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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Springer Science and Business Media Deutschland GmbH
2023
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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 |
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| 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 |
13.211869 |