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Unveiling global flood hotspots: Optimized machine learning techniques for enhanced flood susceptibility modeling
Study region: Worldwide Study focus: Floods are among the most catastrophic and dangerous natural calamities globally, causing irreparable damage to human lives and property, and environmental degradation. Flood susceptibility mapping is a cost-effective tool to mitigate and manage the impacts of flood occurrences, but high accuracy in mapping is important to support management strategies. This study assessed the efficiency of three machine learning approaches, including support vector regression (SVR) and its optimized versions through combination with grey wolf optimizer (GWO) and whale optimization algorithm (WOA), in generating accurate flood susceptibility maps at a global scale. Data from 6682 historical flood events, covering eight flood-related geo-environmental factors were used to generate the maps. All maps produced were evaluated based on root mean square error (RMSE), mean squared error (MSE), standard deviation, and area under the receiver operating characteristic curve (AUC). New hydrological insights for the region: This study reveals that the SVR-GWO model has the best performance in predicting flood-prone areas worldwide based on AUC, RMSE and MSE. The findings indicate that approximately 17.14 % of global land area is highly and very highly susceptible to flood occurrence. Flood hot-spot countries were the United States of America (7.75 %), Indonesia (6.33 %), India (6.31 %), Brazil (5.33 %) and Nigeria (4.08 %). Countries with the lowest probability of flood occurrence were the Russian Federation, Canada, Greenland, the United States of America and China. Incorporating additional satellite-based environmental data could further enhance the model's accuracy. Furthermore, the approach sets a foundation for future research in tailoring flood prediction models to regional scales, addressing the diverse challenges posed by different geographic and environmental settings.
Unveiling global flood hotspots: Optimized machine learning techniques for enhanced flood susceptibility modeling
Study region: Worldwide Study focus: Floods are among the most catastrophic and dangerous natural calamities globally, causing irreparable damage to human lives and property, and environmental degradation. Flood susceptibility mapping is a cost-effective tool to mitigate and manage the impacts of flood occurrences, but high accuracy in mapping is important to support management strategies. This study assessed the efficiency of three machine learning approaches, including support vector regression (SVR) and its optimized versions through combination with grey wolf optimizer (GWO) and whale optimization algorithm (WOA), in generating accurate flood susceptibility maps at a global scale. Data from 6682 historical flood events, covering eight flood-related geo-environmental factors were used to generate the maps. All maps produced were evaluated based on root mean square error (RMSE), mean squared error (MSE), standard deviation, and area under the receiver operating characteristic curve (AUC). New hydrological insights for the region: This study reveals that the SVR-GWO model has the best performance in predicting flood-prone areas worldwide based on AUC, RMSE and MSE. The findings indicate that approximately 17.14 % of global land area is highly and very highly susceptible to flood occurrence. Flood hot-spot countries were the United States of America (7.75 %), Indonesia (6.33 %), India (6.31 %), Brazil (5.33 %) and Nigeria (4.08 %). Countries with the lowest probability of flood occurrence were the Russian Federation, Canada, Greenland, the United States of America and China. Incorporating additional satellite-based environmental data could further enhance the model's accuracy. Furthermore, the approach sets a foundation for future research in tailoring flood prediction models to regional scales, addressing the diverse challenges posed by different geographic and environmental settings.
Unveiling global flood hotspots: Optimized machine learning techniques for enhanced flood susceptibility modeling
Mahdi Panahi (author) / Khabat Khosravi (author) / Fatemeh Rezaie (author) / Zahra Kalantari (author) / Jeong-A. Lee (author)
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
| Elsevier | 2025
Flood Susceptibility Modeling Using Forest-Based Regression
| Springer Verlag | 2022