Citation: | BAI Guangshun, YANG Xuemei, ZHU Jieyong, ZHANG Shitao, ZHU Chuanbing, KANG Xiaobo, SUN Bin, ZHOU Yansong. Susceptibility assessment of geological hazards in Wuhua District of Kuming, China using the weight evidence method[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(5): 128-138. doi: 10.16031/j.cnki.issn.1003-8035.202203037 |
Geological hazard susceptibility assessment is an important basis for territorial space planning and geological hazard prevention and mitigation. In order to explore the evaluation method suitable for the geological hazard susceptibility of low hills and gullies in Yunnan plateau, Wuhua District of Kunming, Yunnan Province, China was selected as a typical study area. Eight factors including the engineering geology groups, distance from faults, elevation, slope, direction, curvature, distance from roads and land use covers were selected, and the weight evidence method based on Bayesian theory was applied to evaluate the susceptibility of geological hazards. After performing the Student-T test of the comprehensive evidence weight of each factor, the classification scheme of factors were optimized. The results of vulnerability zoning based on the evaluation of the model established in this paper showed that 89.9% and 9.1% of the geological hazard points fall into high and medium susceptibility areas. The comparative analysis showed that the modeling results are highly consistent with the geological hazards distribution, which better reveals the characteristics of geological hazards susceptibility in the study area. It can provide reference for the planning of geological hazards prevention in Wuhua District and other low hills and gullies areas of Yunnan plateau.
[1] | REGMI N R,GIARDINO J R,VITEK J D. Modeling susceptibility to landslides using the weight of evidence approach:western Colorado,USA[J]. Geomorphology,2010,115(1/2):172 − 187. doi: 10.1016/j.geomorph.2009.10.002 |
[2] | DU J,GLADE T,WOLDAI T,et al. Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley,Tibet,Chinese Himalayas[J]. Engineering Geology,2020,270:105572. doi: 10.1016/j.enggeo.2020.105572 |
[3] | GOYES-PEÑAFIEL P,HERNANDEZ-ROJAS A. Landslide susceptibility index based on the integration of logistic regression and weights of evidence:A case study in Popayan,Colombia[J]. Engineering Geology,2021,280:105958. doi: 10.1016/j.enggeo.2020.105958 |
[4] | BĂLTEANU D,MICU Mihai,JURCHESCU M,et al. National-scale landslide susceptibility map of Romania in a European methodological framework[J]. Geomorphology,2020,371:107432. doi: 10.1016/j.geomorph.2020.107432 |
[5] | SMITH H G,SPIEKERMANN R,BETTS H,et al. Comparing methods of landslide data acquisition and susceptibility modelling:examples from New Zealand[J]. Geomorphology,2021,381:107660. doi: 10.1016/j.geomorph.2021.107660 |
[6] | 黄立鑫,郝君明,李旺平,等. 基于RBF神经网络-信息量耦合模型的滑坡易发性评价—以甘肃岷县为例[J]. 中国地质灾害与防治学报,2021,32(6):116 − 126. [HUANG Lixin,HAO Junming,LI Wangping,et al. Landslide susceptibility assessment by the coupling method of RBF neural network and information value:A case study in Min Xian,Gansu Province[J]. The Chinese Journal of Geological Hazard and Control,2021,32(6):116 − 126. (in Chinese with English abstract) |
[7] | 廖小平,徐风光,蔡旭东,等. 香丽高速公路边坡地质灾害发育特征与易发性区划[J]. 中国地质灾害与防治学报,2021,32(5):121 − 129. [LIAO Xiaoping,XU Fengguang,CAI Xudong,et al. Development characteristics and susceptibality zoning of slope geological hazards in Xiangli expressway[J]. The Chinese Journal of Geological Hazard and Control,2021,32(5):121 − 129. (in Chinese with English abstract) doi: 10.16031/j.cnki.issn.1003-8035.2021.05-15 |
