Academic Journal
Disaster Precursor Identification and Early Warning of the Lishanyuan Landslide Based on Association Rule Mining
| Title: | Disaster Precursor Identification and Early Warning of the Lishanyuan Landslide Based on Association Rule Mining |
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| Authors: | Junwei Xu, Dongxin Bai, Hongsheng He, Jianlan Luo, Guangyin Lu |
| Source: | Applied Sciences, Vol 12, Iss 24, p 12836 (2022) |
| Publisher Information: | MDPI AG, 2022. |
| Publication Year: | 2022 |
| Collection: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Subject Terms: | disaster precursor identification, early warning, association rule mining, particle swarm optimization, k-means clustering, Apriori algorithm, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/12/24/12836; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app122412836 |
| Access URL: | https://doaj.org/article/ced657b6b3b14399b5c870dc3a84efb8 |
| Accession Number: | edsdoj.657b6b3b14399b5c870dc3a84efb8 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20763417 |
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| DOI: | 10.3390/app122412836 |
| Published in: | Applied Sciences |
| Language: | English |