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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.citedby11
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.accessioned2024-10-14T03:18:09Z
dc.date.available2024-10-14T03:18:09Z
dc.date.issued2023
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 0.5en_US
dc.description.abstractwith 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 inflowen_US
dc.description.abstractbut 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.natureFinalen_US
dc.identifier.doi10.1007/s10489-022-04029-7
dc.identifier.epage10916
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85137012730
dc.identifier.spage10893
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/123456789/34145
dc.identifier.volume53
dc.pagecount23
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleApplied Intelligence
dc.subjectAdaptive neuro-fuzzy inference system (ANFIS)
dc.subjectExtreme Gradient Boosting (XG-Boost)
dc.subjectGrid Search optimizer
dc.subjectHyper-parameters
dc.subjectInflow Forecast
dc.subjectMachine learning
dc.subjectMultilayer Perceptron neural network (MLPNN)
dc.subjectSupport Vector Regression (SVR)
dc.subjectAdaptive boosting
dc.subjectDecision making
dc.subjectForecasting
dc.subjectFuzzy inference
dc.subjectFuzzy neural networks
dc.subjectFuzzy systems
dc.subjectMultilayer neural networks
dc.subjectMultilayers
dc.subjectReservoirs (water)
dc.subjectWater resources
dc.subjectAdaptive neuro-fuzzy inference
dc.subjectAdaptive neuro-fuzzy inference system
dc.subjectExtreme gradient boosting (XG-boost)
dc.subjectGradient boosting
dc.subjectGrid search
dc.subjectGrid search optimizer
dc.subjectHyper-parameter
dc.subjectInflow forecast
dc.subjectMachine-learning
dc.subjectMultilayer perceptron neural network
dc.subjectMultilayers perceptrons
dc.subjectNeuro-fuzzy inference systems
dc.subjectPerceptron neural networks
dc.subjectSearch optimizer
dc.subjectSupport vector regression
dc.subjectSupport vector regressions
dc.subjectMachine learning
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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