Flood susceptibility mapping in El Niño Phenomenom integrating multitemporal radar analysis, GIS and machine learning techniques, Piura river basin, Peru - Badillo-Rivera, Edwin; Santiago, Ramiro; Poma, Ivan; Chavez, Teodosio; Arroyo-Paz, Antonio; Aucahuasi-Almidon, Andrea; Hinostroza, Edilberto; Segura, Eric; Eyzaguirre, Luz; León, Hairo; Virú-Vásquez, Paul (Frontiers in Environmental Science, 2025)
Floods represent the most frequent natural hazard, generating significant impacts on people as well as considerable economic and environmental losses worldwide. These events are particularly exacerbated by extreme climatic phenomena, such as the 2017 Coastal El Niño, the most intense in the past century, with the Piura region of Peru being the most affected. Flood susceptibility mapping (FSM) are essential for mitigating the negative impacts of floods through land-use planning, policy and plan formulation, and fostering community resilience for the sustainable occupation and use of floodplains. This study aimed to develop FSM in northern Peru, particularly in the Piura region, using a hybrid methodology integrating optical and radar remote sensing (RS), GIS, and machine learning (ML) techniques. Sentinel-1 data were used to map flood extent using the Normalized Difference Flood Index (NDFI), while flood susceptibility was modeled using ten topographic variables (derived from a DEM), the Normalized Difference Vegetation Index (NDVI), geology, and geomorphology; issues related to correlation and multicollinearity among topographic variables were addressed through Principal Component Analysis (PCA), selecting four principal components that explained 75.4% of the variance. Six FSMs were generated using Support Vector Machine (SVM) and Random Forest (RF), combined with different methods to estimate the quantitative relationship between variables and flood occurrence: Quantiles (q), Frequency Ratio (FR), and Weights of Evidence (WoE) (SVM-q, SVM-FR, SVM-WoE, RF-q, RF-FR, and RF-WoE). Model validation was performed using metrics such as the Area Under the ROC Curve (AUC), F1-score, and Accuracy, along with a cross-validation analysis. The results revealed that the RF ensemble model with WoE (RF-WoE) exhibited the best performance (AUC = 0.988 in training and >0.907 in validation), outperforming the SVM-based models; the SHAP analysis confirmed the significance of geology, geomorphology, and aspect in flood prediction. The resulting susceptibility maps identified the lower Piura River basin as the most vulnerable area, particularly during the 2017 Coastal El Niño event, due to morphological factors and inadequate land occupation. This study contributes to the field by demonstrating the effectiveness of a hybrid methodology that combines PCA, machine learning, and SHAP analysis, providing a more robust and interpretable approach to flood susceptibility mapping. Finally, the findings provide valuable inputs for local authorities, decision-makers, and organized communities to strengthen resilience, reduce vulnerability, and enhance preparedness against future floods, while also supporting the formulation of public policies and the integration of flood susceptibility into land-use planning for sustainable territorial management.