Landslide Risk Assessment Using Granular Fuzzy Rule-Based Modeling: A Case Study on Earthquake-Triggered Landslides

Abstract

Landslides are one type of destructive and recurring natural calamities in the mountainous regions. The landslide occurrences often lead to immense damage to local infrastructure and loss of land, human lives and livestock. Data-driven risk assessment of landslide risk plays a crucial role in preventing the incoming landslide occurrences and recurrences. In this research, we developed a human-centric framework using information-granules to perform risk assessment of a group of landslides. First, the density-based spatial clustering of applications with noise (DBSCAN) has been selected as the backbone unsupervised learning method to subclusters for landslide risk indication. The clustering outcomes are visualized via t-distributed stochastic neighbor embedding (t-SNE) in the 2-D embedding space. Second, the prototype points within the subclusters produced by DBSCAN are computed for granular construction. Third, interval-based information-granules are constructed and measured via coverage, specificity and area under the coverage-specificity curve (AUC). Last, with the optimal information-granules constructed, two risk measures namely Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) are computed to interpret the rule-based information-granules with respect to the key attributes. Comparative experiments have also been performed against other benchmarking clustering approaches. Computational results indicate that the information-granules constructed from DBSCAN subclusters offered enhanced performance in reveal meaningful information-granules and provide promising results. The proposed approach can capture the main essence of landslide pattern with higher interpretability and help to reduce the computing overhead.