Wee, Wei Joe and Zaini, Nur'atiah Binti and Ahmed, Ali Najah and El-Shafie, Ahmed (2021) A review of models for water level forecasting based on machine learning. Earth Science Informatics, 14 (4). pp. 1707-1728. ISSN 1865-0473, DOI https://doi.org/10.1007/s12145-021-00664-9.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | College of Graduate Studies (COGS), Universiti Tenaga Nasional (UNITEN) under UNITEN POSTGRADUATE SCHOLARSHIP 2019 |
| Uncontrolled Keywords: | Artificial intelligence; Machine learning; Water level; Reservoir; Forecasting |
| Subjects: | Q Science > QE Geology T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Apr 2022 06:36 |
| Last Modified: | 14 Apr 2022 06:36 |
| URI: | http://eprints.um.edu.my/id/eprint/26761 |
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