Modelling of shallow landslides with Machine Learning algorithms

Abstract

This paper introduces three machine learning (ML) algorithms, the 'ensemble' Random Forest (RF), the 'ensemble' Gradient Boosted Regression Tree (GBRT) and the MultiLayer Perceptron neural network (MLP) and applies them to the spatial modelling of shallow landslides near Kvam in Norway. In the development of the ML models, a total of 11 significant landslide controlling factors were selected. The controlling factors relate to the geomorphology, geology, geo-environment and anthropogenic effects: slope angle, aspect, plan curvature, profile curvature, flow accumulation, flow direction, distance to rivers, total water content, saturation, rainfall and distance to roads. It is observed that slope angle was the most significant controlling factor in the ML analyses. The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic (ROC) analysis. The results show that the 'ensemble' GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides, with a 95% probability of landslide detection and 87% prediction efficiency.

Modelling of shallow landslides with Machine Learning algorithms