[8] | 罗守敬,王珊珊,付德荃. 北京山区突发性地质灾害易发性评价[J]. 中国地质灾害与防治学报,2021,32(4):126 − 133. [LUO Shoujing,WANG Shanshan,FU Dequan. Assessment on the susceptibility of sudden geological hazards in mountainous areas of Beijing[J]. The Chinese Journal of Geological Hazard and Control,2021,32(4):126 − 133. (in Chinese with English abstract) |
[9] | 吴赛男,田毅. 我国单体滑坡模拟和区域滑坡易发性评价研究进展[J]. 中国地质灾害与防治学报,2019,30(3):113 − 119. [WU Sainan,TIAN Yi. Review on progress of individual landslide simulation and assessment of reginal landslide susceptibility in China[J]. The Chinese Journal of Geological Hazard and Control,2019,30(3):113 − 119. (in Chinese with English abstract) |
[10] | 闫怡秋,杨志华,张绪教,等. 基于加权证据权模型的青藏高原东部巴塘断裂带滑坡易发性评价[J]. 现代地质,2021,35(1):26 − 37. [YAN Yiqiu,YANG Zhihua,ZHANG Xujiao,et al. Landslide susceptibility assessment based on weight-of-evidence modeling of the Batang fault zone,eastern Tibetan Plateau[J]. Geoscience,2021,35(1):26 − 37. (in Chinese with English abstract) |
[11] | LUSTED L B. An introduction to medical decision making[J]. American Journal of Physical Medicine & Rehabilitation,1970,49(5):322. |
[12] | SPIEGELHALTER D J,KNILL-JONES R P. Statistical and knowledge-based approaches to clinical decision-support systems,with an application in gastroenterology[J]. Journal of the Royal Statistical Society Series A (General),1984,147(1):35. doi: 10.2307/2981737 |
[13] | BONHAM-CARTER G F, AGTERBERG F P, WRIGHT D F. Weights of evidence modelling: A new approach to mapping mineral potential[R]. Natural Resources Canada/CMSS/Information Management, 1990. |
[14] | AGTERBERG F P. Combining indicator patterns in weights of evidence modeling for resource evaluation[J]. Nonrenewable Resources,1992,1(1):39 − 50. doi: 10.1007/BF01782111 |
[15] | AGTERBERG F P, BONHARN-CARTER G F. Weights of evidence modeling and weighted logistic regression for mineral potential mapping[M]. Computers in Geology - 25 Years of Progress. 13 − 32: Oxford University Press, 1994. |
[16] | CARRANZA E,HALE M. Spatial association of mineral occurrences and curvilinear geological features[J]. Mathematical Geology,2002,34:203 − 221. doi: 10.1023/A%3A1014416319335 |
[17] | 孙琳,任娜娜,李云安,等. 基于证据权法的公路路基岩溶塌陷危险性评价[J]. 中国地质灾害与防治学报,2019,30(3):94 − 100. [SUN Lin,REN Nana,LI Yunan,et al. Risk assessment on karst collapse of the highway subgrade based on weights of evidence method[J]. The Chinese Journal of Geological Hazard and Control,2019,30(3):94 − 100. (in Chinese with English abstract) |
[18] | 刘璐瑶, 高惠瑛. 基于证据权与Logistic回归模型耦合的滑坡易发性评价[J/OL]. 工程地质学报. https://doi.org/10.13544/j.cnki.jeg.2020-482. LIU Luyao, GAO Huiying. Landslide susceptibility assessment based on coupling of woe model and logistic regression model[J/OL]. Journal of Engineering Geology. https://doi.org/10.13544/j.cnki.jeg.2020-482. (in Chinese with English abstract) |
[19] | CHEN L F,GUO H X,GONG P S,et al. Landslide susceptibility assessment using weights-of-evidence model and cluster analysis along the highways in the Hubei section of the Three Gorges Reservoir Area[J]. Computers & Geosciences,2021,156:104899. doi: 10.1016/j.cageo.2021.104899 |
