1. 南昌大学工程建设学院
2. 三峡大学三峡库区地质灾害教育部重点实验室
纸质出版:2025
移动端阅览
黄发明, 陈杰, 杨阳, 等. 滑坡易发性相关致灾环境因子研究的综述与展望[J]. 地质科技通报, 2025,44(2):14-37.
黄发明, 陈杰, 杨阳, et al. 滑坡易发性相关致灾环境因子研究的综述与展望[J]. Bulletin of Geological Science and Technology, 2025, 44(2): 14-37.
黄发明, 陈杰, 杨阳, 等. 滑坡易发性相关致灾环境因子研究的综述与展望[J]. 地质科技通报, 2025,44(2):14-37. DOI: 10.19509/j.cnki.dzkq.tb20240766.
黄发明, 陈杰, 杨阳, et al. 滑坡易发性相关致灾环境因子研究的综述与展望[J]. Bulletin of Geological Science and Technology, 2025, 44(2): 14-37. DOI: 10.19509/j.cnki.dzkq.tb20240766.
致灾环境因子是滑坡易发性预测建模的输入变量,是指影响滑坡发生、发展和分布的各种边坡自然属性因子。类型齐全且意义明确的致灾环境因子,对提高滑坡易发性结果的准确性和可靠性至关重要。为了进一步明确致灾环境因子的研究现状和未来展望,本研究在Web of Science的核心合集数据库中进行了文献检索,标题中包含“landslide susceptibility”,出版日期范围从20130101-20231231,收集了767篇滑坡易发性英文论文构成文献数据库。首先统计每篇文献中致灾环境因子数量、获取方法、来源、重要性和认可度等信息,然后详述了致灾环境因子的定义和物理意义;之后对致灾环境因子的优化选取/组合方法、因子联接方法、因子误差及其适宜性等特征进行了讨论,为后续预测滑坡易发性时选取致灾环境因子的不确定性研究提供参考。综述结果表明:(1)文献数据库中共统计出82种致灾环境因子,使用频率较高的因子有40余种,其中坡度、坡向、高程、岩性是使用频率最高的4个因子,坡度、高程、公路密度、岩性、降雨等因子在滑坡易发性预测中的重要性依次增高;(2)发现采用类型齐全且物理意义明确的致灾环境因子、基于环境因子联接方法来构建模型输入变量、消除环境因子中的随机误差、提升环境因子的适宜性和内在可解释性等研究有利于大幅提升机器学习方法预测滑坡易发性的性能,在未来研究中需要重点关注这些关键问题。
石菊松.基于遥感和地理信息系统的滑坡风险评估关键技术研究[D].中国地质科学院,2008(03).
张锦瑞,汪洋,冯霄,李远耀,金必晶,周超,张鑫,邓扬.考虑地表形变和土地利用变更的滑坡时空易发性差异分析[J].地质科技通报,2024(06).
黄发明,吴敦筱,常志璐,陈茜,陶杰,蒋水华,周创兵.滑坡样本缺失下易发性规律和潜在滑坡识别的易发性–InSAR多源信息法[J].岩石力学与工程学报,2025(03).
崔玉龙,朱路路,徐敏,缪海波.基于环境因子优化TSES法选择负样本及其在滑坡易发性评价中的应用[J].地质科技通报,2024(03).
黄发明,欧阳慰平,蒋水华,范宣梅,连志鹏,周创兵.考虑机器学习建模中训练/测试集时空划分原则的滑坡易发性预测建模[J].地球科学,2024(05).
王启盛,熊俊楠,程维明,崔兴洁,庞全,刘俊,陈文杰,唐浩然,宋南霄.耦合统计方法、机器学习模型和聚类算法的滑坡易发性评价方法[J].地球信息科学学报,2024(03).
曹文庚,潘登,徐郅杰,张文培,任宇,南天.河南省滑坡灾害易发性制图研究:多种机器学习模型的对比[J].地质科技通报,2025(01).
黄发明,刘科技,曾子强,田钦,蒋水华,杨阳,周创兵.环境因子筛选及组合方法对滑坡易发性预测的影响规律[J].应用基础与工程科学学报,2024(01).
