Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series

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dc.contributor.authorParsaie, Abbas
dc.contributor.authorGhasemlounia, Redvan
dc.contributor.authorGharehbaghi, Amin
dc.contributor.authorHaghiabi, AmirHamzeh
dc.contributor.authorChadee, Aaron Anil
dc.contributor.authorNou, Mohammad Rashki Ghale
dc.date.accessioned2024-06-13T20:18:02Z
dc.date.available2024-06-13T20:18:02Z
dc.date.issued2024
dc.departmentFakülteler, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractA high-accuracy estimation of the runoff has always been an extremely relevant and challenging subject in hydrology science. Therefore, in the current research, a novel hybrid decomposition-integration-optimization based model is developed to enhance the estimation precision of the runoff. The suggested predictive model is a combination of successive variational mode decomposition (SVMD) technique and Multi-Layer Perceptron neural network (MLP) model integrated with particle swarm optimization (PSO) meta-heuristic algorithm (i.e., hybrid SVMD-MLP-PSO model). To test its performance, the mean monthly runoff data recorded from Sep 1986Aug 2017 in Dez River basin (MRDRm), southwest of Iran, are used. The performance of the recommended model is also matched with other different hybrid and single models including MLP-PSO, SVMD-MLP, and MLP as the benchmark model. In all models, the sequence-to-one regression module of forecasting (i.e., without using meteorological parameters recorded in the study region) is utilized. In the SVMD based hybrid models, the optimal value of compactness of mode (alpha) for the original MRDRm time series is achieved at 100. Then, the PACF (partial autocorrelation function) diagram related to the lag length from each decomposed intrinsic mode function (IMF) sub-signals sequence generated is operated to select the ideal input variables. Performance evaluation metrics prove that the hybrid SVMD-MLP-PSO model under the best predictor and meta-parameters, outperformed with an R2 of 0.89, modified 2012 version of Kling-Gupta efficiency (KGE') of 0.83, volumetric efficiency (VE) of 0.91, Nash-Sutcliffe efficiency (NSE) of 0.88, and RMSE of 13.91 m3/s. Comparatively, the standalone MLP as the benchmark model results in an R2 of 0.24, VE of 0.33, KGE' of 0.2, NSE of 0.29, and RMSE of 153.39 m3/s.
dc.identifier.doi10.1016/j.jhydrol.2024.131041
dc.identifier.issn0022-1694
dc.identifier.issn1879-2707
dc.identifier.scopus2-s2.0-85188722507
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jhydrol.2024.131041
dc.identifier.urihttps://hdl.handle.net/11501/1179
dc.identifier.volume634
dc.identifier.wosWOS:001215266800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGhasemlounia, Redvan
dc.institutionauthorid0000-0003-1796-4562
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Hydrology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMonthly Runoff Forecasting
dc.subjectHybrid Predictive Models
dc.subjectSvmd Algorithm
dc.subjectDez River
dc.titleNovel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series
dc.typeArticle

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