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A review of models for water level forecasting based on machine learning

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dc.citedby7
dc.contributor.authorWee W.J.en_US
dc.contributor.authorZaini N.B.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57226181151en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:05:25Z
dc.date.available2023-05-29T09:05:25Z
dc.date.issued2021
dc.descriptionforecasting method; literature review; machine learning; numerical model; reservoir; water level; water resourceen_US
dc.description.abstractIt is crucial to keep an eye on the water levels in reservoirs in order for them to perform at peak, as they are one of the, if not, the most vital part in water resource management. The water stored is essential in providing water supply, generating hydropower as well as preventing overlasting droughts. Thus, efficient forecasting models are essential in overcoming the issues revolving around hydropower reservoir stations. This paper reviewed the previous research on application of machine learning techniques in forecasting water level in reservoirs. In this review, the discussed machine learning techniques are ANN, ANFIS, BA, COA, SVM, etc., and their main benefits, as well as the literature, are the main focus. Initially, a general study regarding the fundamentals of the respective methods were made. Furthermore, the affecting conditions of water level forecasting, as well as the common issues faced, was also identified, in order to achieve the best results. The advantages and distadvatanges of the algorithms are extracted. In conclusion, hybrid metaheuristic algorithm produced more efficient results. This review paper covered researches conducted from the year 2000 to 2020. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s12145-021-00664-9
dc.identifier.epage1728
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85110872139
dc.identifier.spage1707
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85110872139&doi=10.1007%2fs12145-021-00664-9&partnerID=40&md5=a51df55df9c3a5bf44f07924dca9ead9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25886
dc.identifier.volume14
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEarth Science Informatics
dc.titleA review of models for water level forecasting based on machine learningen_US
dc.typeReviewen_US
dspace.entity.typePublication
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