基于灰色关联度模型的区域滑坡敏感性评价
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摘要
数理统计和机器学习模型如支持向量机(support vector machine,SVM)等,在区域滑坡敏感性评价中得到广泛的应用.但这些模型的建模过程往往较复杂,如在对机器学习进行训练和测试时难以选取合理的非滑坡栅格单元,而且有较多的模型参数需要确定.为提高滑坡敏感性评价建模的效率和精度,提出基于灰色关联度的敏感性评价模型.灰色关联度模型能有效计算各比较样本与参考样本之间的定量的关联度,具有建模过程简洁和评价精度高的优点,该模型目前在区域滑坡敏感性评价中的应用还没有引起研究人员的足够关注且有待进一步拓展.拟将灰色关联度模型用于浙江省飞云江流域南田—雅梅图幅(南田地区)的滑坡敏感性评价,并将得到的评价结果与SVM模型的敏感性评价结果作对比分析.结果显示,灰色关联度模型在高和极高敏感区的滑坡预测精度优于SVM模型,而在中等敏感区的滑坡预测精度略低于SVM模型;整体而言,灰色关联度模型对整个南田地区滑坡敏感性分布的预测精度略高于SVM模型.对两个模型建模过程的对比结果显示,灰色关联度模型建模较简单,具有比SVM模型更高的建模效率,为滑坡敏感性评价提供了一种新思路.
Statistical and machine learning models,such as support vector machine(SVM),have been widely used to assess the landslide susceptibility.However,the modeling processes of statistical and machine learning model are generally complex.For example,it is difficult to select reasonable non-landslide grid cells when the machine learning models are trained and tested,and many model parameters need to be determined.In order to improve the efficiency and accuracy of the model used for landslide susceptibility assessment,the grey relational degree(GRD)model is proposed.The GRD model can efficiently calculate the quantitative relational degrees between the comparative samples and the reference sample,and it has the advantages of simple modeling process and accurate assessment results.However,few studies have been done on the GRD model.In this study,the GRD model is used to assess the landslide susceptibility in the Nantian and Yamei maps(Nantian area)in the FeiyunjiangRiver basin,Zhejiang Province of China,and the assessment results of the GRD model are compared with those of the SVM model.The results show that the GRD model has higher prediction rate than the SVM model in the high and very high susceptibility areas,and has slightly lower prediction rate than the SVM in the moderate susceptibility area.On the whole,the GRD model has slightly higher prediction rate than the SVM for landslide susceptibility assessment in Nantian area.Meanwhile,the results also show that the model process of GRD is simple,it has higher efficiency than the SVM.The GRD model provides a novel idea for landslide susceptibility assessment.
Statistical and machine learning models,such as support vector machine(SVM),have been widely used to assess the landslide susceptibility.However,the modeling processes of statistical and machine learning model are generally complex.For example,it is difficult to select reasonable non-landslide grid cells when the machine learning models are trained and tested,and many model parameters need to be determined.In order to improve the efficiency and accuracy of the model used for landslide susceptibility assessment,the grey relational degree(GRD)model is proposed.The GRD model can efficiently calculate the quantitative relational degrees between the comparative samples and the reference sample,and it has the advantages of simple modeling process and accurate assessment results.However,few studies have been done on the GRD model.In this study,the GRD model is used to assess the landslide susceptibility in the Nantian and Yamei maps(Nantian area)in the FeiyunjiangRiver basin,Zhejiang Province of China,and the assessment results of the GRD model are compared with those of the SVM model.The results show that the GRD model has higher prediction rate than the SVM model in the high and very high susceptibility areas,and has slightly lower prediction rate than the SVM in the moderate susceptibility area.On the whole,the GRD model has slightly higher prediction rate than the SVM for landslide susceptibility assessment in Nantian area.Meanwhile,the results also show that the model process of GRD is simple,it has higher efficiency than the SVM.The GRD model provides a novel idea for landslide susceptibility assessment.