郭飞,赖鹏,黄发明,刘磊磊,王秀娟,何政宇.基于知识图谱的滑坡易发性评价文献综述及研究进展[J].地球科学,2024(05).
郭衍昊,窦杰,向子林,马豪,董傲男,罗万祺.基于优化负样本采样策略的梯度提升决策树与随机森林的汶川同震滑坡易发性评价[J].地质科技通报,2024(03).
孔嘉旭,庄建琦,彭建兵,占洁伟,马鹏辉,牟家琦,王杰,王世宝,郑佳,付玉婷.基于信息量和卷积神经网络的黄土高原滑坡易发性评价[J].地球科学,2023(05).
孙德亮,陈丹璐,密长林,陈星宇,密士文,李晓琴.基于随机森林-特征递归消除模型的可解释性缓丘岭谷地貌滑坡易发性评价[J].地质力学学报,2023(02).
常志璐,黄发明,蒋水华,张崟琅,周创兵,黄劲松.基于多尺度分割方法的斜坡单元划分及滑坡易发性预测[J].工程科学与技术,2023(01).
窦杰,向子林,许强,郑鹏麟,王协康,苏爱军,刘军旗,罗万祺.机器学习在滑坡智能防灾减灾中的应用与发展趋势[J].地球科学,2023(05).
黄发明,李金凤,王俊宇,毛达雄,盛明强.考虑线状环境因子适宜性和不同机器学习模型的滑坡易发性预测建模规律[J].地质科技通报,2022(02).
黄发明,石雨,欧阳慰平,洪安宇,曾子强,徐富刚.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(05).
黄发明,曹昱,范宣梅,李文彬,黄劲松,周创兵,范文彦.不同滑坡边界及其空间形状对滑坡易发性预测不确定性的影响规律[J].岩石力学与工程学报,2021(S2).
李文彬,范宣梅,黄发明,武雪玲,殷坤龙,常志璐.不同环境因子联接和预测模型的滑坡易发性建模不确定性[J].地球科学,2021(10).
黄发明,胡松雁,闫学涯,李明,王俊宇,李文彬,郭子正,范文彦.基于机器学习的滑坡易发性预测建模及其主控因子识别[J].地质科技通报,2022(02).
Xinzhi Zhou,Haijia Wen,Yalan Zhang,Jiahui Xu,Wengang Zhang.Landslide susceptibility mapping using hybrid random forest with Geo Detector and RFE for factor optimization[J].Geoscience Frontiers,2021(05).
罗路广,裴向军,崔圣华,黄润秋,朱凌,何智浩.九寨沟地震滑坡易发性评价因子组合选取研究[J].岩石力学与工程学报,2021(11).
吴雨辰,周晗旭,车爱兰.基于粗糙集–神经网络的IBURI地震滑坡易发性研究[J].岩石力学与工程学报,2021(06).
Wei Chen,Xi Chen,Jianbing Peng,Mahdi Panahi,Saro Lee.Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer[J].Geoscience Frontiers,2021(01).
田乃满,兰恒星,伍宇明,李郎平.人工神经网络和决策树模型在滑坡易发性分析中的性能对比[J].地球信息科学学报,2020(12).
刘瑞,李露瑶,杨鑫,杨远陶,杨梅.基于因子强关联分析方法的滑坡易发性评价[J].地球与环境,2021(02).
徐胜华,刘纪平,王想红,张玉,林荣福,张蒙,刘猛猛,姜涛.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(08).
张玘恺,凌斯祥,李晓宁,孙春卫,徐建祥,黄涛.九寨沟县滑坡灾害易发性快速评估模型对比研究[J].岩石力学与工程学报,2020(08).
朱阿兴,闾国年,周成虎,秦承志.地理相似性:地理学的第三定律?[J].地球信息科学学报,2020(04).
罗路广,裴向军,黄润秋,裴钻,朱凌.GIS支持下CF与Logistic回归模型耦合的九寨沟景区滑坡易发性评价[J].工程地质学报,2021(02).
