Application of machine learning model in landslide susceptibility evaluation

Fuzhen LIU; Ling WANG; Dongsheng XIAO

doi:10.16031/j.cnki.issn.1003-8035.2021.06-12

Volume 32 Issue 6

Dec. 2021

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The Chinese Journal of Geological Hazard and Control > 2021 > 32(6): 98-106. > DOI: 10.16031/j.cnki.issn.1003-8035.2021.06-12

Fuzhen LIU, Ling WANG, Dongsheng XIAO. Application of machine learning model in landslide susceptibility evaluation[J]. The Chinese Journal of Geological Hazard and Control, 2021, 32(6): 98-106. DOI: 10.16031/j.cnki.issn.1003-8035.2021.06-12

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Application of machine learning model in landslide susceptibility evaluation

School of Civil Engineering and Surveying, Southwest Petroleum University, Chengdu, Sichuan　610500, China

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Received Date: November 02, 2020
Revised Date: May 17, 2021
Available Online: December 19, 2021

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Abstract

Machine learning faces two difficulties in the evaluation of landslide susceptibility. One is the objective quantification of evaluation index, and the other is the selection of training sample-0.5pts. For that reason, the frequency ratio method is used to achieve the objective quantification of evaluation index, and the k-means clustering algorithm is used to achieve the selection of non-landslide sample data. The results show that based on the premise that the k-means clustering algorithm selects non-landslides, the training accuracy of the neural network has increased from 73% to 97%, and the training accuracy of the support vector machine has increased from 75% to 96%. Based on the GIS platform, the susceptibility index calculated by the neural network and support vector machine model is divided into five regions according to the natural break point method. The statistical results of the overlay analysis of the zoning map and the historical disaster points show that the evaluation result of the neural network is better than the support vector machine model in the global scope, and the global accuracy is 76% and 74%, respectively. The research results can provide reference for disaster prevention and mitigation in Nanjiang County of China.
- landslide susceptibility,
- frequency ratio quantization,
- k-means clustering,
- neural networks,
- support Vector Machines

