Jianlan Luo
Research Subjects
Jianlan Luo has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Dongxin Bai
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
1 shared publication - Guangyin Lu
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
1 shared publication - Hongsheng He
Geophysical and Geochemical Survey Institute of Hunan Province, Changsha 410014, China
1 shared publication - Junwei Xu
Geophysical and Geochemical Survey Institute of Hunan Province, Changsha 410014, China
1 shared publication
Latest Publications
- Open Access
- 14 December 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (24) , 12836
Disaster Precursor Identification and Early Warning of the Lishanyuan Landslide Based on Association Rule Mining
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.
- 14 December 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (24) , 12836
Open AccessDisaster Precursor Identification and Early Warning of the Lishanyuan Landslide Based on Association Rule Mining
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.