Alireza ARABAMERI,Biswajeet PRADHAN,Khalil REZAE,Masoud SOHRABI,Zahra KALANTARI.GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms[J].Journal of Mountain Science,2019(03).
崔鸿雁,徐帅,张利锋,Roy E.Welsch,Berthold K.P.Horn.机器学习中的特征选择方法研究及展望[J].北京邮电大学学报,2018(01).
范强,巨能攀,向喜琼,黄健.证据权法在区域滑坡危险性评价中的应用——以贵州省为例[J].工程地质学报,2014(03).
Fancheng Zhao,Fasheng Miao,Yiping Wu,Shunqi Gong,Guyue Zheng,Jing Yang,Weiwei Zhan.Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change.[J].The Science of the total environment,2024.
Faming Huang,Haowen Xiong,Shui Hua Jiang,Chi Yao,Xuanmei Fan,Filippo Catani,Zhilu Chang,Xiaoting Zhou,Jinsong Huang,Keji Liu.Modelling landslide susceptibility prediction: A review and construction of semi-supervised imbalanced theory[J].Earth-Science Reviews,2024.
Jiani Wang,Yunqi Wang,Cheng Li,Yaoming Li,Haimei Qi.Landslide susceptibility evaluation based on landslide classification and ANN-NFR modelling in the Three Gorges Reservoir area, China[J].Ecological Indicators,2024.
Al-Najjar Husam A.H.,Pradhan Biswajeet,Beydoun Ghassan,Sarkar Raju,Park Hyuck-Jin,Alamri Adbullah.A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset[J].Gondwana Research,2023.
Yang Can,Liu Lei-Lei,Huang Faming,Huang Lei,Wang Xiao-Mi.Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples[J].Gondwana Research,2023.
Sun Deliang,Gu Qingyu,Wen Haijia,Xu Jiahui,Zhang Yalan,Shi Shuxian,Xue Mengmeng,Zhou Xinzhi.Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization[J].Gondwana Research,2023.
Wang He,Yang Tianhong,Zhang Penghai,Liu Feiyue,Liu Honglei,Niu Peng.Landslide susceptibility prediction considering rock integrity and stress state: a case study[J].Bulletin of Engineering Geology and the Environment,2023.
Youssef K.,Shao K.,Moon S.,Bouchard L. S..Landslide susceptibility modeling by interpretable neural network[J].Communications Earth & Environment,2023.
Chen Wei,Yang Zifan.Landslide susceptibility modeling using bivariate statistical-based logistic regression, naïve Bayes, and alternating decision tree models[J].Bulletin of Engineering Geology and the Environment,2023.
Dai Lanxin,Fan Xuanmei,Wang Xin,Fang Chengyong,Zou Chengbin,Tang Xiaochuan,Wei Zhenlei,Xia Mingyao,Wang Dan,Xu Qiang.Coseismic landslides triggered by the 2022 Luding Ms6.8 earthquake, China[J].Landslides,2023.
Pacheco Quevedo Renata,Velastegui Montoya Andrés,Montalván Burbano Néstor,Morante Carballo Fernando,Korup Oliver,Daleles Rennó Camilo.Land use and land cover as a conditioning factor in landslide susceptibility: a literature review[J].Landslides,2023.
Zhang Junyi,Ma Xianglong,Zhang Jialan,Sun Deliang,Zhou Xinzhi,Mi Changlin,Wen Haijia.Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model.[J].Journal of environmental management,2023.
Casagli Nicola,Intrieri Emanuele,Tofani Veronica,Gigli Giovanni,Raspini Federico.Landslide detection, monitoring and prediction with remote-sensing techniques[J].Nature Reviews Earth & Environment,2023.
Grabowski Dariusz,Laskowicz Izabela,Małka Anna,Rubinkiewicz Jacek.Geoenvironmental conditioning of landsliding in river valleys of lowland regions and its significance in landslide susceptibility assessment: A case study in the Lower Vistula Valley, Northern Poland[J].Geomorphology,2022.
Maloney Kelly O.,Buchanan Claire,Jepsen Rikke D.,Krause Kevin P.,Cashman Matthew J.,Gressler Benjamin P.,Young John A.,Schmid Matthias.Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA[J].Journal of Environmental Management,2022.