引文
Bui,D.,Pradhan,B.,Lofman,O.,et al.,2012a.Landslide Susceptibility Assessment in the Hoa Binh Province of Vietnam:A Comparison of the Levenberg-Marquardt and Bayesian Regularized Neural Networks.Geomorphology,171-172:12-29.https://doi.org/10.1016/j.geomorph.2012.04.023
Bui,D.,Pradhan,B.,Lofman,O.,et al.,2012b.Spatial Prediction of Landslide Hazards in Hoa Binh Province(Vietnam):A Comparative Assessment of the Efficacy of Evidential Belief Functions and Fuzzy Logic Models.Catena,96:28-40.https://doi.org/10.1016/j.catena.2012.04.001
Chung,C.J.,Fabbri,A.G.,2008.Predicting Landslides for Risk Analysis-Spatial Models Tested by a CrossValidation Technique.Geomorphology,94(3-4):438-452.https://doi.org/10.1016/j.geomorph.2006.12.036
Deng,J.L.,1989.Introduction to Grey System Theory.Journal of Grey System,1(1):1-24.
Feng,H.J.,Zhou,A.G.,Yu,J.J.,et al.,2016.A Comparative Study on Plum-Rain-Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province.Earth Science,41(3):403-415(in Chinese with English abstract).
Hong,H.Y.,Pradhan,B.,Xu,C.,et al.,2015.Spatial Prediction of Landslide Hazard at the Yihuang Area(China)Using Two-Class Kernel Logistic Regression,Alternating Decision Tree and Support Vector Machines.Catena,133:266-281.https://doi.org/10.1016/j.catena.2015.05.019
Huang,F.M.,Huang,J.S.,Jiang,S.H.,et al.,2017a.Landslide Displacement Prediction Based on Multivariate Chaotic Model and Extreme Learning Machine.Engineering Geology,218:173-186.https://doi.org/10.1016/j.enggeo.2017.01.016
Huang,F.M.,Yin,K.L.,Huang,J.S.,et al.,2017b.Landslide Susceptibility Mapping Based on Self-Organizing-Map Network and Extreme Learning Machine.Engineering Geology,223:11-22.https://doi.org/10.1016/j.enggeo.2017.04.013
Huang,F.M.,Yin,K.L.,Tao,H.,et al.,2016a.Influencing Factor Analysis and Displacement Prediction in Reservoir Landslides-A Case Study of Three Gorges Reservoir(China).Tehnickivjesnik-Technical Gazette,23(2):617-626.https://doi.org/10.17559/tv-20150314105216
Huang,F.M.,Yin,K.L.,Zhang,G.R.,et al.,2016b.Landslide Displacement Prediction Using Discrete Wavelet Transform and Extreme Learning Machine Based on Chaos Theory.Environmental Earth Sciences,75(20):1376.https://doi.org/10.1007/s12665-016-6133-0
Huang,F.M.,Yin,K.L.,Zhang,G.R.,et al.,2015.Landslide Groundwater Level Time Series Prediction Based on Phase Space Reconstruction and Wavelet AnalysisSupport Vector Machine Optimized by PSO Algorithm.Earth Science,40(7):1254-1265(in Chinese with English abstract).
Huang,W.D.,Yang,J.S.,Chen,J.C.,et al.,2010.Grey Relational Analysis of Physico-Chemical Variables and Water Body Reflectance of a Chi-Chi Earthquake Landslide-Dammed Lake Monitored by Spot Satellite.Journal of Grey System,22(4):297-308.
Kavzoglu,T.,Sahin,E.K.,Colkesen,I.,2014.Landslide Susceptibility Mapping Using GIS-Based Multi-Criteria Decision Analysis,Support Vector Machines,and Logistic Regression.Landslides,11(3):425-439.https://doi.org/10.1007/s10346-013-0391-7
Lee,S.,Ryu,J.H.,Kim,I.S.,2007.Landslide Susceptibility Analysis and Its Verification Using Likelihood Ratio,Logistic Regression,and Artificial Neural Network Models:Case Study of Youngin,Korea.Landslides,4(4):327-338.https://doi.org/10.1007/s10346-007-0088-x
Li,J.L.,Ma,D.H.,Wang,W.,2016.Assessment of Potential Seismic Landslide Hazard Based on Evidence Theory and Entropy Weight Grey Incidence.Journal of Central South University(Science and Technology),47(5):1730-1736(in Chinese with English abstract).
Li,X.Z.,Wang,C.H.,Deng,H.Y.,2010.Application of Grey Relation Analysis and Distance Discrimination Analysis Methods in Discriminating Potential Landslides of Xiluodu Reservoir Area.The Chinese Journal of Geological Hazard and Control,21(4):77-81(in Chinese with English abstract).
