Author(s): Bryant E. Sandoval-A; Alejandro Mendoza; Eliseo Carrisoza
Linked Author(s):
Keywords: Floods; Geomorphometric indices; Machine learning; Naive Bayes; Risk management
Abstract: Flooding is a critical problem in Mexico, in specific states. This study uses a Naive Bayes model to zone flood susceptibility identification based on an inventory compiled from official flood reports between 2000 and 2022. Four geomorphometric indices—TWI, TPI, SPI, and PFI (proposed in this research)—were used, along with slope and roughness. Analysis shows that higher values of TWI and PFI provide the strongest positive signal for occurrence of floods, while SPI and slope reduce this propensity in their high ranges; TPI and roughness give strong signal in intermediate ranges. These patterns confirm the differentiated contribution of each predictor and support the validity of the model. The results allow the generation of susceptibility maps useful for risk management in the region based on geomorphic indices.
Year: 2026