PDF DownloadHTML ]" id="html" rel="external">HTML
[1]ZHAO Dongmei,JIAO Yuanmei,QIU Yingmei,et al.Assessment on Landslide Susceptibility of the Core Area of Hani Race Terraces Heritage Site Maximum Entropy Model[J].Research of Soil and Water Conservation,2020,27(04):392-399.
Copy

Assessment on Landslide Susceptibility of the Core Area of Hani Race Terraces Heritage Site Maximum Entropy Model

References:
[1] 孙小凡,张鹏,党超.基于GIS的城市滑坡灾害易发性评价:以湖北省宜昌市城区为例[J].水土保持通报,2019,38(6): 304-309.
[2] Guzzetti F, Carrara A, Cardinali M, et al. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy[J]. Geomorphology, 1999,31(1/4):181-216.
[3] Pradhan A M S, Kim Y T. Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping[J]. Catena, 2016,140:125-139.
[4] Reichenbach P, Galli M, Cardinali M, et al. Geomorphologic mapping to assess landslide risk: concepts, methods and applications in the Umbria Region of central Italy[M]∥Glade T, Anderson M G, Crozier M J. Landslide Risk Assessment,2005.
[5] Bui D T, Nguyen Q P, Hoang N D, et al. A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS[J]. Landslides, 2017,14(1):1-17.
[6] 樊芷吟,苟晓峰,秦明月,等.基于信息量模型与Logistic回归模型耦合的地质灾害易发性评价[J].工程地质学报,2018,26(2):340-347.
[7] He S, Pan P, Dai L, et al. Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China[J]. Geomorphology, 2012,171:30-41.
[8] 郭子正,殷坤龙,付圣,等.基于GIS与WOE-BP模型的滑坡易发性评价[J].地球科学,2019,44(12):4299-4312.
[9] 王佳佳,殷坤龙,肖莉丽.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[J].岩石力学与工程学报,2014,33(4):797-808.
[10] Reichenbach P, Rossi M, Malamud B D, et al. A review of statistically-based landslide susceptibility models[J]. Earth-Science Reviews, 2018,180:60-91.
[11] Dickson M E, Perry G L W. Identifying the controls on coastal cliff landslides using machine-learning approaches[J]. Environmental Modelling & Software, 2016,76:117-127.
[12] 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):1085-1091.
[13] 李雪平,唐辉明.贝叶斯信息标准在滑坡因子敏感性分析中的应用[J].岩土力学,2006,27(8):1393-1397.
[14] 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.
[15] 冯杭建,周爱国,俞剑君,等.浙西梅雨滑坡易发性评价模型对比[J].地球科学,2016,41(3):403-415.
[16] Phillips S J, Anderson R P, Schapire R E. Maximum entropy modeling of species geographic distributions[J]. Ecological modelling, 2006,190(3/4): 231-259.
[17] Chen W, Pourghasemi H R, Kornejady A, et al. Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques[J]. Geoderma, 2017,305:314-327.
[18] Felicísimo Á M, Cuartero A, Remondo J, et al. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study[J]. Landslides, 2013,10(2):175-189.
[19] Kornejady A, Ownegh M, Bahremand A. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods[J]. Catena, 2017,152:144-162.
[20] Jaynes E T. Where Do We Stand on Maximum Entropy[R]∥ Justice J H. Maximum Entropy and Bayesian Methods in Applied Statistics, Cambridge UK: Cambridge University Press,1978.
[21] Elith J, Phillips S J, Hastie T, et al. A statistical explanation of MaxEnt for ecologists[J]. Diversity and Distributions, 2011,17(1):43-57.
[22] Pham B T, Prakash I, Singh S K, et al. Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: Hybrid machine learning approaches[J]. Catena, 2019,175:203-218.
[23] Jiao Y, Zhao D, Ding Y, et al. Performance evaluation for four GIS-based models purposed to predict and map landslide susceptibility: A case study at a World Heritage site in Southwest China[J]. Catena, 2019,183.DOI:10.1016/j.catena.2019.104221.
Similar References:

Memo

-

Last Update: 2020-07-09

Online:3766       Total Traffic Statistics:27352980

Website Copyright: Research of Soil and Water Conservation Shaanxi ICP No.11014090-10
Tel: 029-87012705 Address: Editorial Department of Research of Soil and Water Conservation, No. 26, Xinong Road, Yangling, Shaanxi Postcode: 712100