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A review of hybrid deep learning applications for streamflow forecasting

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dc.citedby31
dc.contributor.authorNg K.W.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorKoo C.H.en_US
dc.contributor.authorChong K.L.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorid58590894900en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57204843657en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid58136810800en_US
dc.date.accessioned2024-10-14T03:17:34Z
dc.date.available2024-10-14T03:17:34Z
dc.date.issued2023
dc.description.abstractDeep learning has emerged as a powerful tool for streamflow forecasting and its applications have garnered significant interest in the hydrological community. Despite the publication of several review articles on machine learning applications in streamflow forecasting, no review paper has yet focused explicitly on deep learning and its hybrid forms. This paper starts with some characteristics of deep learning models to provide a quick view of deep learning. Next, the configurations and characteristics of hybrid deep learning models, which is a hybridization of modeling techniques with deep learning, are discussed. Another vital role while implementing deep learning modeling is the methods applied for input and hyperparameter optimization. Finally, the limitations encountered in streamflow forecasting using deep learning models and recommendations for further research are outlined. This review covers related studies from 2017 to 2023 to provide the most recent snapshot of deep learning modeling applications in streamflow forecasting. These efforts are expected to contribute to the advancement of streamflow forecasting, potentially enabling more informed decision-making in water resource management. � 2023 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo130141
dc.identifier.doi10.1016/j.jhydrol.2023.130141
dc.identifier.scopus2-s2.0-85171451159
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85171451159&doi=10.1016%2fj.jhydrol.2023.130141&partnerID=40&md5=903795ed5d7799200bb4caaccead356f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33983
dc.identifier.volume625
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Hydrology
dc.subjectAlgorithms
dc.subjectOptimization
dc.subjectPrediction
dc.subjectRiver
dc.subjectRunoff
dc.subjectSupervised learning
dc.subjectDecision making
dc.subjectDeep learning
dc.subjectLearning systems
dc.subjectStream flow
dc.subjectWater management
dc.subjectForecasting: applications
dc.subjectHybrid forms
dc.subjectHybridisation
dc.subjectITS applications
dc.subjectLearning models
dc.subjectMachine learning applications
dc.subjectOn-machines
dc.subjectOptimisations
dc.subjectReview papers
dc.subjectStreamflow forecasting
dc.subjectalgorithm
dc.subjectmachine learning
dc.subjectoptimization
dc.subjectprediction
dc.subjectriver
dc.subjectrunoff
dc.subjectstreamflow
dc.subjectForecasting
dc.titleA review of hybrid deep learning applications for streamflow forecastingen_US
dc.typeReviewen_US
dspace.entity.typePublication
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