Application of Deterministic Method for Landslide Susceptibility with Deep Learning

Abstract

A method for estimating landslide susceptibility based on the analytic hierarchy process (AHP) was developed in 2017 as a deterministic method. The objective of this study is to verify the reliability of the proposed method by applying deep learning to improve the applicability of the method. The AHP-based deterministic method comprises eight factors: fines content, soil thickness, porosity, elastic modulus, shear strength, hydraulic conductivity, saturation, and water content. After dividing the testing area into 1 m square grids, eight factors were derived through field and laboratory experiments. The factor of safety was calculated based on the Mohr-Coulomb failure theory. Finally, the input and output values of deep learning were obtained. Bayesian regularization was applied among gradient descents to improve the learning efficiency when applying machine learning. The actual and predicted factors of safety were compared, and they showed excellent reliability in both the training and test phases. This study demonstrates that the AHP-based deterministic method with deep learning is valuable for determining landslide risk areas.