Debris flow susceptibility mapping in Colorado Front Range, USA: A comparison of physics-based and data-driven approaches

Abstract

This study used an inventory of debris flow triggered by a storm event in Colorado Front Range as an example to compare the capability of data-driven and physics-based approaches for regional-scale debris flow susceptibility mapping (LSM). Nine debris flow contributing factors were collected for the present study based on the availability of geophysical data in the study area. These contributing factors represent hillslope geometries, surface hydrology, and soil conditions. For the physics-based approach, the infinite slope model was used to directly determine the debris flow susceptibility for the study area by calculating the factor of safety (FS) based on parameters derived from geophysical data. For the data-driven approach, an artificial neural network (ANN) was developed to predict debris flow susceptibility for the study area by learning relationships from the contributing factors using the debris flow inventory. The results showed that both physics-based and data-driven models predicted debris flow susceptibility in the study area with relatively high accuracy; the data-driven approach outperformed the physics-based approach as it could extract complex features which the physics-based approach did not consider.