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- Title
Disaster Precursor Identification and Early Warning of the Lishanyuan Landslide Based on Association Rule Mining.
- Authors
Xu, Junwei; Bai, Dongxin; He, Hongsheng; Luo, Jianlan; Lu, Guangyin
- 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.
- Subjects
HUNAN Sheng (China); ASSOCIATION rule mining; NATURAL disaster warning systems; LANDSLIDES; APRIORI algorithm; PARTICLE swarm optimization; DATA mining; RAINFALL
- Publication
Applied Sciences (2076-3417), 2022, Vol 12, Issue 24, p12836
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app122412836