Citation: | CHEN Tao, ZHONG Ziying, NIU Ruiqing, LIU Tong, CHEN Shengyun. Mapping Landslide Susceptibility Based on Deep Belief Network[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1809-1817. DOI: 10.13203/j.whugis20190144 |
[1] |
黄润秋, 许强.中国典型灾难性滑坡[M].北京:科学出版社, 2008
Huang Runqiu, Xu Qiang. China Typical Catastrophic Landslide[M]. Beijing: Science Press, 2008
|
[2] |
陆会燕, 李为乐, 许强, 等.光学遥感与InSAR结合的金沙江白格滑坡上下游滑坡隐患早期识别[J].武汉大学学报·信息科学版, 2019, 44(9): 1 342-1 354 doi: 10.13203/j.whugis20190086
Lu Huiyan, Li Weile, Xu Qiang, et al. Early Detection of Landslides in the Upstream and Downstream Areas of the Baige Landslide, the Jinsha River Based on Optical Remote Sensing and InSAR Technologies[J]. Geomatics and Information Science of Wuhan University, 2019, 44(9): 1 342-1 354 doi: 10.13203/j.whugis20190086
|
[3] |
唐尧, 王立娟, 马国超, 等.基于"高分+"的金沙江滑坡灾情监测与应用前景分析[J].武汉大学学报·信息科学版, 2019, 44(7):1 082-1 092 doi: 10.13203/j.whugis20190048
Tang Yao, Wang Lijuan, Ma Guochao, et al. Disaster Monitoring and Application Prospect Analysis of the Jinsha River Landslide Based on "Gaofen+"[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 1 082-1 092 doi: 10.13203/j.whugis20190048
|
[4] |
刘渊博, 牛瑞卿, 于宪煜, 等.旋转森林模型在滑坡易发性评价中的应用研究[J].武汉大学学报·信息科学版, 2018, 43(6): 959-964 doi: 10.13203/j.whugis20160132
Liu Yuanbo, Niu Ruiqing, Yu Xianyu, et al. Application of the Rotation Forest Model in Landslide Susceptibility Assessment[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 959-964 doi: 10.13203/j.whugis20160132
|
[5] |
Chen W, Xie X, Peng J, et al. GIS-Based Landslide Susceptibility Evaluation Using a Novel Hybrid Integration Approach of Bivariate Statistical Based Random Forest Method[J]. Catena, 2018, 164: 135-149 doi: 10.1016/j.catena.2018.01.012
|
[6] |
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): 1 085-1 091 doi: 10.13203/j.whugis20160515
Liu Jian, Li Shulin, Chen Tao.Landslide Susceptibility Assessment Based on Optimized Random Forest Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1 085-1 091 doi: 10.13203/j.whugis20160515
|
[7] |
Pham B T, Bui D T, Dholakia M B, et al. A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area[J]. Geotechnical and Geological Engineering, 2016, 34(6): 1 807-1 824 doi: 10.1007/s10706-016-9990-0
|
[8] |
Shirzadi A, Bui D T, Pham B T, et al. Shallow Landslide Susceptibility Assessment Using a Novel Hybrid Intelligence Approach[J]. Environmental Earth Sciences, 2017, DOI: 10.1007/s12665-017-6558-0
|
[9] |
Tsangaratos P, Ilia I. Comparison of a Logistic Regression and Naïve Bayes Classifier in Landslide Susceptibility Assessments: The Influence of Models Complexity and Training Dataset Size[J]. Catena, 2016, 145: 164-179 doi: 10.1016/j.catena.2016.06.004
|
[10] |
Chen W, Xie X, Peng J, et al. GIS-Based Landslide Susceptibility Modelling: A Comparative Assessment of Kernel Logistic Regression, Naïve-Bayes Tree, and Alternating Decision Tree Models[J]. Geomatics, Natural Hazards and Risk, 2017, 8(2): 950-973 doi: 10.1080/19475705.2017.1289250
|
[11] |
Wang L J, Guo M, Sawada K, et al. A Comparative Study of Landslide Susceptibility Maps Using Logistic Regression, Frequency Ratio, Decision Tree, Weights of Evidence and Artificial Neural Network[J]. Geosciences Journal, 2016, 20: 117-136 doi: 10.1007/s12303-015-0026-1
|
[12] |
Bui D T, Tuan T A, Klempe H, et al. Spatial Prediction Models for Shallow Landslide Hazards: A Comparative Assessment of the Efficacy of Support Vector Machines, Artificial Neural Networks, Kernel Logistic Regression, and Logistic Model Tree[J]. Landslides, 2016, 13(2): 361-378 doi: 10.1007/s10346-015-0557-6
|
[13] |
Hong H, Pradhan B, Jebur M N, et al. Spatial Prediction of Landslide Hazard at the Luxi Area (China) Using Support Vector Machines[J]. Environmental Earth Sciences, 2016, DOI: 10.1007/s12665-015-4866-9
|
[14] |
Peng L, Niu R, Huang B, et al. Landslide Susceptibility Mapping Based on Rough Set Theory and Support Vector Machines: A Case of the Three Gorges Area, China[J]. Geomorphology, 2014, 204: 287-301 doi: 10.1016/j.geomorph.2013.08.013
|
[15] |
Wu X, Niu R, Ren F, et al. Landslide Susceptibility Mapping Using Rough Sets and Back-Propagation Neural Networks in the Three Gorges, China[J]. Environmental Earth Sciences, 2013, 70(3): 1 307-1 318 doi: 10.1007/s12665-013-2217-2
|
[16] |
