Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region
Floods are among the most destructive natural hazards globally, necessitating the identification of flood-prone areas for effective disaster risk management and sustainable urban development. Advanced data-driven techniques, including machine learning (ML), are increasingly used to map and mitigate...
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MDPI AG
2025-05-01
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| author | Temitope Seun Oluwadare Marina Pannunzio Ribeiro Dongmei Chen Masoud Babadi Ataabadi Saba Hosseini Tabesh Abiodun Esau Daomi |
| author_facet | Temitope Seun Oluwadare Marina Pannunzio Ribeiro Dongmei Chen Masoud Babadi Ataabadi Saba Hosseini Tabesh Abiodun Esau Daomi |
| author_sort | Temitope Seun Oluwadare |
| collection | DOAJ |
| description | Floods are among the most destructive natural hazards globally, necessitating the identification of flood-prone areas for effective disaster risk management and sustainable urban development. Advanced data-driven techniques, including machine learning (ML), are increasingly used to map and mitigate flood risks. However, ML applications for flood risk assessment remain limited in Sorocaba, a sub-region of São Paulo, Brazil. This study employs four ML algorithms—differential evolution (DE), naïve Bayes (NB), random forest (RF), and support vector machines (SVMs)—to develop flood susceptibility models using 16 predictor variables. Key categorical factors influencing flood susceptibility included topographical, anthropogenic, and hydrometeorological, particularly elevation, slope, NDVI, NDWI, and distance to roads. Performance metrics (F1-score and AUC) showed strong results, ranging from 0.94 to 1.00, with the DE and RF models excelling in training, testing, and external datasets. The study highlights model transferability, demonstrating applicability to other regions. Findings reveal that 41% to 50% of Sorocaba is at high flood risk. The explainable artificial intelligence technique Shapley additive explanations (SHAP) further identified moisture and the stream power index (SPI) as significant factors influencing flood occurrence. The study underscores the ML-based model’s potential in highlighting flood-vulnerable areas and guiding flood mitigation strategies, land-use planning, and infrastructure resilience. |
| format | Article |
| id | doaj-art-e0969bd25599436db90fbda56612b8a0 |
| institution | OA Journals |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Land |
| spelling | doaj-art-e0969bd25599436db90fbda56612b8a02025-08-20T02:34:01ZengMDPI AGLand2073-445X2025-05-0114598510.3390/land14050985Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-RegionTemitope Seun Oluwadare0Marina Pannunzio Ribeiro1Dongmei Chen2Masoud Babadi Ataabadi3Saba Hosseini Tabesh4Abiodun Esau Daomi5Geographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, CanadaGeographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, CanadaGeographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, CanadaGeographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, CanadaGeographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, CanadaNational Space Research and Development Agency, Centre for Geodesy and Geodynamics, Toro 740103, NigeriaFloods are among the most destructive natural hazards globally, necessitating the identification of flood-prone areas for effective disaster risk management and sustainable urban development. Advanced data-driven techniques, including machine learning (ML), are increasingly used to map and mitigate flood risks. However, ML applications for flood risk assessment remain limited in Sorocaba, a sub-region of São Paulo, Brazil. This study employs four ML algorithms—differential evolution (DE), naïve Bayes (NB), random forest (RF), and support vector machines (SVMs)—to develop flood susceptibility models using 16 predictor variables. Key categorical factors influencing flood susceptibility included topographical, anthropogenic, and hydrometeorological, particularly elevation, slope, NDVI, NDWI, and distance to roads. Performance metrics (F1-score and AUC) showed strong results, ranging from 0.94 to 1.00, with the DE and RF models excelling in training, testing, and external datasets. The study highlights model transferability, demonstrating applicability to other regions. Findings reveal that 41% to 50% of Sorocaba is at high flood risk. The explainable artificial intelligence technique Shapley additive explanations (SHAP) further identified moisture and the stream power index (SPI) as significant factors influencing flood occurrence. The study underscores the ML-based model’s potential in highlighting flood-vulnerable areas and guiding flood mitigation strategies, land-use planning, and infrastructure resilience.https://www.mdpi.com/2073-445X/14/5/985flood spatial modelingflood susceptibility mappingmachine learningnatural hazardsflood prediction |
| spellingShingle | Temitope Seun Oluwadare Marina Pannunzio Ribeiro Dongmei Chen Masoud Babadi Ataabadi Saba Hosseini Tabesh Abiodun Esau Daomi Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region Land flood spatial modeling flood susceptibility mapping machine learning natural hazards flood prediction |
| title | Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region |
| title_full | Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region |
| title_fullStr | Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region |
| title_full_unstemmed | Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region |
| title_short | Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region |
| title_sort | applying machine learning algorithms for spatial modeling of flood susceptibility prediction over sao paulo sub region |
| topic | flood spatial modeling flood susceptibility mapping machine learning natural hazards flood prediction |
| url | https://www.mdpi.com/2073-445X/14/5/985 |
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