A review of the determinants and prediction methods for off-channel water demand
Off-channel water demand and consumption are critical components of water resource management, influenced by various natural and anthropogenic factors. This study systematically analysed these influencing factors and the methods for predicting off-channel water demand, aiming to provide insights for...
| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
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Science Press
2025-01-01
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| Series: | Guan'gai paishui xuebao |
| Subjects: | |
| Online Access: | https://www.ggpsxb.com/jgpxxben/ch/reader/view_abstract.aspx?file_no=20250101&flag=1 |
| _version_ | 1826866210881404928 |
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| author | HE Yanhu XU Xiaodi |
| author_facet | HE Yanhu XU Xiaodi |
| author_sort | HE Yanhu |
| collection | DOAJ |
| description | Off-channel water demand and consumption are critical components of water resource management, influenced by various natural and anthropogenic factors. This study systematically analysed these influencing factors and the methods for predicting off-channel water demand, aiming to provide insights for effective water resource planning and management at the catchment level. The research employed Cite Space for bibliometric analysis to identify current research trends, key hotspots, and systematically categorised the factors that influence the off-channel water demand, as well as the methods used for its prediction. Key factors affecting off-channel water demand in most catchments include population, water pricing, precipitation, and air temperature. Machine learning algorithms have emerged as a prominent tool for predicting off-channel water demand, often used alongside regression analysis to assess the influence of multiple factors. The quota index method remains widely applied in practical water resource management. Additionally, hybrid approaches, combining time series analysis with other methods, address limitations in standalone models and enhance prediction accuracy. Advances in remote sensing, geospatial big data, artificial intelligence, and machine learning algorithms have significantly improved the accuracy of off-channel water demand predictions, particularly at smaller scales. Future research should focus on enhancing the validation of prediction models and ensuring the robust integration of historical data to improve modeling reliability for off-channel water demand and consumption. |
| first_indexed | 2025-02-16T21:56:08Z |
| format | Article |
| id | doaj.art-27cc49eb3ff74349b185ccfe0569b7ed |
| institution | Directory Open Access Journal |
| issn | 1672-3317 |
| language | zho |
| last_indexed | 2025-02-16T21:56:08Z |
| publishDate | 2025-01-01 |
| publisher | Science Press |
| record_format | Article |
| series | Guan'gai paishui xuebao |
| spelling | doaj.art-27cc49eb3ff74349b185ccfe0569b7ed2025-01-16T07:30:52ZzhoScience PressGuan'gai paishui xuebao1672-33172025-01-014411710.13522/j.cnki.ggps.20241551672-3317(2025)01-0001-07A review of the determinants and prediction methods for off-channel water demandHE Yanhu0XU Xiaodi1School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, ChinaOff-channel water demand and consumption are critical components of water resource management, influenced by various natural and anthropogenic factors. This study systematically analysed these influencing factors and the methods for predicting off-channel water demand, aiming to provide insights for effective water resource planning and management at the catchment level. The research employed Cite Space for bibliometric analysis to identify current research trends, key hotspots, and systematically categorised the factors that influence the off-channel water demand, as well as the methods used for its prediction. Key factors affecting off-channel water demand in most catchments include population, water pricing, precipitation, and air temperature. Machine learning algorithms have emerged as a prominent tool for predicting off-channel water demand, often used alongside regression analysis to assess the influence of multiple factors. The quota index method remains widely applied in practical water resource management. Additionally, hybrid approaches, combining time series analysis with other methods, address limitations in standalone models and enhance prediction accuracy. Advances in remote sensing, geospatial big data, artificial intelligence, and machine learning algorithms have significantly improved the accuracy of off-channel water demand predictions, particularly at smaller scales. Future research should focus on enhancing the validation of prediction models and ensuring the robust integration of historical data to improve modeling reliability for off-channel water demand and consumption.https://www.ggpsxb.com/jgpxxben/ch/reader/view_abstract.aspx?file_no=20250101&flag=1water demand predictioncite spaceinfluencing factorsmachine learning |
| spellingShingle | HE Yanhu XU Xiaodi A review of the determinants and prediction methods for off-channel water demand Guan'gai paishui xuebao water demand prediction cite space influencing factors machine learning |
| title | A review of the determinants and prediction methods for off-channel water demand |
| title_full | A review of the determinants and prediction methods for off-channel water demand |
| title_fullStr | A review of the determinants and prediction methods for off-channel water demand |
| title_full_unstemmed | A review of the determinants and prediction methods for off-channel water demand |
| title_short | A review of the determinants and prediction methods for off-channel water demand |
| title_sort | review of the determinants and prediction methods for off channel water demand |
| topic | water demand prediction cite space influencing factors machine learning |
| url | https://www.ggpsxb.com/jgpxxben/ch/reader/view_abstract.aspx?file_no=20250101&flag=1 |
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