Li,Y.Y.,Huang,J.S.,Jiang,S.H.,et al.,2017.A Web-Based GPSSystem for Displacement Monitoring and Failure Mechanism Analysis of Reservoir Landslide.Scientific Reports,7(1).https://doi.org/10.1038/s41598-017-17507-7
Liu,Y.L.,Huang,X.L.,Duan,J.,et al.,2017.The Assessment of Traffic Accident Risk Based on Grey Relational Analysis and Fuzzy Comprehensive Evaluation Method.Natural Hazards,88(3):1409-1422.https://doi.org/10.1007/s11069-017-2923-2
Liu,S.F.,Cai,H.,Yang,Y.J.,et al.,2013.Advance in Grey Incidence Analysis Modelling.Systems EngineeringTheory&Practice,33(8):2041-2046(in Chinese with English abstract).
Lu,X.C.,Gong,M.,Tang,L.S.,et al.,2006.Improved Grey Association Analysis Method Based on T-S Fuzzy Neural Network Model-Application in Evaluating of Landslide Hazards in Guangdong Province.The Chinese Journal of Geological Hazard and Control,17(3):143-146(in Chinese with English abstract).
Marjanovi'c,M.,Kovaˇcevi'c,M.,Bajat,B.,et al.,2011.Landslide Susceptibility Assessment Using SVM Machine Learning Algorithm.Engineering Geology,123(3):225-234.https://doi.org/10.1016/j.enggeo.2011.09.006
Peng,L.,Niu,R.Q.,Huang,B.,et al.,2014.Landslide Susceptibility Mapping Based on Rough Set Theory and Support Vector Machines:A Case of the Three Gorges Area,China.Geomorphology,204:287-301.https://doi.org/10.1016/j.geomorph.2013.08.013
Pham,B.T.,Tien Bui,D.,Prakash,I.,et al.,2017.Hybrid Integration of Multilayer Perceptron Neural Networks and Machine Learning Ensembles for Landslide Susceptibility Assessment at Himalayan Area(India)Using GIS.Catena,149:52-63.https://doi.org/10.1016/j.catena.2016.09.007
Pradhan,B.,2013.A Comparative Study on the Predictive Ability of the Decision Tree,Support Vector Machine and Neuro-Fuzzy Models in Landslide Susceptibility Mapping Using GIS.Computers&Geosciences,51:350-365.https://doi.org/10.1016/j.cageo.2012.08.023
Yao,X.,Tham,L.G.,Dai,F.C.,2008.Landslide Susceptibility Mapping Based on Support Vector Machine:A Case Study on Natural Slopes of Hong Kong,China.Geomorphology,101(4):572-582.https://doi.org/10.1016/j.geomorph.2008.02.011
Tsangaratos,P.,Benardos,A.,2014.Estimating Landslide Susceptibility through a Artificial Neural Network Classifier.Natural Hazards,74(3):1489-1516.https://doi.org/10.1007/s11069-014-1245-x
Wang,J.,Guo,J.,Wang,W.D.,et al.,2012.Application and Comparison of Weighted Linear Combination Model and Logistic Regression Model in Landslide Susceptibility Mapping.Journal of Central South University(Science and Technology),43(5):1932-1939(in Chinese with English abstract).
Wang,J.J.,Yin,K.L.,Xiao,L.L.,2014.Landslide Susceptibility Assessment Based on GIS and Weighted Information Value:A Case Study of Wanzhou District,three Gorges Reservoir.Chinese Journal of Rock Mechanics and Engineering,33(4):797-808(in Chinese with English abstract).
Wu,Y.P.,Chen,L.X.,Cheng,C.,et al.,2017.GIS-Based Landslide Hazard Predicting System and Its Real-Time Test during a Typhoon,Zhejiang Province,Southeast China.Engineering Geology,175:9-21.https://doi.org/10.1016/j.enggeo.2014.03.005
Wu,Y.P.,Zhang,Q.X.,Tang,H.M.,et al.,2014.Landslide Hazard Warning Based on Effective Rainfall Intensity.Earth Science,39(7):889-895(in Chinese with English abstract).
Xu,X.H.,Shang,Y.Q.,2009.Integrated Zonation of Landslide Stability.Chinese Journal of Geotechnical Engineering,31(5):669-674(in Chinese with English abstract).