Liao Mingyong,Wen Haijia,Yang Ling.Identifying the essential conditioning factors of landslide susceptibility models under different grid resolutions using hybrid machine learning: A case of Wushan and Wuxi counties, China[J].Catena,2022.
Huang Faming,Tao Siyu,Li Deying,Lian Zhipeng,Catani Filippo,Huang Jinsong,Li Kailong,Zhang Chuhong.Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies[J].Remote Sensing,2022.
Ekmekcioğlu Ömer,Koc Kerim.Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards[J].Catena,2022.
Bag Rakhohori,Mondal Ismail,Dehbozorgi Mahroo,Bank Subhra Pratim,Das Dipendra Nath,Bandyopadhyay Jatisankar,Pham Quoc Bao,Fadhil Al-Quraishi Ayad M.,Nguyen Xuan Cuong.Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment[J].Journal of Cleaner Production,2022.
Liu Lei Lei,Zhang Yi Li,Xiao Ting,Yang Can.A frequency ratio–based sampling strategy for landslide susceptibility assessment[J].Bulletin of Engineering Geology and the Environment,2022.
Huang Faming,Chen Jiawu,Liu Weiping,Huang Jinsong,Hong Haoyuan,Chen Wei.Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold[J].Geomorphology,2022.
Liu Qiang,Tang Aiping,Huang Ziyuan,Sun Lixin,Han Xiaosheng.Discussion on the tree-based machine learning model in the study of landslide susceptibility[J].Natural Hazards,2022.
Hakim Wahyu Luqmanul,Rezaie Fatemeh,Nur Arip Syaripudin,Panahi Mahdi,Khosravi Khabat,Lee Chang-Wook,Lee Saro.Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea[J].Journal of Environmental Management,2022.
Panahi Mahdi,Rahmati Omid,Rezaie Fatemeh,Lee Saro,Mohammadi Farnoush,Conoscenti Christian.Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates[J].Catena,2022.
Pardo Igúzquiza E.,Dowd P.A..The mapping of closed depressions and its contribution to the geodiversity inventory[J].International Journal of Geoheritage and Parks,2021.
Pham Binh Thai,Van Dao Dong,Acharya Tri Dev,Van Phong Tran,Costache Romulus,Van Le Hiep,Nguyen Hanh Bich Thi,Prakash Indra.Performance assessment of artificial neural network using chi-square and backward elimination feature selection methods for landslide susceptibility analysis[J].Environmental Earth Sciences,2021.
Zou Qiang,Jiang Hu,Cui Peng,Zhou Bin,Jiang Yao,Qin Mingyue,Liu Yanguo,Li Cong.A new approach to assess landslide susceptibility based on slope failure mechanisms[J].Catena,2021.
Jiang Chengcheng,Fan Wen,Yu Ningyu,Liu Enlong.Spatial modeling of gully head erosion on the Loess Plateau using a certainty factor and random forest model[J].Science of the Total Environment,2021.
Lima, Pedro,Steger, Stefan,Glade, Thomas.Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria[J].Landslides,2021.
Tanyu Burak F.,Abbaspour Aiyoub,Alimohammadlou Yashar,Tecuci Gheorghe.Landslide susceptibility analyses using Random Forest, C4.5, and C5.0 with balanced and unbalanced datasets[J].Catena,2021.
Huang Faming,Ye Zhou,Jiang Shui-Hua,Huang Jinsong,Chang Zhilu,Chen Jiawu.Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models[J].Catena,2021.
Libo Cheng,Jia Li,Ping Duan,Mingguo Wang.A small attentional YOLO model for landslide detection from satellite remote sensing images[J].Landslides,2021.
Ionut Cristi Nicu,Luigi Lombardo,Lena Rubensdotter.Preliminary assessment of thaw slump hazard to Arctic cultural heritage in Nordenskiöld Land, Svalbard[J].Landslides,2021.
Yongguang Zhang,Mirco Migliavacca,Josep Penuelas,Weimin Ju.Advances in hyperspectral remote sensing of vegetation traits and functions[J].Remote Sensing of Environment,2021.
