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

forecasting method; literature review; machine learning; numerical model; reservoir; water level; water resource

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Bibliographic Details
Main Authors: Wee W.J., Zaini N.B., Ahmed A.N., El-Shafie A.
Other Authors: 57226181151
Format: Review
Published: Springer Science and Business Media Deutschland GmbH 2023
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id my.uniten.dspace-25886
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spelling my.uniten.dspace-258862023-05-29T17:05:25Z A review of models for water level forecasting based on machine learning Wee W.J. Zaini N.B. Ahmed A.N. El-Shafie A. 57226181151 56905328500 57214837520 16068189400 forecasting method; literature review; machine learning; numerical model; reservoir; water level; water resource It 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. Final 2023-05-29T09:05:25Z 2023-05-29T09:05:25Z 2021 Review 10.1007/s12145-021-00664-9 2-s2.0-85110872139 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110872139&doi=10.1007%2fs12145-021-00664-9&partnerID=40&md5=a51df55df9c3a5bf44f07924dca9ead9 https://irepository.uniten.edu.my/handle/123456789/25886 14 4 1707 1728 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description forecasting method; literature review; machine learning; numerical model; reservoir; water level; water resource
author2 57226181151
author_facet 57226181151
Wee W.J.
Zaini N.B.
Ahmed A.N.
El-Shafie A.
format Review
author Wee W.J.
Zaini N.B.
Ahmed A.N.
El-Shafie A.
spellingShingle Wee W.J.
Zaini N.B.
Ahmed A.N.
El-Shafie A.
A review of models for water level forecasting based on machine learning
author_sort Wee W.J.
title A review of models for water level forecasting based on machine learning
title_short A review of models for water level forecasting based on machine learning
title_full A review of models for water level forecasting based on machine learning
title_fullStr A review of models for water level forecasting based on machine learning
title_full_unstemmed A review of models for water level forecasting based on machine learning
title_sort review of models for water level forecasting based on machine learning
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806423523742187520
score 13.222552

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