtained in four buoys along the Irish coast was used in the comparative analysis. Results show that the application of the statistical distributions to characterize significant wave data needs to be addressed carefully and in each particular case one of the statistical models mentioned fits better the data than the others. Depending on the value of the threshold u different results are obtained. Other variables of the fit, as the number of points and the estimation of the model parameters, are analyzed and the respective conclusions were drawn. Some guidelines on the application of the POT method are presented. Modeling the tail of the distributions shows to be, for the present case, a highly non-linear task and, due to its growing importance, should be addressed carefully for an efficient estimation of very low occurrence events. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20events" title="extreme events">extreme events</a>, <a href="https://publications.waset.org/abstracts/search?q=offshore%20structures" title=" offshore structures"> offshore structures</a>, <a href="https://publications.waset.org/abstracts/search?q=peak-over-threshold" title=" peak-over-threshold"> peak-over-threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=significant%20wave%20data" title=" significant wave data"> significant wave data</a> </p> <a href="https://publications.waset.org/abstracts/56287/analysis-of-the-statistical-characterization-of-significant-wave-data-exceedances-for-designing-offshore-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56287.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">272</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">5857</span> Joint Probability Distribution of Extreme Water Level with Rainfall and Temperature: Trend Analysis of Potential Impacts of Climate Change</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Razmi">Ali Razmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Golian"> Saeed Golian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate change is known to have the potential to impact adversely hydrologic patterns for variables such as rainfall, maximum and minimum temperature and sea level rise. Long-term average of these climate variables could possibly change over time due to climate change impacts. In this study, trend analysis was performed on rainfall, maximum and minimum temperature and water level data of a coastal area in Manhattan, New York City, Central Park and Battery Park stations to investigate if there is a significant change in the data mean. Partial Man-Kendall test was used for trend analysis. Frequency analysis was then performed on data using common probability distribution functions such as Generalized Extreme Value (GEV), normal, log-normal and log-Pearson. Goodness of fit tests such as Kolmogorov-Smirnov are used to determine the most appropriate distributions. In flood frequency analysis, rainfall and water level data are often separately investigated. However, in determining flood zones, simultaneous consideration of rainfall and water level in frequency analysis could have considerable effect on floodplain delineation (flood extent and depth). The present study aims to perform flood frequency analysis considering joint probability distribution for rainfall and storm surge. First, correlation between the considered variables was investigated. Joint probability distribution of extreme water level and temperature was also investigated to examine how global warming could affect sea level flooding impacts. Copula functions were fitted to data and joint probability of water level with rainfall and temperature for different recurrence intervals of 2, 5, 25, 50, 100, 200, 500, 600 and 1000 was determined and compared with the severity of individual events. Results for trend analysis showed increase in long-term average of data that could be attributed to climate change impacts. GEV distribution was found as the most appropriate function to be fitted to the extreme climate variables. The results for joint probability distribution analysis confirmed the necessity for incorporation of both rainfall and water level data in flood frequency analysis. <p class="card-text"><strong>Keywords:</strong> <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=climate%20variables" title=" climate variables"> climate variables</a>, <a href="https://publications.waset.org/abstracts/search?q=copula" title=" copula"> copula</a>, <a href="https://publications.waset.org/abstracts/search?q=joint%20probability" title=" joint probability"> joint probability</a> </p> <a href="https://publications.waset.org/abstracts/49083/joint-probability-distribution-of-extreme-water-level-with-rainfall-and-temperature-trend-analysis-of-potential-impacts-of-climate-change" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49083.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">360</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">5856</span> Influence of Precipitation and Land Use on Extreme Flow in Prek Thnot River Basin of Mekong River in Cambodia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chhordaneath%20Hen">Chhordaneath Hen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ty%20Sok"> Ty Sok</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilan%20Ich"> Ilan Ich</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratboren%20Chan"> Ratboren Chan</a>, <a href="https://publications.waset.org/abstracts/search?q=Chantha%20Oeurng"> Chantha Oeurng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The damages caused by hydrological extremes such as flooding have been severe globally, and several research studies indicated extreme precipitations play a crucial role. Cambodia is one of the most vulnerable countries exposed to floods and drought as consequences of climate impact. Prek Thnot River Basin in the southwest