Developments and Trends in Water Level Forecasting Using Machine Learning Models—A Review
Water level forecasting in rivers, lakes, and reservoirs is crucial for effective water resource management, flood control, and environmental planning. This review examines the latest developments and trends in water level forecasting research from 2011-2024. A wide range of methods are explored, in...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10949142/ |
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| author | Abdus Samad Azad Nahina Islam Md Nurun Nabi Hifsa Khurshid Mohammad Ashraful Siddique |
| author_facet | Abdus Samad Azad Nahina Islam Md Nurun Nabi Hifsa Khurshid Mohammad Ashraful Siddique |
| author_sort | Abdus Samad Azad |
| collection | DOAJ |
| description | Water level forecasting in rivers, lakes, and reservoirs is crucial for effective water resource management, flood control, and environmental planning. This review examines the latest developments and trends in water level forecasting research from 2011-2024. A wide range of methods are explored, including traditional statistical models (ARIMA, regression) and advanced techniques like artificial neural networks (ANN), fuzzy logic, support vector machines (SVM), and deep learning models (LSTM). The study assesses the performance and accuracy of these applied models, analyzing their strengths and limitations in capturing water system dynamics and uncertainties. It investigates how data sources (hydrological, meteorological, historical) and variables (rainfall, evaporation, inflow) impact forecast accuracy. The significance of different variables for improving model predictive capabilities is determined. Spatiotemporal aspects are explored, examining model applicability across local, regional, and global scales. Approaches to quantifying and communicating uncertainties associated with probabilistic forecasting for decision-making are evaluated. Detailed analysis identifies proven model efficiencies, potential challenges, and suggests future research directions. By comprehensively reviewing recent water level forecasting literature, this study provides state-of-the-art knowledge on applying machine learning models for reservoir water level prediction. It guides water resource strategies, flood mitigation measures, and decision-making for sustainable water systems management. This review is a valuable resource for researchers and practitioners in hydrology and related fields. |
| format | Article |
| id | doaj-art-16b5b78ce0df461ea77fa4fbea61e398 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-16b5b78ce0df461ea77fa4fbea61e3982025-08-20T02:12:34ZengIEEEIEEE Access2169-35362025-01-0113630486306510.1109/ACCESS.2025.355791010949142Developments and Trends in Water Level Forecasting Using Machine Learning Models—A ReviewAbdus Samad Azad0https://orcid.org/0000-0001-7429-4134Nahina Islam1https://orcid.org/0000-0002-5469-8104Md Nurun Nabi2https://orcid.org/0000-0002-4087-930XHifsa Khurshid3Mohammad Ashraful Siddique4https://orcid.org/0009-0000-3246-5177School of Engineering and Technology, Central Queensland University, Rockhampton, AustraliaSchool of Engineering and Technology, Central Queensland University, Rockhampton, AustraliaSchool of Engineering and Technology, Central Queensland University, Rockhampton, AustraliaInterdisciplinary Research Center for Advanced Materials, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaSchool of Engineering and Technology, Central Queensland University, Rockhampton, AustraliaWater level forecasting in rivers, lakes, and reservoirs is crucial for effective water resource management, flood control, and environmental planning. This review examines the latest developments and trends in water level forecasting research from 2011-2024. A wide range of methods are explored, including traditional statistical models (ARIMA, regression) and advanced techniques like artificial neural networks (ANN), fuzzy logic, support vector machines (SVM), and deep learning models (LSTM). The study assesses the performance and accuracy of these applied models, analyzing their strengths and limitations in capturing water system dynamics and uncertainties. It investigates how data sources (hydrological, meteorological, historical) and variables (rainfall, evaporation, inflow) impact forecast accuracy. The significance of different variables for improving model predictive capabilities is determined. Spatiotemporal aspects are explored, examining model applicability across local, regional, and global scales. Approaches to quantifying and communicating uncertainties associated with probabilistic forecasting for decision-making are evaluated. Detailed analysis identifies proven model efficiencies, potential challenges, and suggests future research directions. By comprehensively reviewing recent water level forecasting literature, this study provides state-of-the-art knowledge on applying machine learning models for reservoir water level prediction. It guides water resource strategies, flood mitigation measures, and decision-making for sustainable water systems management. This review is a valuable resource for researchers and practitioners in hydrology and related fields.https://ieeexplore.ieee.org/document/10949142/Artificial intelligenceforecastingmachine learningreservoirwater level |
| spellingShingle | Abdus Samad Azad Nahina Islam Md Nurun Nabi Hifsa Khurshid Mohammad Ashraful Siddique Developments and Trends in Water Level Forecasting Using Machine Learning Models—A Review IEEE Access Artificial intelligence forecasting machine learning reservoir water level |
| title | Developments and Trends in Water Level Forecasting Using Machine Learning Models—A Review |
| title_full | Developments and Trends in Water Level Forecasting Using Machine Learning Models—A Review |
| title_fullStr | Developments and Trends in Water Level Forecasting Using Machine Learning Models—A Review |
| title_full_unstemmed | Developments and Trends in Water Level Forecasting Using Machine Learning Models—A Review |
| title_short | Developments and Trends in Water Level Forecasting Using Machine Learning Models—A Review |
| title_sort | developments and trends in water level forecasting using machine learning models x2014 a review |
| topic | Artificial intelligence forecasting machine learning reservoir water level |
| url | https://ieeexplore.ieee.org/document/10949142/ |
| work_keys_str_mv | AT abdussamadazad developmentsandtrendsinwaterlevelforecastingusingmachinelearningmodelsx2014areview AT nahinaislam developmentsandtrendsinwaterlevelforecastingusingmachinelearningmodelsx2014areview AT mdnurunnabi developmentsandtrendsinwaterlevelforecastingusingmachinelearningmodelsx2014areview AT hifsakhurshid developmentsandtrendsinwaterlevelforecastingusingmachinelearningmodelsx2014areview AT mohammadashrafulsiddique developmentsandtrendsinwaterlevelforecastingusingmachinelearningmodelsx2014areview |