基于粗糙集–神经网络的IBURI地震滑坡易发性研究

doi:10.13722/j.cnki.jrme.2020.1122

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1999 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The landslides induced by strong earthquakes are wide in distribution and large in quantity，showing obvious regional characteristic. Besides intense ground motion，earthquake-triggered landslides are also affected by environmental conditions such as rainfall，regional hydrogeology and other complex factors. Reasonable index selection strategy can fundamentally improve the accuracy and efficiency of the susceptibility evaluation of seismic landslides. The present research takes 3307 landslides triggered by Mj 6.7 Hokkaido Eastern Iburi earthquake on September 6，2018，as the study object to analyze the construction of evaluation indicator system. First，based on the comprehensive consideration of seismic characteristics，17 original evaluation indicators are selected according to the earthquake disaster investigation and data analysis. Then，rough set theory is introduced as the index selection strategy，and the reduction ability of the rough set is adopted to delete 9 factors with less relevance to landslides. Finally，the input layer of BP neural network is constructed with the optimized indicator system，and a rough set-BP neural network model for evaluating the susceptibility of seismic landslides is established. The results show that the prediction accuracy of the model is improved from 63.8% to 94.4%，indicating that the rough set-BP neural network model can effectively improve the accuracy of seismic landslide susceptibility evaluation.

Key words： slope engineering seismic landslides evaluation of susceptibility rough set theory BP neural network Hokkaido IBURI earthquake

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WU Yuchen
	ZHOU Hanxu
	CHE Ailan

Cite this article:

WU Yuchen,ZHOU Hanxu,CHE Ailan. Susceptibility of landslides caused by IBURI earthquake based on rough set-neural network[J]. , 2021, 40(6): 1226-1235.

URL:

https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2020.1122 OR https://rockmech.whrsm.ac.cn/EN/Y2021/V40/I6/1226