part of Cambodia, which is in the plate and plateau region and a part of the Mekong Delta, was selected to investigate the changes in extreme precipitation and hydrological extreme. Furthermore, to develop a statistical relationship between these phenomena in this basin from 1995 to 2020 using Multiple Linear Regression. The precipitation and hydrological extreme were assessed via the attributes and trends of rainfall patterns during the study periods. The extreme flow was defined as a dependent variable, while the independent variables are various extreme precipitation indices. The study showed that all extreme precipitations indices (R10, R20, R35, CWD, R95p, R99p, and PRCPTOT) had increasing decency. However, the number of rain days per year had a decreasing tendency, which can conclude that extreme rainfall was more intense in a shorter period of the year. The study showed a similar relationship between extreme precipitation and hydrological extreme and land use change association with hydrological extreme. The direct combination of land use and precipitation equals 37% of the flood causes in this river. This study provided information on these two causes of flood events and an understanding of expectations of climate change consequences for flood and water resources management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20precipitation" title="extreme precipitation">extreme precipitation</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrological%20extreme" title=" hydrological extreme"> hydrological extreme</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use" title=" land use"> land use</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20cover" title=" land cover"> land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=Prek%20Thnot%20river%20basin" title=" Prek Thnot river basin"> Prek Thnot river basin</a> </p> <a href="https://publications.waset.org/abstracts/155816/influence-of-precipitation-and-land-use-on-extreme-flow-in-prek-thnot-river-basin-of-mekong-river-in-cambodia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155816.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">111</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">5855</span> Effect of Outliers in Assessing Significant Wave Heights Through a Time-Dependent GEV Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Calder%C3%B3n-Vega">F. Calderón-Vega</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20D.%20Garc%C3%ADa-Soto"> A. D. García-Soto</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20M%C3%B6sso"> C. Mösso</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recorded significant wave heights sometimes exhibit large uncommon values (outliers) that can be associated with extreme phenomena such as hurricanes and cold fronts. In this study, some extremely large wave heights recorded in NOAA buoys (National Data Buoy Center, noaa.gov) are used to investigate their effect in the prediction of future wave heights associated with given return periods. Extreme waves are predicted through a time-dependent model based on the so-called generalized extreme value distribution. It is found that the outliers do affect the estimated wave heights. It is concluded that a detailed inspection of outliers is envisaged to determine whether they are real recorded values since this will impact defining design wave heights for coastal protection purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GEV%20model" title="GEV model">GEV model</a>, <a href="https://publications.waset.org/abstracts/search?q=non-stationary" title=" non-stationary"> non-stationary</a>, <a href="https://publications.waset.org/abstracts/search?q=seasonality" title=" seasonality"> seasonality</a>, <a href="https://publications.waset.org/abstracts/search?q=outliers" title=" outliers"> outliers</a> </p> <a href="https://publications.waset.org/abstracts/147991/effect-of-outliers-in-assessing-significant-wave-heights-through-a-time-dependent-gev-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147991.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">195</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">5854</span> Calibration of Hybrid Model and Arbitrage-Free Implied Volatility Surface</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kun%20Huang">Kun Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates whether the combination of local and stochastic volatility models can be calibrated exactly to any arbitrage-free implied volatility surface of European option. The risk neutral Brownian Bridge density is applied for calibration of the leverage function of our Hybrid model. Furthermore, the tails of marginal risk neutral density are generated by Generalized Extreme Value distribution in order to capture the properties of asset returns. The local volatility is generated from the arbitrage-free implied volatility surface using stochastic volatility inspired parameterization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arbitrage%20free%20implied%20volatility" title="arbitrage free implied volatility">arbitrage free implied volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=calibration" title=" calibration"> calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20distribution" title=" extreme value distribution"> extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20model" title=" hybrid model"> hybrid model</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20volatility" title=" local volatility"> local volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=risk-neutral%20density" title=" risk-neutral density"> risk-neutral density</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility" title=" stochastic volatility"> stochastic volatility</a> </p> <a href="https://publications.waset.org/abstracts/62414/calibration-of-hybrid-model-and-arbitrage-free-implied-volatility-surface" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62414.