Chen W, Pourghasemi H R, Zhao Z. A GIS-Based Comparative Study of Dempster-Shafer, Logistic Regression, and Artificial Neural Network Models for Landslide Susceptibility Mapping[J]. Geocarto International, 2017, 32(4): 367-385 doi: 10.1080/10106049.2016.1140824
|
[17] |
Tian Y Y, Xu C, Hong H Y, et al. Mapping Earthquake-Triggered Landslide Susceptibility by Use of Artificial Neural Network(ANN) Models:An Example of the 2013 Minxian (China) Mw 5.9 Event[J]. Geomatics, Natural Hazards and Risk, 2019, 10(1): 1-25 doi: 10.1080/19475705.2018.1487471
|
[18] |
Andrieu C, Freitas N D, Doucet A, et al. An Introduction to MCMC for Machine Learning[J]. Machine Learning, 2003, 50(1-2): 5-43 http://www.tandfonline.com/servlet/linkout?suffix=cit0002&dbid=16&doi=10.1080%2F00273171.2018.1428892&key=10.1023%2FA%3A1020281327116
|
[19] |
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 doi: 10.1016/j.scitotenv.2019.02.263
|
[20] |
Hinton G E, Salakhutdinov R R. Reducing the Dimensionality of Data with Neural Networks[J].Science, 2006, 313(5 786): 504-507 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4bf28668b4ac9aa4374f7393e37d2e9d
|
[21] |
Lee H, Ekanadham C, Ng A Y. Sparse Deep Belief Net Model for Visual Area V2[C]. The 21st Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2007
|
[22] |
Nair V, Hinton G E. 3D Object Recognition with Deep Belief Nets[C]. The 23rd Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2009
|
[23] |
Sarikaya R, Hinton G E, Ramabhadran B. Deep Belief Nets for Natural Language Call-Routing[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, Czech, Republic, 2011
|
[24] |
李景富, 牛瑞卿.基于GIS的斜坡结构图自动化制图方法研究[J].人民长江, 2009, 40(19): 38-40 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=rmcj200919013
Li Jingfu, Niu Ruiqing. Research on Automatic Drawing Method for Structural Drawings of Slope Based on GIS[J]. Yangtze River, 2009, 40(19): 38-40 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=rmcj200919013
|
[25] |
地质矿产部编写组.长江三峡工程库岸稳定性研究[M].北京:地质出版社, 1988
Compilation Team of Ministry of Geology and Mineral Resources. Unstability Problems of the Slope Along Yangtze River in the Region of the Three Gorges Project[M]. Beijing: Geological Publishing House, 1988
|
[26] |
《长江三峡工程重大地质与地震问题研究》编写组.长江三峡工程重大地质与地震问题研究[M].北京:地质出版社, 1992
Compilation Team of Research on Major Geological and Seismic Problems of the Three Gorges Project of the Yangtze River. Research on Major Geological and Seismic Problems of the Three Gorges Project of the Yangtze River[M]. Beijing: Geological Publishing House, 1992
|
[27] |
刘广润, 晏鄂川, 练操.论滑坡分类[J].工程地质学报, 2002, 10(4): 339-342 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb200204001
Liu Guangrun, Yan Echuan, Nian Chao. Discussion on Classification of Landslides[J]. Journal of Engineering Geology, 2002, 10(4): 339-342 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb200204001
|
[28] |
Páez A, Wheeler D C. Geographically Weighted Regression[J]. International Encyclopedia of Human Geography, 2009, 47(1): 407-414
|
[29] |
Pike R J. The Geometric Signature: Quantifying Landslide-Terrain Types from Digital Elevation Models[J]. Mathematical Geology, 1988, 20(5): 491-511 doi: 10.1007/BF00890333
|
[30] |
Montgomery D R, Dietrich W E. A Physically Based Model for the Topographic Control on Shallow Landsliding[J]. Water Resources Research, 1994, 30(4): 1 153-1 171 doi: 10.1029/93WR02979
|
[31] |
Chowdhury R N, Zhang S. Modelling the Risk of Progressive Slope Failure: A New Approach[J]. Reliability Engineering & System Safety, 1993, 40(1): 17-30 http://www.sciencedirect.com/science/article/pii/095183209390115F
|
[32] |
彭令, 牛瑞卿, 陈丽霞. GIS支持下三峡库区秭归县滑坡灾害空间预测[J].地理研究, 2010, 29(10): 1 889-1 898 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlyj201010017
Peng Ling, Niu Ruiqing, Chen Lixia. Landslide Hazard Spatial Prediction in Zigui County of the Three Gorges Reservoir Area Based on GIS[J]. Geographical Research, 2010, 29(10): 1 889-1 898 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlyj201010017
|
[33] |
李军, 周成虎.基于栅格GIS滑坡风险评价方法中格网大小选取分析[J].遥感学报, 2003, 7(2): 86-92 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ygxb200302002
Li Jun, Zhou Chenghu. Appropriate Grid Size for Terrain Based Landslide Risk Assessment in Lantau Island, Hong Kong[J]. Journal of Remote Sensing, 2003, 7(2): 86-92 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ygxb200302002
|