[1] 许冲，徐锡伟，周本刚，等. 同震滑坡发生概率研究——新一代地震滑坡危险性模型[J]. 工程地质学报，2019，27(5)：1 122–1 130. (XU Chong，XU Xiwei，ZHOU Bengang，et al. Probability of coseismic landslides：a new generation of earthquake-triggered landslide hazard model[J]. Journal of Engineering Geology，2019，27(5)：1 122–1 130.(in Chinese))
[2] 马思远，许冲，王涛，等. 应用2类Newmark简易模型进行2008年汶川地震滑坡评估[J]. 地震地质，2019，41(3)：774–788.(MA Siyuan，XU Chong，WANG Tao，et al. Application of two simplified Newmark models to the assessment of landslides triggered by the 2008 Wenchuan earthquake[J]. Seismology and Geology，2019，41(3)：774–788.(in Chinese))
[3] JIN K P，YAO L K，CHENG Q G，et al. Seismic landslides hazard zoning based on the modified Newmark model：a case study from the Lushan earthquake，China[J]. Natural Hazards，2019，99：493–509.
[4] WANG Y，FANG Z C，HONG H Y. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County，China[J]. Science of the Total Environment，2019，666：975–993.
[5] CAO J，ZHANG Z，WANG C Z，et al. Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau[J]. Catena，2019，175：63–76.
[6] 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))
[7] MOAYEDI H，MEHRABI M，KALANTAR B，et al. Novel hybrids of adaptive neuro-fuzzy inference system(ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide[J]. Geomatics，Natural Hazards and Risk，2019，10(1)：1 879–1 911.
[8] 王兰民，车爱兰，王林. 日本北海道6.7级地震灾害特点与启示[J]. 城市与减灾，2019，(1)：1–8.(WANG Lanmin，CHE Ailan，WANG Lin. Disaster features and enlightenment of Hokkaido Ms6.7 earthquake in Japan[J]. City and Disaster Reduction，2019，(1)：1–8.(in Chinese))
[9] ZHANG S，LI R，WANG F W，et al. Characteristics of landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake，Northern Japan[J]. Landslides，2019，16：1 691–1 708.
[10] DHAKAL Y P，KUNUGI T，KIMURA T，et al. Peak ground motions and characteristics of nonlinear site response during the 2018 Mw 6.6 Hokkaido eastern Iburi earthquake[J]. Earth，Planets and Space，2019，56–71.
[11] NAM K，WANG F W. The performance of using an autoencoder for prediction and susceptibility assessment of landslides：A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan[J]. Geoenvironmental Disasters，2019，6(1)：19.
[12] YAMAGISHI H，YAMAZAKI F. Landslides by the 2018 Hokkaido Iburi-Tobu Earthquake on September 6[J]. Landslides，2018，15(12)：2 521–2 524.
[13] SEREY A，PIÑERO-FELICIANGELI L，SEPÚLVEDA S A，et al. Landslides induced by the 2010 Chile megathrust earthquake：a comprehensive inventory and correlations with geological and seismic factors[J]. Landslides，2019，16(6)：1 153–1 165.
[14] 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)：2 079–2 093.(LUO Luguang，PEI Xiangjun，HUANG Runqiu. Earthquake-triggered landslide occurrence probability in strong seismically mountainous area：a case study of Jiuzhaigou National Geopark[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(10)：2 079–2 093.(in Chinese))
[15] 胡燕，王慧琴，黄东宇，等. 基于改进GA-RS的火焰图像特征自适应选择[J]. 计算机工程，2015，41(8)：186–189.(HU Yan，WANG Huiqin，HUANG Dongyu，et al. Adaptive selection of flame image features based on improved GA-RS[J]. Computer Engineering，2015，41(8)：186–189.(in Chinese))
[16] ZHANG Q H，XIE Q，WANG G Y. A survey on rough set theory and its applications[J]. CAAI Transactions on Intelligence Technology，2016，1(4)：323–333.
[17] 唐睿旋，晏鄂川，唐薇. 基于粗糙集和BP神经网络的滑坡易发性评价[J]. 煤田地质与勘探，2017，45(6)：129–138.(TANG Ruixuan，YAN Echuan，TANG Wei. Landslide susceptibility evaluation based on rough set and back-propagation neural network[J]. Coal Geology and Exploration，2017，45(6)：129–138.(in Chinese))
[18] 严武文. 基于粗集—神经网络的区域滑坡灾害易发性预测研究[硕士学位论文][D]. 武汉：中国地质大学，2010.(YAN Wuwen. The study of regional landslide susceptibility prediction based on rough sets-neural network[M. S. Thesis][D]. Wuhan：China University of Geosciences，2010.(in Chinese))
[19] 于宪煜，胡友健，牛瑞卿. 基于RS-SVM模型的滑坡易发性评价因子选择方法研究[J]. 地理与地理信息科学，2016，32(3)：23–28.(YU Xianyu，HU Youjian，NIU Ruiqing. Research on the method to select landslides susceptibility evaluation factors based on RS-SVM model[J]. Geography and Geo-Information Science，2016，32(3)：23–28.(in Chinese))
[20] 吴尚智，苟平章. 粗糙集和信息熵的属性约简算法及其应用[J]. 计算机工程，2011，37(7)：56–58.(WU Shangzhi，GOU Pingzhang. Attribute reduction algorithm on rough set and information entropy and its application[J]. Computer Engineering，2011，37(7)：56–58.(in Chinese))
[21] 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))
[22] MELCHIORRE C，MATTEUCCI M，AZZONI A，et al. Artificial neural networks and cluster analysis in landslide susceptibility zonation[J]. Geomorphology，2008，94：379–400.
[23] XU K，GUO Q，LI Z W，et al. Landslide susceptibility evaluation based on BPNN and GIS：a case of Guojiaba in the Three Gorges Reservoir Area[J]. International Journal of Geographical Information Science，2015，29(7)：1 111–1 124.
[24] 赵久彬，刘元雪，刘娜，等. 海量监测数据下分布式BP神经网络区域滑坡空间预测方法[J]. 岩土力学，2019，40(7)：2 866–2 872. (ZHAO Jiubin，LIU Yuanxue，LIU Na，et al. Spatial prediction method of regional landslide based on distributed bp neural network algorithm under massive monitoring data[J]. Rock and Soil Mechanics，2019，40(7)：2 866–2 872.(in Chinese))
[25] ZÊZERE J L，PEREIRA S，MELO R，et al. Mapping landslide susceptibility using data-driven methods[J]. Science of the Total Environment，2016，589：250–267.
[26] 张玘恺，凌斯祥，李晓宁，等. 九寨沟县滑坡灾害易发性快速评估模型对比研究[J]. 岩石力学与工程学报，2020，39(8)：1 595–1 610. (ZHANG Qikai，LING Sixiang，LI Xiaoning，et al. Comparison of landslide susceptibility mapping rapid assessment models in Jiuzhaigou County，Sichuan province，China[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(8)：1 595–1 610.(in Chinese))