Evaluation on landslide susceptibility based on self-organizing feature map network and random forest model：A case study of Dayu County of Jiangxi Province

HE Shu; ABUDIKEYIMU XMSY; HU Meng; CHEN Kang

doi:10.16031/j.cnki.issn.1003-8035.2022.01-16

2022 Vol. 33, No. 1

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Article navigation > The Chinese Journal of Geological Hazard and Control > 2022 > 33(1): 132-140

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HE Shu, ABUDIKEYIMU XMSY, HU Meng, CHEN Kang. Evaluation on landslide susceptibility based on self-organizing feature map network and random forest model：A case study of Dayu County of Jiangxi Province[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(1): 132-140. doi: 10.16031/j.cnki.issn.1003-8035.2022.01-16

Citation:

HE Shu, ABUDIKEYIMU XMSY, HU Meng, CHEN Kang. Evaluation on landslide susceptibility based on self-organizing feature map network and random forest model：A case study of Dayu County of Jiangxi Province[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(1): 132-140. doi: 10.16031/j.cnki.issn.1003-8035.2022.01-16

Evaluation on landslide susceptibility based on self-organizing feature map network and random forest model：A case study of Dayu County of Jiangxi Province

1.
Jiangxi University of Science and Technology, School Resources Environmental Engineering, Ganzhou, Jiangxi　341000, China
2.
Key Laboratory of Development and Application of Ionic Rare Earth Resources (Jiangxi University of Science and Technology), Ministry of Education, Ganzhou, Jiangxi　341000, China

Received Date: 15 April 2021

Revised Date: 22 June 2021

Publish Date: 25 February 2022

Abstract

Abstract

In order to further explore the influence of evaluation units and non-landslide sample selection methods on landslide susceptibility prediction, a landslide susceptibility evaluation model is established based on self-organizing feature map network and random forest model in this paper. According to the relationship between grid units and slope units, an optimized calculation method of landslide susceptibility index is proposed. Aiming at the deficiencies of grid units and slope units in the evaluation of landslide susceptibility, this model proposes an optimized calculation method for landslide susceptibility index based on the relationship between grid cells and slope cells. On this basis, a landslide susceptibility evaluation model was established based on the random forest Tree Bagger classifier. By comparing and analyzing the influence of self-organizing feature map network and random non-landslide sample selection methods on the evaluation results, the effectiveness of the three evaluation models of self-organizing feature map network, random forest and self-organizing feature map network -random forest were discussed. The evaluation model has been applied to the landslide susceptibility evaluation in Dayu County. The results show that the prediction accuracy of random forest and self-organizing feature map network-random forest is higher, reaching 91.19% and 94.94% respectively, and the AUC of success rate curve was 0.822 and 0.849 respectively. It shows that self-organizing feature map network-random forest has higher prediction rate and success rate, although the prediction accuracy of self-organizing feature map network clustering is limited, it can effectively improve the evaluation accuracy of random forest model as the basis for selecting non landslide samples.
- slope units /
- landslide susceptibility /
- self-organizing feature map network /
- random forest /
- non-landslide sample

