Coal Geology & Exploration


At present, there is no fixed way to predict the sensitive zones of subsidence disaster development in underground coal mining areas, and the prediction result of sensitive zones has a great uncertainty. Herein, the subsidence disaster in Xishan area of Taiyuan City, Shanxi Province was taken as the research object. Totally 4 types of kernel SVM based prediction model for sensitivity zoning of subsidence disaster were constructed with the methods of GIS spatial analysis, statistical analysis and Support Vector Machine (SVM) in combination, taking the subsidence disaster data checked and recorded in 2012 and 2014 as the modeling and verification data respectively, as well as the elevation, slope gradient, slope aspect, topographic relief, surface curvature, stratigraphic strata and geological structure as the sensitivity assessment factors. Meanwhile, analysis was performed on the weight of assessment factors, the model optimization, the prediction results of sensitivity zoning, the prediction accuracy, and the applicability of models respectively. The results show that the polynomial kernel-SVM model (PL-SVM) has relatively high training accuracy (with the area under the receiver characteristic curve of AUC=0.854) and validation accuracy (AUC=0.755), as well as good prediction capability. Thus, it has the best performance among the 4 types of models, and the sensitivity zoning is reasonable, with more points of subsidence disaster distributed in a small area of the very-high and high sensitive zones, while few points of subsidence disaster distributed in a large area of the low sensitive zones. As predicted by the PL-SVM model, the area proportion of very-high, high, moderate and low sensitive zones of subsidence disaster in Taiyuan Xishan area is 20.19%, 17.43%, 21.18% and 41.20%, respectively. Besides, the frequency ratio and the sensitivity grade are in good positive correlation, showing a linear functional relation. The sensitivity assessment result based on PL-SVM model is reliable and has good applicability, which has reference significance to the study on the development characteristics of subsidence disasters in underground coal mining areas and the prediction of key areas in disaster survey.


underground coal mining area, subsidence disaster, sensitivity zoning, assessment factor, support vector machine, prediction model, geological disaster




[1] 邹友峰,邓喀中,马伟民. 矿山开采沉陷工程[M]. 徐州:中国矿业大学出版社,2003.

[2] 毕银丽,伍越,张健,等. 采用 HYDRUS模拟采煤沉陷地裂缝区土壤水盐运移规律[J]. 煤炭学报,2020,45(1):360−367

BI Yinli,WU Yue,ZHANG Jian,et al. Simulation of soil water and salt movement in mining ground fissure zone based on HYDRUS[J]. Journal of China Coal Society,2020,45(1):360−367

[3] 彭苏萍,毕银丽. 黄河流域煤矿区生态环境修复关键技术与战略思考[J]. 煤炭学报,2020,45(4):1211−1221

PENG Suping,BI Yinli. Strategic consideration and core technology about environmental ecological restoration in coal mine areas in the Yellow River Basin of China[J]. Journal of China Coal Society,2020,45(4):1211−1221

[4] POURGHASEMI H R,PRADHAN B,GOKCEOGLU C. Application of fuzzy logic and Analytical Hierarchy Process (AHP) to landslide susceptibility mapping at Haraz watershed,Iran[J]. Natural Hazards,2012,63(2):965−996.

[5] PHAM B T,SHIRZADI A,BUI D T,et al. A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling:A case study in the Himalayan area,India[J]. International Journal of Sediment Research,2018,33(2):157−170.

[6] ZAMANIRAD M,SARRAF A,SEDGHI H,et al. Modeling the influence of groundwater exploitation on land subsidence susceptibility using machine learning algorithms[J]. Natural Resources Research,2020,29:1127−1141.

[7] ELMAHDY S I,MOHAMED M M,ALI T A,et al. Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain,UAE[J]. Geocarto International,2020,37(1):315−331.

[8] EBRAHIMY H,FEIZIZADEH B,SALMANI S,et al. A comparative study of land subsidence susceptibility mapping of Tasuj plane,Iran,using boosted regression tree,random forest and classification and regression tree methods[J]. Environmental Earth Sciences,2020,79(10):223.

[9] LEE S,HYUN–JOO O,KI–DONG K. Statistical spatial modeling of ground subsidence hazard near an abandoned underground coal mine[J]. Disaster Advances,2010,3(1):11−23.

[10] OH H,AHN S,CHOI J,et al. Sensitivity analysis for the GIS–based mapping of the ground subsidence hazard near abandoned underground coal mines[J]. Environmental Earth Sciences,2011,64(2):347−358.

[11] PRAKASH S S,MANISH Y,JYOTI D A,et al. Multivariate statistical approach for assessment of subsidence in Jharia coalfields,India[J]. Arabian Journal of Geosciences,2017,10(8):191.

[12] OH H,SYIFA M,LEE C,et al. Land subsidence susceptibility mapping using bayesian,functional,and meta−ensemble machine learning models[J]. Applied. Sciences,2019,9(6):1248.

[13] MOHAMMADY M,POURGHASEMI H R,AMIRI M. Assessment of land subsidence susceptibility in Semnan plain (Iran):A comparison of support vector machine and weights of evidence data mining algorithms[J]. Natural Hazards,2019,99(2):951−971.

[14] EMAMI S N,YOUSEFI S,POURGHASEMI H R,et al. A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)[J]. Bulletin of Engineering Geology and the Environment,2020,79:5291−5308.

