Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region

Temitope Seun Oluwadare (), Marina Pannunzio Ribeiro, Dongmei Chen (), Masoud Babadi Ataabadi, Saba Hosseini Tabesh and Abiodun Esau Daomi
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Temitope Seun Oluwadare: Geographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Marina Pannunzio Ribeiro: Geographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Dongmei Chen: Geographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Masoud Babadi Ataabadi: Geographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Saba Hosseini Tabesh: Geographic Information and Spatial Analysis Laboratory, Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Abiodun Esau Daomi: National Space Research and Development Agency, Centre for Geodesy and Geodynamics, Toro 740103, Nigeria

Land, 2025, vol. 14, issue 5, 1-24

Abstract: 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.

Keywords: flood spatial modeling; flood susceptibility mapping; machine learning; natural hazards; flood prediction (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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