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References

[1]	黄发明, 叶舟, 姚池, 等. 滑坡易发性预测不确定性: 环境因子不同属性区间划分和不同数据驱动模型的影响[J]. 地球科学,2020,45(12):4535 − 4549. [HUANG Faming, YE Zhou, YAO Chi, et al. Uncertainties of landslide susceptibility prediction: different attribute interval divisions of environmental factors and different data-based models[J]. Earth Science,2020,45(12):4535 − 4549. (in Chinese with English abstract) Google Scholar
[2]	PEETHAMBARAN B, ANBALAGAN R, KANUNGO D P, et al. A comparative evaluation of supervised machine learning algorithms for township level landslide susceptibility zonation in parts of Indian Himalayas[J]. Catena,2020,195:104751. doi: 10.1016/j.catena.2020.104751 CrossRef Google Scholar
[3]	周超, 殷坤龙, 曹颖, 等. 基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价[J]. 地球科学,2020,45(6):1865 − 1876. [ZHOU Chao, YIN Kunlong, CAO Ying, et al. Landslide susceptibility assessment by applying the coupling method of radial basis neural network and adaboost: A case study from the Three Gorges Reservoir area[J]. Earth Science,2020,45(6):1865 − 1876. (in Chinese with English abstract) Google Scholar
[4]	BAHARVAND S, RAHNAMARAD J, SOORI S, et al. Landslide susceptibility zoning in a catchment of Zagros mountains using fuzzy logic and GIS[J]. Environmental Earth Sciences,2020,79(10):1 − 10. Google Scholar
[5]	杨永刚, 殷坤龙, 赵海燕, 等. 基于C5.0决策树-快速聚类模型的万州区库岸段乡镇滑坡易发性区划[J]. 地质科技情报,2019,38(6):189 − 197. [YANG Yonggang, YIN Kunlong, ZHAO Haiyan, et al. Landslide susceptibility evaluation for township units of bank section in Wanzhou district based on C5.0 decision tree and K-means cluster model[J]. Geological Science and Technology Information,2019,38(6):189 − 197. (in Chinese with English abstract) Google Scholar
[6]	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) Google Scholar
[7]	夏辉, 殷坤龙, 梁鑫, 等. 基于SVM-ANN模型的滑坡易发性评价: 以三峡库区巫山县为例[J]. 中国地质灾害与防治学报,2018,29(5):13 − 19. [XIA Hui, YIN Kunlong, LIANG Xin, et al. Landslide susceptibility assessment based on SVM-ANN Models: A case stualy for Wushan County in the Three Gorges Reservoir[J]. The Chinese Journal of Geological Hazard and Control,2018,29(5):13 − 19. (in Chinese with English abstract) Google Scholar
[8]	刘坚, 李树林, 陈涛. 基于优化随机森林模型的滑坡易发性评价[J]. 武汉大学学报(信息科学版),2018,43(7):1085 − 1091. [LIU Jian, LI Shulin, CHEN Tao. Landslide susceptibility assesment based on optimized random forest model[J]. Geomatics and Information Science of Wuhan University,2018,43(7):1085 − 1091. (in Chinese with English abstract) Google Scholar
[9]	杜国梁,杨志华,袁颖,等. 基于逻辑回归-信息量的川藏交通廊道滑坡易发性评价[J].水文地质工程地质,2021,48(5):102-111. Google Scholar DU Guoliang, YANG Zhihua, YUAN Ying, et al. Landslide susceptibility mapping in the Sichuan-Tibet traffic corridor using logistic regression-information value method[J]. Hydrogeology & Engineering,2021,48(5):102-111.(in Chinese with English) Google Scholar
[10]	连志鹏, 徐勇, 付圣, 等. 采用多模型融合方法评价滑坡灾害易发性: 以湖北省五峰县为例[J]. 地质科技通报,2020,39(3):178 − 186. [LIAN Zhipeng, XU Yong, FU Sheng, et al. Landslide susceptibility assessment based on multi-model fusion method: A case study in Wufeng County, Hubei Province[J]. Bulletin of Geological Science and Technology,2020,39(3):178 − 186. (in Chinese with English abstract) Google Scholar
[11]	郭子正, 殷坤龙, 付圣, 等. 基于GIS与WOE-BP模型的滑坡易发性评价[J]. 地球科学,2019,44(11):4299 − 4312. [GUO Zizheng, YIN Kunlong, FU Sheng, et al. Evaluation of landslide susceptibility based on GIS and WOE-BP model[J]. Earth Science,2019,44(11):4299 − 4312. (in Chinese with English abstract) Google Scholar
[12]	鲜木斯艳·阿布迪克依木, 何书. 基于MIV-BP神经网络的滑坡易发性空间预测[J]. 人民长江,2019,50(12):140 − 144. [ABUDIKEYIMU XMSY, HE Shu. Spatial prediction on landslide vulnerability based on MIV-BP neural network[J]. Yangtze River,2019,50(12):140 − 144. (in Chinese with English abstract) Google Scholar
[13]	李文娟, 邵海. 基于遥感影像多尺度分割与地质因子评价的滑坡易发性区划[J]. 中国地质灾害与防治学报,2021,32(2):94 − 99. [LI Wenjuan, SHAO Hai. Landslide susceptibility assessment based on multi-scale segmentation of remote sensing and geological factor evaluation[J]. The Chinese Journal of Geological Hazard and Control,2021,32(2):94 − 99. (in Chinese with English abstract) Google Scholar
[14]	PARYANI S, NESHAT A, JAVADI S, et al. Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping[J]. Natural Hazards,2020,103(2):1961 − 1988. doi: 10.1007/s11069-020-04067-9 CrossRef Google Scholar
[15]	谈树成, 赵娟娟, 杨林, 等. 基于GIS和信息量-快速聚类模型的滑坡易发性研究: 以云南省福贡县为例[J]. 云南大学学报(自然科学版),2018,40(6):1148 − 1158. [TAN Shucheng, ZHAO Juanjuan, YANG Lin, et al. Evaluation of landslide susceptibility based on GIS and the information value & the K-means cluster model—Take Fugong County as an Example[J]. Journal of Yunnan University (Natural Sciences Edition),2018,40(6):1148 − 1158. (in Chinese with English abstract) Google Scholar
