Publication:
A review of deep learning and machine learning techniques for hydrological inflow forecasting

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dc.citedby12
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2024-10-14T03:17:30Z
dc.date.available2024-10-14T03:17:30Z
dc.date.issued2023
dc.description.abstractConventional machine learning models have been widely used for reservoir inflow and rainfall prediction. Nowadays, researchers focus on a new computing architecture in the area of AI, namely, deep learning for hydrological forecasting parameters. This review paper tends to broadcast more of the intriguing interest in reservoir inflow prediction utilizing deep learning and machine learning algorithms. The AI models utilized for different hydrology sectors, as well as the most prevalent machine learning techniques, will be explored in this thorough study, which divides AI techniques into two primary categories: deep learning and machine learning. In this study, we look at the long short-term memory deep learning method as well as three traditional machine learning algorithms: support vector machine, random forest, and boosted regression tree. Under each part, a summary of the findings is provided. For convenience of reference, some of the benefits and drawbacks discovered through literature reviews have been listed. Finally, future recommendations and overall conclusions based on research findings are given. This review focuses on papers from high-impact factor periodicals published over a 4�years period beginning in 2018 onwards. � 2023, The Author(s), under exclusive licence to Springer Nature B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s10668-023-03131-1
dc.identifier.epage12216
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85150172679
dc.identifier.spage12189
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85150172679&doi=10.1007%2fs10668-023-03131-1&partnerID=40&md5=03c6fb3c45ace3d5e5a5adc73d3e9646
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33955
dc.identifier.volume25
dc.pagecount27
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleEnvironment, Development and Sustainability
dc.subjectDeep learning
dc.subjectLong short-term memory (LSTM)
dc.subjectMachine learning
dc.subjectStreamflow prediction
dc.subjectalgorithm
dc.subjectforecasting method
dc.subjectinflow
dc.subjectliterature review
dc.subjectmachine learning
dc.subjectprediction
dc.subjectrainfall
dc.subjectreservoir
dc.subjectstreamflow
dc.titleA review of deep learning and machine learning techniques for hydrological inflow forecastingen_US
dc.typeReviewen_US
dspace.entity.typePublication
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