Yu,J.M.,Zhang,X.N.,Xiong,C.L.,2017.A Methodology for Evaluating Micro-Surfacing Treatment on Asphalt Pavement Based on Grey System Models and Grey Rational Degree Theory.Construction and Building Materials,150:214-226.https://doi.org/10.13039/501100005015
Zhu,L.,Lu,Y.M.,Luo,J.P.,2013.Regional Landslide Susceptibility Model Based on Gray and Elman Neural Networks.Journal of Natural Disasters,22(5):120-126(in Chinese with English abstract).
Zhu,X.X.,Wang,Y.M.,Gong,X.F.,2013.Assessment of the Landslide Hazards in Wencheng County of Zhejiang Province.The Chinese Journal of Geological Hazard and Control,24(3):13-18(in Chinese with English abstract).
冯杭建,周爱国,俞剑君,等,2016.浙西梅雨滑坡易发性评价模型对比.地球科学,41(3):403-415.
黄发明,殷坤龙,张桂荣,等,2015.基于相空间重构和小波分析-粒子群向量机的滑坡地下水位预测.地球科学,40(7):1254-1265.
李嘉良,马东辉,王威,2016.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.中南大学学报(自然科学版),47(5):1730-1736.
李秀珍,王成华,邓宏艳,2010.灰色关联度法和距离判别分析法在溪洛渡库区潜在滑坡判识中的应用.中国地质灾害与防治学报,21(4):77-81.
刘思峰,蔡华,杨英杰,等,2013.灰色关联分析模型研究进展.系统工程理论与实践,33(8):2041-2046.
陆显超,龚民,汤连生,等,2006.基于T-S模糊神经系统的灰色关联分析方法---以广东省滑坡危险性评价为例.中国地质灾害与防治学报,17(3):143-146.
王佳佳,殷坤龙,肖莉丽,2014.基于GIS和信息量的滑坡灾害易发性评价---以三峡库区万州区为例.岩石力学与工程学报,33(4):797-808.
王进,郭靖,王卫东,等,2012.权重线性组合与逻辑回归模型在滑坡易发性区划中的应用与比较.中南大学学报(自然科学版),43(5):1932-1939.
吴益平,张秋霞,唐辉明,等,2014.基于有效降雨强度的滑坡灾害危险性预警.地球科学,39(7):889-895.
徐兴华,尚岳全,2009.滑坡稳定性综合区划研究.岩土工程学报,31(5):669-674.
朱莉,卢毅敏,罗建平,2013.基于灰色-Elman神经网络的区域滑坡易发性模型.自然灾害学报,22(5):120-126.
朱晓曦,王一鸣,龚新法,2013.浙江省文成县滑坡灾害危险性评价.中国地质灾害与防治学报,24(3):13-18.
Bui,D.,Pradhan,B.,Lofman,O.,et al.,2012b.Spatial Prediction of Landslide Hazards in Hoa Binh Province(Vietnam):A Comparative Assessment of the Efficacy of Evidential Belief Functions and Fuzzy Logic Models.Catena,96:28-40.https://doi.org/10.1016/j.catena.2012.04.001
Chung,C.J.,Fabbri,A.G.,2008.Predicting Landslides for Risk Analysis-Spatial Models Tested by a CrossValidation Technique.Geomorphology,94(3-4):438-452.https://doi.org/10.1016/j.geomorph.2006.12.036
Deng,J.L.,1989.Introduction to Grey System Theory.Journal of Grey System,1(1):1-24.
Feng,H.J.,Zhou,A.G.,Yu,J.J.,et al.,2016.A Comparative Study on Plum-Rain-Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province.Earth Science,41(3):403-415(in Chinese with English abstract).
