*Article* **Disaster Precursor Identification and Early Warning of the Lishanyuan Landslide Based on Association Rule Mining**

**Junwei Xu 1,2,3, Dongxin Bai 2,3,\*, Hongsheng He 1, Jianlan Luo <sup>1</sup> and Guangyin Lu 2,3**


**Abstract:** It is the core prerequisite of landslide warning to mine short-term deformation patterns and extract disaster precursors from real-time and multi-source monitoring data. This study used the sliding window method and gray relation analysis to obtain features from multi-source, real-time monitoring data of the Lishanyuan landslide in Hunan Province, China. Then, the k-means algorithm with particle swarm optimization was used for clustering. Finally, the Apriori algorithm is used to mine strong association rules between the high-speed deformation process and rainfall features of this landslide to obtain short-term deformation patterns and precursors of the disaster. The data mining results show that the landslide has a high-speed deformation probability of more than 80% when rainfall occurs within 24 h and the cumulative rainfall is greater than 130.60 mm within 7 days. It is of great significance to extract the short-term deformation pattern of landslides by data mining technology to improve the accuracy and reliability of early warning.

**Keywords:** disaster precursor identification; early warning; association rule mining; particle swarm optimization; k-means clustering; Apriori algorithm; gray relation analysis
