Identifying Landslide Hotspots Using Unsupervised Clustering: A Case Study

Abstract

Landslides pose significant threats to life, property, and infrastructure. This study explores applying unsupervised learning techniques to identify and understand landslide-prone areas. We analyzed topographic data by employing K-Means, Hierarchical Clustering, Spectral Clustering, Mean Shift Clustering, and DBSCAN to uncover hidden patterns in landslide occurrence. Evaluation metrics, including the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used to assess the performance of these algorithms. Hierarchical Clustering achieved the highest Silhouette Score of 0.635, indicating excellent cluster separation. However, Mean Shift Clustering outperformed the other methods with a superior Davies-Bouldin Index of 0.603 and the highest Calinski-Harabasz Index of 4121.75, demonstrating the best overall clustering performance. DBSCAN also performed well, with a Silhouette Score of 0.610 and 12 noise points identified. These findings contribute to a deeper understanding of landslide spatial distribution and can inform the development of effective early warning systems and mitigation strategies.