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.

中文翻译：

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使用聚类分析进行滑坡空间预测

时间聚类是滑坡发生的本质，因此在数据驱动的滑坡空间预测（即敏感性评估）中应考虑它。然而，如何确定滑坡时间簇以及如何整合来自不同滑坡时间簇的磁化率图仍然存在问题。本文提出了考虑滑坡时间聚类的滑坡空间预测模型的总体框架。该新颖的框架首先根据时空聚类分析识别的每个滑坡时间聚类分别评估滑坡敏感性，然后通过加权平均整合单独的评估。在案例研究中，该总体框架采用叠加网络滑坡敏感性评估方法实现，并用于三明市和汶川地震区的滑坡空间预测。结果表明，所提出的框架优于不考虑滑坡时间聚类的传统磁化率模型，并且基于所有滑坡时间聚类的磁化率模型的集成将提高滑坡空间预测的性能，因为长期时空滑坡活动的知识水平被考虑。这种新颖的总体框架突出了在滑坡空间预测中考虑滑坡时间聚类的好处，并且可以为滑坡风险管理提供更好的支持。