Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment
Floods have been identified as one of the world's most common and widely distributed natural disasters over the last few decades. Floods' negative impacts could be significantly reduced if accurately predicted or forecasted in advance. Apart from large-scale spatiotemporal data and greater...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-01-01
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| Series: | AIMS Environmental Science |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/environsci.2025004 |
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| author | Vijendra Kumar Kul Vaibhav Sharma Nikunj K. Mangukiya Deepak Kumar Tiwari Preeti Vijay Ramkar Upaka Rathnayake |
| author_facet | Vijendra Kumar Kul Vaibhav Sharma Nikunj K. Mangukiya Deepak Kumar Tiwari Preeti Vijay Ramkar Upaka Rathnayake |
| author_sort | Vijendra Kumar |
| collection | DOAJ |
| description | Floods have been identified as one of the world's most common and widely distributed natural disasters over the last few decades. Floods' negative impacts could be significantly reduced if accurately predicted or forecasted in advance. Apart from large-scale spatiotemporal data and greater attention to data from the Internet of Things, the worldwide volume of digital data is increasing. Artificial intelligence plays a vital role in analyzing and developing the corresponding flood mitigation plan, flood prediction, or forecast. Machine learning (ML)-based models have recently received much attention due to their self-learning capabilities from data without incorporating any complex physical processes. This study provides a comprehensive review of ML approaches used in flood prediction, forecasting, and classification tasks, serving as a guide for future challenges. The importance and challenges of applying these techniques to flood prediction are discussed. Finally, recommendations and future directions of ML models in flood analysis are presented. |
| format | Article |
| id | doaj-art-c40704ce2d334070869bc4dda63469f4 |
| institution | OA Journals |
| issn | 2372-0352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Environmental Science |
| spelling | doaj-art-c40704ce2d334070869bc4dda63469f42025-08-20T02:34:12ZengAIMS PressAIMS Environmental Science2372-03522025-01-011217210510.3934/environsci.2025004Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environmentVijendra Kumar0Kul Vaibhav Sharma1Nikunj K. Mangukiya2Deepak Kumar Tiwari3Preeti Vijay Ramkar4Upaka Rathnayake5Department of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Kothrud, Pune, Maharashtra, 411038, IndiaDepartment of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Kothrud, Pune, Maharashtra, 411038, IndiaDepartment of Hydrology, Indian Institute of Technology Roorkee, 247667, Uttarakhand, IndiaDepartment of Civil Engineering, GLA University, Mathura, UP, 281406 IndiaDepartment of Civil Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, Maharashtra, 411018, IndiaDepartment of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, Sligo F91 YW50, IrelandFloods have been identified as one of the world's most common and widely distributed natural disasters over the last few decades. Floods' negative impacts could be significantly reduced if accurately predicted or forecasted in advance. Apart from large-scale spatiotemporal data and greater attention to data from the Internet of Things, the worldwide volume of digital data is increasing. Artificial intelligence plays a vital role in analyzing and developing the corresponding flood mitigation plan, flood prediction, or forecast. Machine learning (ML)-based models have recently received much attention due to their self-learning capabilities from data without incorporating any complex physical processes. This study provides a comprehensive review of ML approaches used in flood prediction, forecasting, and classification tasks, serving as a guide for future challenges. The importance and challenges of applying these techniques to flood prediction are discussed. Finally, recommendations and future directions of ML models in flood analysis are presented.https://www.aimspress.com/article/doi/10.3934/environsci.2025004machine learningwater resourcesfloodartificial intelligencenatural hazards & disasters |
| spellingShingle | Vijendra Kumar Kul Vaibhav Sharma Nikunj K. Mangukiya Deepak Kumar Tiwari Preeti Vijay Ramkar Upaka Rathnayake Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment AIMS Environmental Science machine learning water resources flood artificial intelligence natural hazards & disasters |
| title | Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment |
| title_full | Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment |
| title_fullStr | Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment |
| title_full_unstemmed | Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment |
| title_short | Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment |
| title_sort | machine learning applications in flood forecasting and predictions challenges and way out in the perspective of changing environment |
| topic | machine learning water resources flood artificial intelligence natural hazards & disasters |
| url | https://www.aimspress.com/article/doi/10.3934/environsci.2025004 |
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