Hong,H.Y.,Pradhan,B.,Xu,C.,et al.,2015.Spatial Prediction of Landslide Hazard at the Yihuang Area(China)Using Two-Class Kernel Logistic Regression,Alternating Decision Tree and Support Vector Machines.Catena,133:266-281.https://doi.org/10.1016/j.catena.2015.05.019
Huang,F.M.,Huang,J.S.,Jiang,S.H.,et al.,2017a.Landslide Displacement Prediction Based on Multivariate Chaotic Model and Extreme Learning Machine.Engineering Geology,218:173-186.https://doi.org/10.1016/j.enggeo.2017.01.016
Huang,F.M.,Yin,K.L.,Huang,J.S.,et al.,2017b.Landslide Susceptibility Mapping Based on Self-Organizing-Map Network and Extreme Learning Machine.Engineering Geology,223:11-22.https://doi.org/10.1016/j.enggeo.2017.04.013
Huang,F.M.,Yin,K.L.,Tao,H.,et al.,2016a.Influencing Factor Analysis and Displacement Prediction in Reservoir Landslides-A Case Study of Three Gorges Reservoir(China).Tehnickivjesnik-Technical Gazette,23(2):617-626.https://doi.org/10.17559/tv-20150314105216
Huang,F.M.,Yin,K.L.,Zhang,G.R.,et al.,2016b.Landslide Displacement Prediction Using Discrete Wavelet Transform and Extreme Learning Machine Based on Chaos Theory.Environmental Earth Sciences,75(20):1376.https://doi.org/10.1007/s12665-016-6133-0
Huang,F.M.,Yin,K.L.,Zhang,G.R.,et al.,2015.Landslide Groundwater Level Time Series Prediction Based on Phase Space Reconstruction and Wavelet AnalysisSupport Vector Machine Optimized by PSO Algorithm.Earth Science,40(7):1254-1265(in Chinese with English abstract).
Huang,W.D.,Yang,J.S.,Chen,J.C.,et al.,2010.Grey Relational Analysis of Physico-Chemical Variables and Water Body Reflectance of a Chi-Chi Earthquake Landslide-Dammed Lake Monitored by Spot Satellite.Journal of Grey System,22(4):297-308.
Kavzoglu,T.,Sahin,E.K.,Colkesen,I.,2014.Landslide Susceptibility Mapping Using GIS-Based Multi-Criteria Decision Analysis,Support Vector Machines,and Logistic Regression.Landslides,11(3):425-439.https://doi.org/10.1007/s10346-013-0391-7
Lee,S.,Ryu,J.H.,Kim,I.S.,2007.Landslide Susceptibility Analysis and Its Verification Using Likelihood Ratio,Logistic Regression,and Artificial Neural Network Models:Case Study of Youngin,Korea.Landslides,4(4):327-338.https://doi.org/10.1007/s10346-007-0088-x
Li,J.L.,Ma,D.H.,Wang,W.,2016.Assessment of Potential Seismic Landslide Hazard Based on Evidence Theory and Entropy Weight Grey Incidence.Journal of Central South University(Science and Technology),47(5):1730-1736(in Chinese with English abstract).
Li,X.Z.,Wang,C.H.,Deng,H.Y.,2010.Application of Grey Relation Analysis and Distance Discrimination Analysis Methods in Discriminating Potential Landslides of Xiluodu Reservoir Area.The Chinese Journal of Geological Hazard and Control,21(4):77-81(in Chinese with English abstract).
Li,Y.Y.,Huang,J.S.,Jiang,S.H.,et al.,2017.A Web-Based GPSSystem for Displacement Monitoring and Failure Mechanism Analysis of Reservoir Landslide.Scientific Reports,7(1).https://doi.org/10.1038/s41598-017-17507-7
Liu,Y.L.,Huang,X.L.,Duan,J.,et al.,2017.The Assessment of Traffic Accident Risk Based on Grey Relational Analysis and Fuzzy Comprehensive Evaluation Method.Natural Hazards,88(3):1409-1422.https://doi.org/10.1007/s11069-017-2923-2
Liu,S.F.,Cai,H.,Yang,Y.J.,et al.,2013.Advance in Grey Incidence Analysis Modelling.Systems EngineeringTheory&Practice,33(8):2041-2046(in Chinese with English abstract).
Lu,X.C.,Gong,M.,Tang,L.S.,et al.,2006.Improved Grey Association Analysis Method Based on T-S Fuzzy Neural Network Model-Application in Evaluating of Landslide Hazards in Guangdong Province.The Chinese Journal of Geological Hazard and Control,17(3):143-146(in Chinese with English abstract).