[20] | ALSABHAN A H,SINGH K,SHARMA A,et al. Landslide susceptibility assessment in the Himalayan range based along Kasauli - Parwanoo Road corridor using weight of evidence,information value,and frequency ratio[J]. Journal of King Saud University - Science,2022,34(2):101759. doi: 10.1016/j.jksus.2021.101759 |
[21] | SAHA A,SAHA S. Comparing the efficiency of weight of evidence,support vector machine and their ensemble approaches in landslide susceptibility modelling:a study on Kurseong region of Darjeeling Himalaya,India[J]. Remote Sensing Applications:Society and Environment,2020,19:100323. doi: 10.1016/j.rsase.2020.100323 |
[22] | 黄发明,石雨,欧阳慰平,等. 基于证据权和卡方自动交互检测决策树的滑坡易发性预测[J]. 土木与环境工程学报(中英文),2022,44(5):1 − 15. [HUANG Faming,SHI Yu,OUYANG Weiping,et al. Landslide susceptibility prediction modeling based on weight of evidence and Chi-square automatic interactive detection[J]. Journal of Civil and Environmental Engineering,2022,44(5):1 − 15. (in Chinese with English abstract) |
[23] | 杨华阳,许向宁,杨鸿发. 基于证据权法的九寨沟地震滑坡危险性评价[J]. 中国地质灾害与防治学报,2020,31(3):20 − 29. [YANG Huayang,XU Xiangning,YANG Hongfa. The Jiuzhaigou co-seismic landslide hazard assessment based on weight of evidence method[J]. The Chinese Journal of Geological Hazard and Control,2020,31(3):20 − 29. (in Chinese with English abstract) |
[24] | 云南省地质局第二区测队. 昆明幅G-48-25 1/20万地质调查报告[R]. 昆明: 云南省地质局, 1971 The Second Regional Survey Bureau. Kunming G-48-25 1/200000 Geological survey report[R]. Kunming: Yunnan Geological Bureau, 1971. (in Chinese with English abstract) |
[25] | 云南省地质局第二区测大队. 武定幅G-48-19 1/20万地质图, 矿产图及其说明书[R]. 昆明: 云南省地质局, 1969 The Second Regional Survey Bureau. Wuding G-48-19 1/200000 geological map, mineral map and description[R]. Kunming: Yunnan Geological Bureau, 1969. (in Chinese) |
[26] | BONHAM-CARTER G F. Geographic information systems for geoscientists: Modelling with GIS[M]. Canada: Pergamon, 1994. |
[27] | CARTER GF B,AGTERBERG F P,WRIGHT D F. Integration of geological datasets for gold exploration in Nova Scotia[J]. photogrammetric Engineering & Remote Sensing,1990,54(11):1585 − 1592. |
[28] | AGTERBERG F P, BONHAM-CARTER G F, WRIGHT D F. Statistical pattern integration for mineral exploration[M].Computer Applications in Resource Estimation Amsterdam: Elsevier, 1990: 1 − 21. |
[29] | HOYER A,KUSS O. Meta-analysis of full ROC curves with flexible parametric distributions of diagnostic test values[J]. Research Synthesis Methods,2020,11(2):301 − 313. doi: 10.1002/jrsm.1395 |
[30] | WALKER S P. The ROC curve redefined - optimizing sensitivity (and specificity) to the lived reality of cancer[J]. The New England Journal of Medicine,2019,380(17):1594 − 1595. doi: 10.1056/NEJMp1814951 |
[31] | OMAR L,IVRISSIMTZIS I. Using theoretical ROC curves for analysing machine learning binary classifiers[J]. Pattern Recognition Letters,2019,128:447 − 451. doi: 10.1016/j.patrec.2019.10.004 |
[32] | 王高峰, 郭宁, 邓兵, 等. 不同组合模型区域滑坡易发性及精度分析[J]. 西北地质,2021,54(2):259 − 272. [WANG Gaofeng, GUO Ning, DENG Bing, et al. Analysis of landslide susceptibility and accuracy in different combination models[J]. Northwestern Geology,2021,54(2):259 − 272. (in Chinese with English abstract) |
Basic data charts of factors
Map of geological hazard distribution (The bottom was rendered by elevation and hillshade)
Statistical charts of correlation between the factors and the number of geological hazard points
Calculation results charts of factor evidence weights
ROC curve of model prediction performance
Grid map of geological hazard susceptibility
Factors and geological hazards in typical zone