[16]	孙长明, 马润勇, 尚合欣, 等. 基于滑坡分类的西宁市滑坡易发性评价[J]. 水文地质工程地质,2020,47(3):173 − 181. [SUN Changming, MA Runyong, SHANG Hexin, et al. Landslide susceptibility assessment in Xining based on landslide classification[J]. Hydrogeology & Engineering Geology,2020,47(3):173 − 181. (in Chinese with English abstract) Google Scholar
[17]	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) Google Scholar
[18]	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) Google Scholar
[19]	李颖, 应保胜, 容芷君, 等. 基于SOM的产品设计结构模块划分及其评价[J]. 武汉科技大学学报,2018,41(4):301 − 306. [LI Ying, YING Baosheng, RONG Zhijun, et al. SOM-based product design structure clustering and its evaluation[J]. Journal of Wuhan University of Science and Technology,2018,41(4):301 − 306. (in Chinese with English abstract) Google Scholar
[20]	王小川, 史峰, 郁磊. MATLAB神经网络43个案例分析[M]. 北京: 北京航空航天大学出版社, 2013. Google Scholar WANG Xiaochuan, SHI Feng, YU Lei. Analysis of 43 cases of neural network in matlab [M]. Beijing: Beijing University of Aeronautics & Astronautics Press, 2013. (in Chinese) Google Scholar
[21]	林荣福, 刘纪平, 徐胜华, 等. 随机森林赋权信息量的滑坡易发性评价方法[J]. 测绘科学,2020,45(21):131 − 138. [LIN Rongfu, LIU Jiping, XU Shenghua, et al. Evaluation method of landslide susceptibility based on random forest weighted information[J]. Science of Surveying and Mapping,2020,45(21):131 − 138. (in Chinese with English abstract) Google Scholar
[22]	李亭, 田原, 邬伦, 等. 基于随机森林方法的滑坡灾害危险性区划[J]. 地理与地理信息科学,2014,30(6):25 − 30. [LI Ting, TIAN Yuan, WU Lun, et al. Landslide susceptibility mapping using random forest[J]. Geography and Geo-Information Science,2014,30(6):25 − 30. (in Chinese with English abstract) doi: 10.3969/j.issn.1672-0504.2014.06.006 CrossRef Google Scholar
[23]	颜阁, 梁收运, 赵红亮. 基于GIS的斜坡单元划分方法改进与实现[J]. 地理科学,2017,37(11):1764 − 1770. [YAN Ge, LIANG Shouyun, ZHAO Hongliang. An approach to improving slope unit division using GIS technique[J]. Scientia Geographica Sinica,2017,37(11):1764 − 1770. (in Chinese with English abstract) Google Scholar
[24]	罗路广, 裴向军, 黄润秋. 强震山区地震滑坡发生概率研究: 以九寨沟国家地质公园为例[J]. 岩石力学与工程学报,2020,39(10):2079 − 2093. [LUO Luguang, PEI Xiangjun, HUANG Runqiu. Earthquake-triggered landslide occurrence probability in strong seismically mountainous areas: A case study of Jiuzhaigou National Geopark[J]. Chinese Journal of Rock Mechanics and Engineering,2020,39(10):2079 − 2093. (in Chinese with English abstract) Google Scholar
[25]	黄启乐, 陈伟, 唐绪波, 等. 区域地质灾害评价中斜坡单元划分方法研究[J]. 自然灾害学报,2017,26(5):157 − 164. [HUANG Qile, CHEN Wei, TANG Xubo, et al. Study on the method of slope unit zoning in regional geo-hazards risk assessment[J]. Journal of Natural Disasters,2017,26(5):157 − 164. (in Chinese with English abstract) Google Scholar
[26]	程温鸣, 彭令, 牛瑞卿. 基于粗糙集理论的滑坡易发性评价: 以三峡库区秭归县境内为例[J]. 中南大学学报(自然科学版),2013,44(3):1083 − 1090. [CHENG Wenming, PENG Ling, NIU Ruiqing. Landslide susceptibility assessment based on rough set theory: Taking Zigui County territory in Three Gorges Reservoir for example[J]. Journal of Central South University (Science and Technology),2013,44(3):1083 − 1090. (in Chinese with English abstract) Google Scholar
[27]	宫清华, 黄光庆, 张冬良, 等. 基于斜坡单元的浅层滑坡风险区划: 以华南松岗河小流域为例[J]. 安全与环境学报,2017,17(2):615 − 620. [GONG Qinghua, HUANG Guangqing, ZHANG Dongliang, et al. On the application of the slope element unit method to the shallow landslide risk-prevention mapping with a case study sample of Songgang watershed[J]. Journal of Safety and Environment,2017,17(2):615 − 620. (in Chinese with English abstract) Google Scholar
[28]	薛强, 张茂省, 高波. 斜坡单元支持下基于土体含水率的陕西省清涧县城区黄土滑坡危险性评价[J]. 中国地质,2020,47(6):1904 − 1914. [XUE Qiang, ZHANG Maosheng, GAO Bo. Hazard assessment of loess landslide based on soil moisture content and supported by slope unit in Qingjian City, Shaanxi Province[J]. Geology in China,2020,47(6):1904 − 1914. (in Chinese with English abstract) doi: 10.12029/gc20200624 CrossRef Google Scholar
[29]	PHAM B T, TIEN BUI D, PRAKASH I, et al. Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS[J]. Natural Hazards,2016,83(1):97 − 127. doi: 10.1007/s11069-016-2304-2 CrossRef Google Scholar
[30]	武雪玲, 任福, 牛瑞卿. 多源数据支持下的三峡库区滑坡灾害空间智能预测[J]. 武汉大学学报(信息科学版),2013,38(8):963 − 968. [WU Xueling, REN Fu, NIU Ruiqing. Spatial intelligent prediction of landslide hazard based on multi-source data in Three Gorges Reservoir area[J]. Geomatics and Information Science of Wuhan University,2013,38(8):963 − 968. (in Chinese with English abstract) Google Scholar