[15] ARABAMERI A,SAHA S,ROY J,et al. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility[J]. Science of the Total Environment,2020,726:138595.

[16] NADIRI A A,KHATIBI R,KHALIFI P,et al. A study of subsidence hotspots by mapping vulnerability indices through innovatory “ALPRIFT”using artificial intelligence at two levels[J]. Bulletin of Engineering Geology and the Environment,2020,79:3989−4003.

[17] BIANCHINI S,SOLARI L,SOLDATO M D,et al. Ground subsidence susceptibility (GSS) mapping in Grosseto plain (Tuscany,Italy) based on satellite InSAR data using frequency ratio and fuzzy logic[J]. Remote Sensing,2019,11(17):11.

[18] 覃乙根,杨根兰,谢金,等. 贵州省开阳县斜坡地质灾害孕灾因子敏感性分析[J]. 煤田地质与勘探,2020,48(4):190−198

QIN Yigen,YANG Genlan,XIE Jin,et al. Sensitivity analysis of disaster–pregnant environmental factors for slope geological hazards in Kaiyang County,Guizhou Province[J]. Coal Geology & Exploration,2020,48(4):190−198

[19] ABDOLLAHI S,POURGHASEMI H R,GHANBARIAN G A,et al. Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions[J]. Bulletin of Engineering Geology and the Environment,2019,78(6):4017−4034.

[20] GHORBANZADEH O,ROSTAMZADEH H,BLASCHKE T,et al. A new GIS–based data mining technique using an Adaptive Neuro–Fuzzy Inference System (ANFIS) and k–fold cross–validation approach for land subsidence susceptibility mapping[J]. Natural Hazards,2018,94:497−517.

[21] SINGH K B,DHAR B B. Sinkhole subsidence due to mining[J]. Geotechnical & Geological Engineering,1997,15(4):327−341.

[22] SAHU P,LOKHANDE R D. An investigation of sinkhole subsidence and its preventive measures in underground coal mining[J]. Procedia Earth and Planetary Science,2015,11:63−75.

[23] WU Zhiyong,NIU Qinghe,LI Wenping,et al. Ground stability evaluation of a coal–mining area:A case study of Yingshouyingzi mining area,China[J]. Journal of Geophysics and Engineering,2018,15(5):2252−2265.

[24] 张明媚,葛永慧,薛永安,等. 地下采煤区地质灾害发育空间特征及其成因[J]. 太原理工大学学报,2019,50(4):472−477

ZHANG Mingmei,GE Yonghui,XUE Yong’an,et al. Spatial characteristics and genesis of geological disasters in underground coal mining area[J]. Journal of Taiyuan University of Technology,2019,50(4):472−477

[25] 张明媚,薛永安. 斜坡地质灾害敏感性评价中地势起伏度提取最佳尺度研究[J]. 太原理工大学学报,2020,51(6):881−888

ZHANG Mingmei,XUE Yong’an. Optimal scale for extracting relief amplitude in slope geological hazard sensitivity evaluation[J]. Journal of Taiyuan University of Technology,2020,51(6):881−888

[26] 张明媚,薛永安,吕义清,等. 地下采煤扰动影响区数字地貌时空演变特征[J]. 煤矿安全,2019,50(5):243−246

ZHANG Mingmei,XUE Yong’an,LYU Yiqing,et al. Temporal and spatial evolution characteristics of digital geomorphology in affected zone of underground coal mining disturbance[J]. Safety in Coal Mines,2019,50(5):243−246

[27] 张明媚,薛永安,吕义清,等. 地下采煤扰动影响土地利用时空演变研究[J]. 中国煤炭,2019,45(5):102−106

ZHANG Mingmei,XUE Yong’an,LYU Yiqing,et al. Research on temporal–spatial evolution of land utilization affected by underground coal mining disturbance[J]. China Coal,2019,45(5):102−106

[28] 邹友峰. 高强度开采地表生态环境演变机理与调控[M]. 北京:科学出版社,2019.

[29] GIROSI F. An equivalence between sparse approximation and support vector machines[J]. Neural Computation,1998,10(6):1455−1480.

[30] 徐胜华,刘纪平,王想红,等. 熵指数融入支持向量机的滑坡灾害易发性评价方法:以陕西省为例[J]. 武汉大学学报·信息科学版,2020,45(8):1214−1222

XU Shenghua,LIU Jiping,WANG Xianghong,et al. Landslide susceptibility assessment method incorporating index of entropy based on support vector machine:A case study of Shaanxi Province[J]. Geomatics and Information Science of Wuhan University,2020,45(8):1214−1222

[31] 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 & Exploration,2017,45(6):129−138

[32] 古天龙,李龙,常亮,等. 公平机器学习:概念、分析与设计[J]. 计算机学报,10/25/2022,46(5):1018−1051

GU Tianlong,LI Long,CHANG Liang,et al. Fair machine learning:Concepts,analysis,and design[J]. Chinese Journal of Computers,10/25/2022,46(5):1018−1051

[33] WANG Yue,WEN Haijia,SUN Deliang,et al. Quantitative assessment of landslide risk based on susceptibility mapping using random forest and geo−detector[J]. Remote Sensing,2021,13(13):2625.



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