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[1]	邱海军. 区域滑坡崩塌地质灾害特征分析及其易发性和危险性评价研究: 以宁强县为例[D]. 西安: 西北大学, 2012. QIU Haijun. Study on the regional landslide characteristic analysis and hazard assessment: A case study of Ningqiang County[D]. Xi'an: Northwest University, 2012. (in Chinese with English abstract)
[2]	唐亚明, 张茂省, 李林, 等. 滑坡易发性危险性风险评价例析[J]. 水文地质工程地质,2011,38(2):125 − 129. [TANG Yaming, ZHANG Maosheng, LI Lin, et al. Discrimination to the landslide susceptibility, hazard and risk assessment[J]. Hydrogeology & Engineering Geology,2011,38(2):125 − 129. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-3665.2011.02.022
[3]	杨秀梅. 基于GIS的地质灾害危险性评价[D]. 兰州: 兰州大学, 2008. YANG Xiumei. Geological hazard risk assessment based on GIS[D]. Lanzhou: Lanzhou University, 2008. (in Chinese with English abstract)
[4]	胡芹龙, 王运生. 基于GIS的川西地貌过渡带滑坡灾害易发性评价[J]. 成都理工大学学报(自然科学版),2018,45(6):746 − 753. [HU Qinlong, WANG Yunsheng. The susceptibility assessment of geological disasters in geomorphic transition zone based on GIS, western Sichuan, China[J]. Journal of Chengdu University of Technology (Science & Technology Edition),2018,45(6):746 − 753. (in Chinese with English abstract)
[5]	郭长宝, 唐杰, 吴瑞安, 等. 基于证据权模型的川藏铁路加查: 朗县段滑坡易发性评价[J]. 山地学报,2019,37(2):240 − 251. [GUO Changbao, TANG Jie, WU Ruian, et al. Landslide susceptibility assessment based on WOE model along Jiacha—Langxian County section of Sichuan—Tibet railway, China[J]. Mountain Research,2019,37(2):240 − 251. (in Chinese with English abstract)
[6]	兰恒星, 伍法权, 王思敬. 基于GIS的滑坡CF多元回归模型及其应用[J]. 山地学报,2002,20(6):732 − 737. [LAN Hengxing, WU Faquan, WANG Sijing. GIS based landslide CF multi-variable regression model and its application[J]. Journal of Mountain Research,2002,20(6):732 − 737. (in Chinese with English abstract) DOI: 10.3969/j.issn.1008-2786.2002.06.015
[7]	郭子正, 殷坤龙, 黄发明, 等. 基于滑坡分类和加权频率比模型的滑坡易发性评价[J]. 岩石力学与工程学报,2019,38(2):287 − 300. [GUO Zizheng, YIN Kunlong, HUANG Faming, et al. Evaluation of landslide susceptibility based on landslide classification and weighted frequency ratio model[J]. Chinese Journal of Rock Mechanics and Engineering,2019,38(2):287 − 300. (in Chinese with English abstract)
[8]	许冲, 戴福初, 姚鑫, 等. GIS支持下基于层次分析法的汶川地震区滑坡易发性评价[J]. 岩石力学与工程学报,2009,28(增刊 2):3978 − 3985. [XU Chong, DAI Fuchu, YAO Xin, et al. Gis-based landslide susceptibility assessment using analytical hierarchy process in Wenchuan earthquake region[J]. Chinese Journal of Rock Mechanics and Engineering,2009,28(Sup 2):3978 − 3985. (in Chinese with English abstract)
[9]	苏强. 基于DEM的黄土滑坡危险性评价研究[D]. 北京: 中国地质大学(北京), 2006. SU Qiang. Research on loess landslide hazard zonation based on DEM[D]. Beijing: China University of Geosciences, 2006. (in Chinese with English abstract)
[10]	李嘉良, 马东辉, 王威. 基于证据理论和熵权灰色关联的潜在地震滑坡危险性评价[J]. 中南大学学报(自然科学版),2016,47(5):1730 − 1736. [LI Jialiang, MA Donghui, WANG Wei. Assessment of potential seismic landslide hazard based on evidence theory and entropy weight grey incidence[J]. Journal of Central South University (Science and Technology),2016,47(5):1730 − 1736. (in Chinese with English abstract) DOI: 10.11817/j.issn.1672-7207.2016.05.036
[11]	牛瑞卿, 彭令, 叶润青, 等. 基于粗糙集的支持向量机滑坡易发性评价[J]. 吉林大学学报(地球科学版),2012,42(2):430 − 439. [NIU Ruiqing, PENG Ling, YE Runqing, et al. Landslide susceptibility assessment based on rough sets and support vector machine[J]. Journal of Jilin University (Earth Science Edition),2012,42(2):430 − 439. (in Chinese with English abstract)
[12]	王森, 许强, 罗博宇, 等. 基于分形理论的南江县滑坡敏感性分析与易发性评价[J]. 水文地质工程地质,2017,44(3):119 − 126. [WANG Sen, XU Qiang, LUO Boyu, et al. Vulnerability analysis and susceptibility evaluation of landslides based on fractal theory in Nanjiang County[J]. Hydrogeology & Engineering Geology,2017,44(3):119 − 126. (in Chinese with English abstract)
[13]	何静. 基于机器学习的滑坡灾害空间预测及风险评估[D]. 成都: 电子科技大学, 2019. HE Jing. Spatial prediction and risk assessment of landslides based on machine learning[D]. Chengdu: University of Electronic Science and Technology of China, 2019. (in Chinese with English abstract)
[14]	黄发明, 殷坤龙, 蒋水华, 等. 基于聚类分析和支持向量机的滑坡易发性评价[J]. 岩石力学与工程学报,2018,37(1):156 − 167. [HUANG Faming, YIN Kunlong, JIANG Shuihua, et al. Landslide susceptibility assessment based on clustering analysis and support vector machine[J]. Chinese Journal of Rock Mechanics and Engineering,2018,37(1):156 − 167. (in Chinese with English abstract)
[15]	王敞, 陈增强, 袁著祉. 基于遗传算法的K均值聚类分析[J]. 计算机科学,2003,30(2):163 − 164. [WANG Chang, CHEN Zengqiang, YUAN Zhuzhi. K-means clustering based on genetic algorithm[J]. Computer Science,2003,30(2):163 − 164. (in Chinese with English abstract) DOI: 10.3969/j.issn.1002-137X.2003.02.044
[16]	陈玉萍, 袁志强, 周博, 等. 遗传算法优化BP网络在滑坡灾害预测中的应用研究[J]. 水文地质工程地质,2012,39(1):114 − 119. [CHEN Yuping, YUAN Zhiqiang, ZHOU Bo, et al. Application of back propagation neural networks with optimization of genetic algorithms to landslide hazard prediction[J]. Hydrogeology & Engineering Geology,2012,39(1):114 − 119. (in Chinese with English abstract)
[17]	武雪玲, 沈少青, 牛瑞卿. GIS支持下应用PSO-SVM模型预测滑坡易发性[J]. 武汉大学学报(信息科学版),2016,41(5):665 − 671. [WU Xueling, SHEN Shaoqing, NIU Ruiqing. Landslide susceptibility prediction using GIS and PSO-SVM[J]. Geomatics and Information Science of Wuhan University,2016,41(5):665 − 671. (in Chinese with English abstract)
[18]	张俊, 殷坤龙, 王佳佳, 等. 三峡库区万州区滑坡灾害易发性评价研究[J]. 岩石力学与工程学报,2016,35(2):284 − 296. [ZHANG Jun, YIN Kunlong, WANG Jiajia, et al. Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering,2016,35(2):284 − 296. (in Chinese with English abstract)

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