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Forecasting multi-step-ahead reservoir monthly and daily inflow using machine learning models based on different scenarios

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dc.citedby1
dc.contributor.authorIbrahim K.S.M.H.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorKoo C.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57225749816en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57204843657en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:39:59Z
dc.date.available2023-05-29T09:39:59Z
dc.date.issued2022
dc.descriptionAdaptive boosting; Decision making; Forecasting; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Multilayer neural networks; Multilayers; Reservoirs (water); Water resources; Adaptive neuro-fuzzy inference; Adaptive neuro-fuzzy inference system; Extreme gradient boosting (XG-boost); Gradient boosting; Grid search; Grid search optimizer; Hyper-parameter; Inflow forecast; Machine-learning; Multilayer perceptron neural network; Multilayers perceptrons; Neuro-fuzzy inference systems; Perceptron neural networks; Search optimizer; Support vector regression; Support vector regressions; Machine learningen_US
dc.description.abstractDam reservoir operations are a critical issue for decision-makers in maximizing the use of water resources. Artificial Intelligence and Machine Learning models (AI & ML) approaches are increasingly popular for reservoir inflow predictions. In this study, the multilayer perceptron neural network (MLP), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and the Extreme Gradient Boosting (XG-Boost), were adopted to forecast reservoir inflows for the monthly and daily timeframes. Results showed that: (1) For the monthly timeframe, all the four models were proficient in obtaining efficient monthly reservoir inflows by scoring at least an R� of 0.5; with the XG-Boost ranked as the best model, followed by the MLPNN, SVR, and lastly ANFIS. (2) the XG-Boost still outperforms all other models for forecasting daily inflow; but however, with reduced performance. The models were still ranked in the same order, with the ANFIS showing very poor performance in scenario-2, scenario-3, and scenario-4. (3) For daily inflows, the best scenarios are scenario-5, scenario-6, scenario-7 as the models were trained based on the 1,3,5, days-lag forecasted inflow, and overall, the XG-Boost outperforms all the other models. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.en_US
dc.description.natureArticle in Pressen_US
dc.identifier.doi10.1007/s10489-022-04029-7
dc.identifier.scopus2-s2.0-85137012730
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85137012730&doi=10.1007%2fs10489-022-04029-7&partnerID=40&md5=bd9166cb42dfb864b6f3eca345fdaa72
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27131
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleApplied Intelligence
dc.titleForecasting multi-step-ahead reservoir monthly and daily inflow using machine learning models based on different scenariosen_US
dc.typeArticleen_US
dspace.entity.typePublication
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