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

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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Bibliographic Details
Main Authors: Temitope Seun Oluwadare, Marina Pannunzio Ribeiro, Dongmei Chen, Masoud Babadi Ataabadi, Saba Hosseini Tabesh, Abiodun Esau Daomi
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Land
Subjects:
flood spatial modeling
flood susceptibility mapping
machine learning
natural hazards
flood prediction
Online Access:https://www.mdpi.com/2073-445X/14/5/985
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Summary: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.
ISSN:2073-445X

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