Machine learning approaches coupled with variational mode decomposition: a novel method for forecasting monthly reservoir inflows

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dc.authoridGHAREHBAGHI, AMIN/0000-0002-2898-3681;
dc.authorwosidGHAREHBAGHI, AMIN/JVD-7375-2023
dc.authorwosidGhasemlounia, Redvan/GQZ-7676-2022
dc.contributor.authorAhmadi, Farshad
dc.contributor.authorGhasemlounia, Redvan
dc.contributor.authorGharehbaghi, Amin
dc.date.accessioned2024-06-13T20:17:51Z
dc.date.available2024-06-13T20:17:51Z
dc.date.issued2024
dc.departmentİstanbul Gedik Üniversitesi
dc.description.abstractReliable and precise reservoir inflow predicting is very significant for water resource management. In this research, different single and hybrid Variational Mode Decomposition (VMD) with estimation models including K-star (K*), Gaussian Process Regression (GPR), and Long Short-Term Memory (LSTM) models are developed to predict long-term time series of average monthly reservoir inflows in Baroon Dam (RIBDm) sited in Maku city, Northwest Iran. Using Pearson's correlation coefficient (PCC) analysis among observed potential meteorological predictors and RIBDm confirms the rainfall (Pave) as the only effective input variable. To reduce the influence of overfitting problems and well-configuration of the approaches developed, an algorithm tuning over meta-parameters together with a trial-and-error technique are applied. The outcomes of modeling show that in the both single K* and hybrid VMD-K* models, the optimum value of the global blend parameter (b) is 10%, yet, by rising the value of b from 10 to 100%, the accuracy of both models are markedly reduced. In both standard LSTM and hybrid VMD-LSTM models, the ideal dropout rate (P-rate) is gained 0.5. Likewise, in both models, as number of hidden neurons (NHN) is held constant, increasing P-rate causes to decrease running time, also as P-rate remains constant, increasing NHN causes to increase running time. Results of statistical indicators and visual analysis of comparison plots approve the hybrid VMD-LSTM model as the superior method with an R2 of 0.8, KGE of 0.87, RMSE of 1.15 (m3/s), and MBE of 0.15 (m3/s). Nonetheless, under the ideal scenario by the K* model, R2 is 0.27, RMSE is 2.75 (m3/s), KGE is 0.43, and MBE is 0.15 (m3/s).
dc.identifier.doi10.1007/s12145-023-01186-2
dc.identifier.endpage760
dc.identifier.issn1865-0473
dc.identifier.issn1865-0481
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85180207284
dc.identifier.scopusqualityQ2
dc.identifier.startpage745
dc.identifier.urihttps://doi.org/10.1007/s12145-023-01186-2
dc.identifier.urihttps://hdl.handle.net/11501/1117
dc.identifier.volume17
dc.identifier.wosWOS:001127994300001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEarth Science Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectVariational Mode Decomposition (Vmd) Algorithm
dc.subjectGaussian Process Regression (Gpr)
dc.subjectK-Star Model
dc.subjectLong Short-Term Memory (Lstm) Model
dc.subjectMonthly Reservoir Inflows
dc.subjectNeural-Network
dc.subjectPrediction
dc.subjectLstm
dc.titleMachine learning approaches coupled with variational mode decomposition: a novel method for forecasting monthly reservoir inflows
dc.typeArticle

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