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">267</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">5853</span> New Hybrid Method to Model Extreme Rainfalls</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Youness%20Laaroussi">Youness Laaroussi</a>, <a href="https://publications.waset.org/abstracts/search?q=Zine%20Elabidine%20Guennoun"> Zine Elabidine Guennoun</a>, <a href="https://publications.waset.org/abstracts/search?q=Amine%20Amar"> Amine Amar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modeling and forecasting dynamics of rainfall occurrences constitute one of the major topics, which have been largely treated by statisticians, hydrologists, climatologists and many other groups of scientists. In the same issue, we propose in the present paper a new hybrid method, which combines Extreme Values and fractal theories. We illustrate the use of our methodology for transformed Emberger Index series, constructed basing on data recorded in Oujda (Morocco). The index is treated at first by Peaks Over Threshold (POT) approach, to identify excess observations over an optimal threshold u. In the second step, we consider the resulting excess as a fractal object included in one dimensional space of time. We identify fractal dimension by the box counting. We discuss the prospect descriptions of rainfall data sets under Generalized Pareto Distribution, assured by Extreme Values Theory (EVT). We show that, despite of the appropriateness of return periods given by POT approach, the introduction of fractal dimension provides accurate interpretation results, which can ameliorate apprehension of rainfall occurrences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20values%20theory" title="extreme values theory">extreme values theory</a>, <a href="https://publications.waset.org/abstracts/search?q=fractals%20dimensions" title=" fractals dimensions"> fractals dimensions</a>, <a href="https://publications.waset.org/abstracts/search?q=peaks%20Over%20threshold" title=" peaks Over threshold"> peaks Over threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall%20occurrences" title=" rainfall occurrences"> rainfall occurrences</a> </p> <a href="https://publications.waset.org/abstracts/24836/new-hybrid-method-to-model-extreme-rainfalls" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24836.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">5852</span> Nonstationary Modeling of Extreme Precipitation in the Wei River Basin, China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiyuan%20Tao">Yiyuan Tao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Under the impact of global warming together with the intensification of human activities, the hydrological regimes may be altered, and the traditional stationary assumption was no longer satisfied. However, most of the current design standards of water infrastructures were still based on the hypothesis of stationarity, which may inevitably result in severe biases. Many critical impacts of climate on ecosystems, society, and the economy are controlled by extreme events rather than mean values. Therefore, it is of great significance to identify the non-stationarity of precipitation extremes and model the precipitation extremes in a nonstationary framework. The Wei River Basin (WRB), located in a continental monsoon climate zone in China, is selected as a case study in this study. Six extreme precipitation indices were employed to investigate the changing patterns and stationarity of precipitation extremes in the WRB. To identify if precipitation extremes are stationary, the Mann-Kendall trend test and the Pettitt test, which is used to examine the occurrence of abrupt changes are adopted in this study. Extreme precipitation indices series are fitted with non-stationary distributions that selected from six widely used distribution functions: Gumbel, lognormal, Weibull, gamma, generalized gamma and exponential distributions by means of the time-varying moments model generalized additive models for location, scale and shape (GAMLSS), where the distribution parameters are defined as a function of time. The results indicate that: (1) the trends were not significant for the whole WRB, but significant positive/negative trends were still observed in some stations, abrupt changes for consecutive wet days (CWD) mainly occurred in 1985, and the assumption of stationarity is invalid for some stations; (2) for these nonstationary extreme precipitation indices series with significant positive/negative trends, the GAMLSS models are able to capture well the temporal variations of the indices, and perform better than the stationary model. Finally, the differences between the quantiles of nonstationary and stationary models are analyzed, which highlight the importance of nonstationary modeling of precipitation extremes in the WRB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20precipitation" title="extreme precipitation">extreme precipitation</a>, <a href="https://publications.waset.org/abstracts/search?q=GAMLSSS" title=" GAMLSSS"> GAMLSSS</a>, <a href="https://publications.waset.org/abstracts/search?q=non-stationary" title=" non-stationary"> non-stationary</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20River%20Basin" title=" Wei River Basin"> Wei River Basin</a> </p> <a href="https://publications.waset.org/abstracts/112313/nonstationary-modeling-of-extreme-precipitation-in-the-wei-river-basin-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112313.