Application,Challenges,and Prospect of Machine Learning in Dam Seepage Prediction
The adverse effects of seepage will increase the dam failure risk, and applying machine learning to accurate seepage prediction is crucial to dam safety and stability.This paper reviews the application, challenges, and solutions of machine learning in dam seepage prediction.Machine learning can not...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2024-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.04.001 |
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| author | WANG Wei LIAO Jielin ZHU Shaokun |
| author_facet | WANG Wei LIAO Jielin ZHU Shaokun |
| author_sort | WANG Wei |
| collection | DOAJ |
| description | The adverse effects of seepage will increase the dam failure risk, and applying machine learning to accurate seepage prediction is crucial to dam safety and stability.This paper reviews the application, challenges, and solutions of machine learning in dam seepage prediction.Machine learning can not only predict the seepage behavior of dams but also identify some key parameters such as dam permeability coefficient and groundwater level in seepage prediction.Artificial neural networks, support vector machines, and decision trees have been widely employed in seepage prediction of dams.Integrated algorithms greatly improve prediction accuracy by integrating the advantages of multiple algorithms.Machine learning models still have many shortcomings in data quantity and quality, model interpretability, complexity, scalability, deployment, and implementation.Future research directions include developing advanced machine learning algorithms, creating physics data dual-drive models and interpretable models, and enhancing experimental testing and validation.The relevant achievements can provide references for studying dam seepage prediction based on machine learning models. |
| format | Article |
| id | doaj-art-f84aee5bd3014bf0b0bee503830197f6 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-f84aee5bd3014bf0b0bee503830197f62025-01-15T03:00:50ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-01-014555309317Application,Challenges,and Prospect of Machine Learning in Dam Seepage PredictionWANG WeiLIAO JielinZHU ShaokunThe adverse effects of seepage will increase the dam failure risk, and applying machine learning to accurate seepage prediction is crucial to dam safety and stability.This paper reviews the application, challenges, and solutions of machine learning in dam seepage prediction.Machine learning can not only predict the seepage behavior of dams but also identify some key parameters such as dam permeability coefficient and groundwater level in seepage prediction.Artificial neural networks, support vector machines, and decision trees have been widely employed in seepage prediction of dams.Integrated algorithms greatly improve prediction accuracy by integrating the advantages of multiple algorithms.Machine learning models still have many shortcomings in data quantity and quality, model interpretability, complexity, scalability, deployment, and implementation.Future research directions include developing advanced machine learning algorithms, creating physics data dual-drive models and interpretable models, and enhancing experimental testing and validation.The relevant achievements can provide references for studying dam seepage prediction based on machine learning models.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.04.001damseepage predictionmachine learningparameter identification |
| spellingShingle | WANG Wei LIAO Jielin ZHU Shaokun Application,Challenges,and Prospect of Machine Learning in Dam Seepage Prediction Renmin Zhujiang dam seepage prediction machine learning parameter identification |
| title | Application,Challenges,and Prospect of Machine Learning in Dam Seepage Prediction |
| title_full | Application,Challenges,and Prospect of Machine Learning in Dam Seepage Prediction |
| title_fullStr | Application,Challenges,and Prospect of Machine Learning in Dam Seepage Prediction |
| title_full_unstemmed | Application,Challenges,and Prospect of Machine Learning in Dam Seepage Prediction |
| title_short | Application,Challenges,and Prospect of Machine Learning in Dam Seepage Prediction |
| title_sort | application challenges and prospect of machine learning in dam seepage prediction |
| topic | dam seepage prediction machine learning parameter identification |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.04.001 |
| work_keys_str_mv | AT wangwei applicationchallengesandprospectofmachinelearningindamseepageprediction AT liaojielin applicationchallengesandprospectofmachinelearningindamseepageprediction AT zhushaokun applicationchallengesandprospectofmachinelearningindamseepageprediction |