Landslide spatial prediction using cluster analysis

Abstract

Temporal clustering is an intrinsic nature of landslide occurrences, therefore it should be considered in data-driven landslide spatial prediction (i.e., susceptibility assessment). However, it remains problematic regarding how to determine landslide temporal clusters and how to integrate susceptibility maps derived from different landslide temporal clusters. In this paper, a general framework of landslide spatial prediction model considering the temporal clustering of landslides is proposed. This novel framework first assesses landslide susceptibility separately based on each landslide temporal cluster identified by spatiotemporal clustering analysis and then integrates separate assessments by weighted averaging. In a case study, this general framework is implemented using the stacking network landslide susceptibility assessment method and used in the landslide spatial prediction of the Sanming City and Wenchuan seismic areas. The results show that the proposed framework outperformed traditional susceptibility models that do not consider landslide temporal clustering, and the integration of susceptibility models based on all landslide temporal clusters will promote the performance of landslide spatial prediction because levels of knowledge in long-term spatiotemporal landslide activities are considered. This novel general framework highlights the benefit of considering landslide temporal clustering in landslide spatial prediction and can provide better support for landslide risk management.