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">124</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">5851</span> Orthogonal Basis Extreme Learning Algorithm and Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Li">Ying Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Li"> Yan Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new algorithm for single hidden layer feedforward neural networks (SLFN), Orthogonal Basis Extreme Learning (OBEL) algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layer(s) during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability. <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=orthogonal%20basis%20extreme%20learning" title=" orthogonal basis extreme learning"> orthogonal basis extreme learning</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a> </p> <a href="https://publications.waset.org/abstracts/15129/orthogonal-basis-extreme-learning-algorithm-and-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15129.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">534</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">5850</span> Regional Flood-Duration-Frequency Models for Norway</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danielle%20M.%20Barna">Danielle M. Barna</a>, <a href="https://publications.waset.org/abstracts/search?q=Kolbj%C3%B8rn%20Engeland"> Kolbjørn Engeland</a>, <a href="https://publications.waset.org/abstracts/search?q=Thordis%20Thorarinsdottir"> Thordis Thorarinsdottir</a>, <a href="https://publications.waset.org/abstracts/search?q=Chong-Yu%20Xu"> Chong-Yu Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Often design flood values are needed at locations with insufficient data. Additionally, in hydrologic applications where flood retention is important (e.g., floodplain management and reservoir design), design flood values are required at different flood durations. A statistical approach to this problem is a development of a regression model for extremes where some of the parameters are dependent on flood duration in addition to being covariate-dependent. In hydrology, this is called a regional flood-duration-frequency (regional-QDF) model. Typically, the underlying statistical distribution is chosen to be the Generalized Extreme Value (GEV) distribution. However, as the support of the GEV distribution depends on both its parameters and the range of the data, special care must be taken with the development of the regional model. In particular, we find that the GEV is problematic when developing a GAMLSS-type analysis due to the difficulty of proposing a link function that is independent of the unknown parameters and the observed data. We discuss these challenges in the context of developing a regional QDF model for Norway. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=design%20flood%20values" title="design flood values">design flood values</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20statistics" title=" bayesian statistics"> bayesian statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20modeling%20of%20extremes" title=" regression modeling of extremes"> regression modeling of extremes</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20analysis" title=" extreme value analysis"> extreme value analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=GEV" title=" GEV"> GEV</a> </p> <a href="https://publications.waset.org/abstracts/163938/regional-flood-duration-frequency-models-for-norway" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163938.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">72</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">5849</span> The Vertex Degree Distance of One Vertex Union of the Cycle and the Star</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Wang">Ying Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Haiyan%20Xie"> Haiyan Xie</a>, <a href="https://publications.waset.org/abstracts/search?q=Aoming%20Zhang"> Aoming Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The degree distance of a graph is a graph invariant that is more sensitive than the Wiener index. In this paper, we calculate the vertex degree distances of one vertex union of the cycle and the star, and the degree distance of one vertex union of the cycle and the star. These results lay a foundation for further study on the extreme value of the vertex degree distances, and the distribution of the vertices with the extreme value in one vertex union of the cycle and the star. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=degree%20distance" title="degree distance">degree distance</a>, <a href="https://publications.waset.org/abstracts/search?q=vertex-degree-distance" title=" vertex-degree-distance"> vertex-degree-distance</a>, <a href="https://publications.waset.org/abstracts/search?q=one%20vertex%20union%20of%20a%20cycle%20and%20a%20star" title=" one vertex union of a cycle and a star"> one vertex union of a cycle and a star</a>, <a href="https://publications.waset.org/abstracts/search?q=graph" title=" graph"> graph</a> </p> <a href="https://publications.waset.org/abstracts/127232/the-vertex-degree-distance-of-one-vertex-union-of-the-cycle-and-the-star" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127232.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">154</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">5848</span> Application of Stochastic Models to Annual Extreme Streamflow Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Hamidi%20Machekposhti">Karim Hamidi Machekposhti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Sedghi"> Hossein Sedghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study was designed to find the best stochastic model (using of time series analysis) for annual extreme streamflow (peak and maximum streamflow) of Karkheh River at Iran. The Auto-regressive Integrated Moving Average (ARIMA) model used to simulate these series and forecast those in future. For the analysis, annual extreme streamflow data of Jelogir Majin station (above of Karkheh dam reservoir) for the years 1958–2005 were used. A visual inspection of the time plot gives a little increasing trend; therefore, series is not stationary. The stationarity observed in Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots of annual extreme streamflow was removed using first order differencing (d=1) in order to the development of the ARIMA model. Interestingly, the ARIMA(4,1,1) model developed was found to be most suitable for simulating annual extreme streamflow for Karkheh River. The model was found to be appropriate to forecast ten years of annual extreme streamflow and assist decision makers to establish priorities for water demand. The Statistical Analysis System (SAS) and Statistical Package for the Social Sciences (SPSS) codes were used to determinate of the best model for this series. