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.
Citation: 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[J]. AIMS Environmental Science, 2025, 12(1): 72-105. doi: 10.3934/environsci.2025004
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.
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Figures(2) / Tables(4)
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[J]. AIMS Environmental Science, 2025, 12(1): 72-105. doi: 10.3934/environsci.2025004
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