Marjanovi'c,M.,Kovaˇcevi'c,M.,Bajat,B.,et al.,2011.Landslide Susceptibility Assessment Using SVM Machine Learning Algorithm.Engineering Geology,123(3):225-234.https://doi.org/10.1016/j.enggeo.2011.09.006
Peng,L.,Niu,R.Q.,Huang,B.,et al.,2014.Landslide Susceptibility Mapping Based on Rough Set Theory and Support Vector Machines:A Case of the Three Gorges Area,China.Geomorphology,204:287-301.https://doi.org/10.1016/j.geomorph.2013.08.013
Pham,B.T.,Tien Bui,D.,Prakash,I.,et al.,2017.Hybrid Integration of Multilayer Perceptron Neural Networks and Machine Learning Ensembles for Landslide Susceptibility Assessment at Himalayan Area(India)Using GIS.Catena,149:52-63.https://doi.org/10.1016/j.catena.2016.09.007
Pradhan,B.,2013.A Comparative Study on the Predictive Ability of the Decision Tree,Support Vector Machine and Neuro-Fuzzy Models in Landslide Susceptibility Mapping Using GIS.Computers&Geosciences,51:350-365.https://doi.org/10.1016/j.cageo.2012.08.023
Yao,X.,Tham,L.G.,Dai,F.C.,2008.Landslide Susceptibility Mapping Based on Support Vector Machine:A Case Study on Natural Slopes of Hong Kong,China.Geomorphology,101(4):572-582.https://doi.org/10.1016/j.geomorph.2008.02.011
Tsangaratos,P.,Benardos,A.,2014.Estimating Landslide Susceptibility through a Artificial Neural Network Classifier.Natural Hazards,74(3):1489-1516.https://doi.org/10.1007/s11069-014-1245-x
Wang,J.,Guo,J.,Wang,W.D.,et al.,2012.Application and Comparison of Weighted Linear Combination Model and Logistic Regression Model in Landslide Susceptibility Mapping.Journal of Central South University(Science and Technology),43(5):1932-1939(in Chinese with English abstract).
Wang,J.J.,Yin,K.L.,Xiao,L.L.,2014.Landslide Susceptibility Assessment Based on GIS and Weighted Information Value:A Case Study of Wanzhou District,three Gorges Reservoir.Chinese Journal of Rock Mechanics and Engineering,33(4):797-808(in Chinese with English abstract).
Wu,Y.P.,Chen,L.X.,Cheng,C.,et al.,2017.GIS-Based Landslide Hazard Predicting System and Its Real-Time Test during a Typhoon,Zhejiang Province,Southeast China.Engineering Geology,175:9-21.https://doi.org/10.1016/j.enggeo.2014.03.005
Wu,Y.P.,Zhang,Q.X.,Tang,H.M.,et al.,2014.Landslide Hazard Warning Based on Effective Rainfall Intensity.Earth Science,39(7):889-895(in Chinese with English abstract).
Xu,X.H.,Shang,Y.Q.,2009.Integrated Zonation of Landslide Stability.Chinese Journal of Geotechnical Engineering,31(5):669-674(in Chinese with English abstract).
Yu,J.M.,Zhang,X.N.,Xiong,C.L.,2017.A Methodology for Evaluating Micro-Surfacing Treatment on Asphalt Pavement Based on Grey System Models and Grey Rational Degree Theory.Construction and Building Materials,150:214-226.https://doi.org/10.13039/501100005015
Zhu,L.,Lu,Y.M.,Luo,J.P.,2013.Regional Landslide Susceptibility Model Based on Gray and Elman Neural Networks.Journal of Natural Disasters,22(5):120-126(in Chinese with English abstract).
Zhu,X.X.,Wang,Y.M.,Gong,X.F.,2013.Assessment of the Landslide Hazards in Wencheng County of Zhejiang Province.The Chinese Journal of Geological Hazard and Control,24(3):13-18(in Chinese with English abstract).
冯杭建,周爱国,俞剑君,等,2016.浙西梅雨滑坡易发性评价模型对比.地球科学,41(3):403-415.
黄发明,殷坤龙,张桂荣,等,2015.基于相空间重构和小波分析-粒子群向量机的滑坡地下水位预测.地球科学,40(7):1254-1265.
李嘉良,马东辉,王威,2016.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.中南大学学报(自然科学版),47(5):1730-1736.
李秀珍,王成华,邓宏艳,2010.灰色关联度法和距离判别分析法在溪洛渡库区潜在滑坡判识中的应用.中国地质灾害与防治学报,21(4):77-81.
刘思峰,蔡华,杨英杰,等,2013.灰色关联分析模型研究进展.系统工程理论与实践,33(8):2041-2046.
陆显超,龚民,汤连生,等,2006.基于T-S模糊神经系统的灰色关联分析方法---以广东省滑坡危险性评价为例.中国地质灾害与防治学报,17(3):143-146.
王佳佳,殷坤龙,肖莉丽,2014.基于GIS和信息量的滑坡灾害易发性评价---以三峡库区万州区为例.岩石力学与工程学报,33(4):797-808.
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