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stochastic%20models" title="stochastic models">stochastic models</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20streamflow" title=" extreme streamflow"> extreme streamflow</a>, <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20river" title=" Karkheh river"> Karkheh river</a> </p> <a href="https://publications.waset.org/abstracts/97759/application-of-stochastic-models-to-annual-extreme-streamflow-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97759.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">148</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">5847</span> Extreme Value Theory Applied in Reliability Analysis: Case Study of Diesel Generator Fans</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jelena%20Vucicevic">Jelena Vucicevic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reliability analysis represents a very important task in different areas of work. In any industry, this is crucial for maintenance, efficiency, safety and monetary costs. There are ways to calculate reliability, unreliability, failure density and failure rate. In this paper, the results for the reliability of diesel generator fans were calculated through Extreme Value Theory. The Extreme Value Theory is not widely used in the engineering field. Its usage is well known in other areas such as hydrology, meteorology, finance. The significance of this theory is in the fact that unlike the other statistical methods it is focused on rare and extreme values, and not on average. It should be noted that this theory is not designed exclusively for extreme events, but for extreme values in any event. Therefore, this is a great opportunity to apply the theory and test if it could be applied in this situation. The significance of the work is the calculation of time to failure or reliability in a new way, using statistic. Another advantage of this calculation is that there is no need for technical details and it can be implemented in any part for which we need to know the time to fail in order to have appropriate maintenance, but also to maximize usage and minimize costs. In this case, calculations have been made on diesel generator fans but the same principle can be applied to any other part. The data for this paper came from a field engineering study of the time to failure of diesel generator fans. The ultimate goal was to decide whether or not to replace the working fans with a higher quality fan to prevent future failures. The results achieved in this method will show the approximation of time for which the fans will work as they should, and the percentage of probability of fans working more than certain estimated time. Extreme Value Theory can be applied not only for rare and extreme events, but for any event that has values which we can consider as extreme. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title="extreme value theory">extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=lifetime" title=" lifetime"> lifetime</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability%20analysis" title=" reliability analysis"> reliability analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=statistic" title=" statistic"> statistic</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20to%20failure" title=" time to failure"> time to failure</a> </p> <a href="https://publications.waset.org/abstracts/78201/extreme-value-theory-applied-in-reliability-analysis-case-study-of-diesel-generator-fans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78201.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">328</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">5846</span> Prediction of Extreme Precipitation in East Asia Using Complex Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Feng%20Guolin">Feng Guolin</a>, <a href="https://publications.waset.org/abstracts/search?q=Gong%20Zhiqiang"> Gong Zhiqiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to study the spatial structure and dynamical mechanism of extreme precipitation in East Asia, a corresponding climate network is constructed by employing the method of event synchronization. It is found that the area of East Asian summer extreme precipitation can be separated into two regions: one with high area weighted connectivity receiving heavy precipitation mostly during the active phase of the East Asian Summer Monsoon (EASM), and another one with low area weighted connectivity receiving heavy precipitation during both the active and the retreat phase of the EASM. Besides，a way for the prediction of extreme precipitation is also developed by constructing a directed climate networks. The simulation accuracy in East Asia is 58% with a 0-day lead, and the prediction accuracy is 21% and average 12% with a 1-day and an n-day (2≤n≤10) lead, respectively. Compare to the normal EASM year, the prediction accuracy is lower in a weak year and higher in a strong year, which is relevant to the differences in correlations and extreme precipitation rates in different EASM situations. Recognizing and identifying these effects is good for understanding and predicting extreme precipitation in East Asia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synchronization" title="synchronization">synchronization</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20network" title=" climate network"> climate network</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a> </p> <a href="https://publications.waset.org/abstracts/64827/prediction-of-extreme-precipitation-in-east-asia-using-complex-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64827.