Using Unsupervised Learning to Explore Landslides Driving Factors from Topographic and Hydrological Catchment Features

Landslides are a widespread geohazard with significant impacts on lives and economies worldwide. While past research has primarily emphasized creating inventories, and analysing spatial and temporal patterns, the objective of this study is to explore the relationship between landslides events taken place in different catchments using only topographical and physical attributes from the disasters’ areas. The aim is to improve the understanding of the occurrence and susceptibility of such events, as well as the possible similarities between the events and the catchments. To this end, multicollinearity and mutual information analysis were performed to identify both linear and nonlinear relationships between the variables, assisting on the identification of the most relevant driving factors to historical landslides in the study area. Furthermore, the events were grouped using 5 different unsupervised clustering techniques, KMeans, Mean Shift, DBSCAN, Hierarchical and Spectral Custering, to analyse the relationship between landslides taken place in different catchments and their underlying driving forces. Clustering evaluation metrics, i.e. Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used assess the performance of these algorithms. The results show that, for a preliminary study and providing insights on the relevance of driving factors and similarities between events, unsupervised learning proves to be an important tool. Nevertheless, to find more applicable and in-depth associations between extreme disasters and its driving factors, more robust machine learning techniques can and should be used.