Utilization of PSO algorithm in estimation of water level change of Lake Beysehir

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dc.contributor.authorBuyukyildiz, Meral
dc.contributor.authorTezel, Gulay
dc.date.accessioned2020-03-26T19:43:25Z
dc.date.available2020-03-26T19:43:25Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, unlike backpropagation algorithm which gets local best solutions, the usefulness of particle swarm optimization (PSO) algorithm, a population-based optimization technique with a global search feature, inspired by the behavior of bird flocks, in determination of parameters of support vector machines (SVM) and adaptive network-based fuzzy inference system (ANFIS) methods was investigated. For this purpose, the performances of hybrid PSO-epsilon support vector regression (PSO-epsilon SVR) and PSO-ANFIS models were studied to estimate water level change of Lake Beysehir in Turkey. The change in water level was also estimated using generalized regression neural network (GRNN) method, an iterative training procedure. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R (2)) were used to compare the obtained results. Efforts were made to estimate water level change (L) using different input combinations of monthly inflow-lost flow (I), precipitation (P), evaporation (E), and outflow (O). According to the obtained results, the other methods except PSO-ANN generally showed significantly similar performances to each other. PSO-epsilon SVR method with the values of minMAE = 0.0052 m, maxMAE = 0.04 m, and medianMAE = 0.0198 m; minRMSE = 0.0070 m, maxRMSE = 0.0518 m, and medianRMSE = 0.0241 m; minR (2) = 0.9169, maxR (2) = 0.9995, medianR (2) = 0.9909 for the I-P-E-O combination in testing period became superior in forecasting water level change of Lake Beysehir than the other methods. PSO-ANN models were the least successful models in all combinations.en_US
dc.identifier.doi10.1007/s00704-015-1660-2en_US
dc.identifier.endpage191en_US
dc.identifier.issn0177-798Xen_US
dc.identifier.issn1434-4483en_US
dc.identifier.issue01.02.2020en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage181en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00704-015-1660-2
dc.identifier.urihttps://hdl.handle.net/20.500.12395/35673
dc.identifier.volume128en_US
dc.identifier.wosWOS:000398936200014en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER WIENen_US
dc.relation.ispartofTHEORETICAL AND APPLIED CLIMATOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleUtilization of PSO algorithm in estimation of water level change of Lake Beysehiren_US
dc.typeArticleen_US

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