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">442</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">5845</span> Possibility Theory Based Multi-Attribute Decision-Making: Application in Facility Location-Selection Problem under Uncertain and Extreme Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bezhan%20Ghvaberidze">Bezhan Ghvaberidze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A fuzzy multi-objective facility location-selection problem (FLSP) under uncertain and extreme environments based on possibility theory is developed. The model’s uncertain parameters in the q-rung orthopair fuzzy values are presented and transformed in the Dempster-Shaper’s belief structure environment. An objective function – distribution centers’ selection ranking index as an extension of Dempster’s extremal expectations under discrimination q-rung orthopair fuzzy information is constructed. Experts evaluate each humanitarian aid from distribution centers (HADC) against each of the uncertain factors. HADCs location problem is reduced to the bicriteria problem of partitioning the set of customers by the set of centers: (1) – Minimization of transportation costs; (2) – Maximization of centers’ selection ranking indexes. Partitioning type constraints are also constructed. For an illustration of the obtained results, a numerical example is created from the facility location-selection problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FLSP" title="FLSP">FLSP</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20combinatorial%20optimization%20problem" title=" multi-objective combinatorial optimization problem"> multi-objective combinatorial optimization problem</a>, <a href="https://publications.waset.org/abstracts/search?q=evidence%20theory" title=" evidence theory"> evidence theory</a>, <a href="https://publications.waset.org/abstracts/search?q=HADC" title=" HADC"> HADC</a>, <a href="https://publications.waset.org/abstracts/search?q=q-rung%20orthopair%20fuzzy%20set" title=" q-rung orthopair fuzzy set"> q-rung orthopair fuzzy set</a>, <a href="https://publications.waset.org/abstracts/search?q=possibility%20theory" title=" possibility theory"> possibility theory</a> </p> <a href="https://publications.waset.org/abstracts/161061/possibility-theory-based-multi-attribute-decision-making-application-in-facility-location-selection-problem-under-uncertain-and-extreme-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161061.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">119</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">5844</span> Applying the Extreme-Based Teaching Model in Post-Secondary Online Classroom Setting: A Field Experiment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leon%20Pan">Leon Pan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The first programming course within post-secondary education has long been recognized as a challenging endeavor for both educators and students alike. Historically, these courses have exhibited high failure rates and a notable number of dropouts. Instructors often lament students' lack of effort in their coursework, and students often express frustration that the teaching methods employed are not effective. Drawing inspiration from the successful principles of Extreme Programming, this study introduces an approach—the Extremes-based teaching model — aimed at enhancing the teaching of introductory programming courses. To empirically determine the effectiveness of the model, a comparison was made between a section taught using the extreme-based model and another utilizing traditional teaching methods. Notably, the extreme-based teaching class required students to work collaboratively on projects while also demanding continuous assessment and performance enhancement within groups. This paper details the application of the extreme-based model within the post-secondary online classroom context and presents the compelling results that emphasize its effectiveness in advancing the teaching and learning experiences. The extreme-based model led to a significant increase of 13.46 points in the weighted total average and a commendable 10% reduction in the failure rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme-based%20teaching%20model" title="extreme-based teaching model">extreme-based teaching model</a>, <a href="https://publications.waset.org/abstracts/search?q=innovative%20pedagogical%20methods" title=" innovative pedagogical methods"> innovative pedagogical methods</a>, <a href="https://publications.waset.org/abstracts/search?q=project-based%20learning" title=" project-based learning"> project-based learning</a>, <a href="https://publications.waset.org/abstracts/search?q=team-based%20learning" title=" team-based learning"> team-based learning</a> </p> <a href="https://publications.waset.org/abstracts/171936/applying-the-extreme-based-teaching-model-in-post-secondary-online-classroom-setting-a-field-experiment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171936.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">59</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=extreme%20value%20distribution&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20value%20distribution&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20value%20distribution&page=4">4</a></li> <li class="page-item"><a class="page-link" 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