Zoning debris flow risk in Than Uyen region, Lai Chau province through machine learning models

Abstract

Debris flows are one of the most common and hazardous types of natural disasters in mountainous regions of Vietnam, particularly in the Than Uyen region, Lai Chau Province. This region is characterized by steep terrain, high rainfall, degraded vegetation cover, and weak soil structure, all of which contribute to the frequent triggering of debris flows. This study aims to assess debris flow susceptibility using Linear Regression and the Random Forest (RF) model, integrating remote sensing data, GIS, and field surveys. A total of ten input variables were selected to train the models using 422 sample points. The RF model demonstrated superior performance, achieving an accuracy of 86% and an AUC of 0.91, compared to 74% accuracy and an R² of 0.65 from the regression model. Given its higher reliability and practical relevance, the RF model was used to generate a debris flow susceptibility map for the entire region. Field validation conducted in Khoen On, Ta Mung, and Muong Cang communes confirmed a strong spatial agreement between high-susceptibility zones and actual debris flow events. The study recommends implementing early warning systems, continuous monitoring, and resettlement planning in high- susceptibility areas. It also emphasizes integrating the model outputs into spatial planning frameworks to enhance climate change adaptation and disaster risk reduction in mountainous areas