Wei Chen,Yang Li.GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models[J].Catena,2020.
Goyes Peñafiel Paul,Hernandez Rojas Alejandra.Landslide susceptibility index based on the integration of logistic regression and weights of evidence: A case study in Popayan, Colombia[J].Engineering Geology,2020.
Chen Wei,Chen Yunzhi,Tsangaratos Paraskevas,Ilia Ioanna,Wang Xiaojing.Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments[J].Remote Sensing,2020.
Fasheng Miao,Yiping Wu,Linwei Li,Kang Liao,Yang Xue.Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods[J].Natural Hazards,2020.
Mahdi Panahi,Amiya Gayen,Hamid Reza Pourghasemi,Fatemeh Rezaie,Saro Lee.Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms[J].Science of the Total Environment,2020.
Faming Huang,Zhongshan Cao,Jianfei Guo,Shui-Hua Jiang,Shu Li,Zizheng Guo.Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping[J].Catena,2020.
Nathalie Cruz Sena,Gustavo Vieira Veloso,Elpídio Inácio Fernandes-Filho,Marcio Rocha Francelino,Carlos Ernesto G.R. Schaefer.Analysis of terrain attributes in different spatial resolutions for digital soil mapping application in southeastern Brazil[J].Geoderma Regional,2020.
Haoyuan Hong,Junzhi Liu,A-Xing Zhu.Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble[J].Science of the Total Environment,2020.
Abdul-Lateef Balogun,Shamsudeen Temitope Yekeen,Biswajeet Pradhan,Omar F. Althuwaynee.Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models[J].Remote Sensing,2020.
Deuk-Hwan Lee,Yun-Tae Kim,Seung-Rae Lee.Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions[J].Remote Sensing,2020.
Zhuo Chen,Fei Ye,Wenxi Fu,Yutian Ke,Haoyuan Hong.The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China[J].Natural Hazards,2020.
Lucchese Luísa Vieira,de Oliveira Guilherme Garcia,Pedrollo Olavo Correa.Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment.[J].Environmental monitoring and assessment,2020.
School of Civil Engineering and Architecture, Nanchang University, 330000, Nanchang, China,School of Information Engineering, Nanchang University, 330000, Nanchang, China,School of Civil Engineering and Architecture, Nanchang University, 330000, Nanchang, China,School of Information Engineering, Nanchang University, 330000, Nanchang, China,ARC Centre of Excellence for Geotechnical Science and Engineering, University of Newcastle, Newcastle, NSW, Australia,School of Information Engineering, Nanchang University, 330000, Nanchang, China.A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction[J].Landslides: Journal of the International Consortium on Landslides,2020.
Samuele Segoni,Giulio Pappafico,Tania Luti,Filippo Catani.Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization[J].Landslides,2020.
Yaming Tang,Fan Feng,Zizheng Guo,Wei Feng,Zhengguo Li,Jiayun Wang,Qiaoyin Sun,Hongna Ma,Yane Li.Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the Loess Plateau area in Shanxi (China)[J].Journal of Cleaner Production,2020.
School of Geoscience and Technology,Southwest Petroleum University,School of Geoscience and Technology,Southwest Petroleum University,State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Institute of Geology, China Earthquake Administration,School of Geoscience and Technology,Southwest Petroleum University,State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences.Local-scale landslide susceptibility mapping using the B-GeoSVC model[J].Landslides,2019.
Du Juan,Glade Thomas,Woldai Tsehaie,Chai Bo,Zeng Bin.Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas[J].Engineering Geology,2019(prep).
Tzu-Hsin Karen Chen,Alexander V. Prishchepov,Rasmus Fensholt,Clive E. Sabel.Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017[J].Remote Sensing of Environment,2019.
Binh Thai Pham,Indra Prakash,Sushant K. Singh,Ataollah Shirzadi,Himan Shahabi,Thi-Thu-Trang Tran,Dieu Tien Bui.Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches[J].Catena,2019.
Jie Dou,Ali P. Yunus,Dieu Tien Bui,Abdelaziz Merghadi,Mehebub Sahana,Zhongfan Zhu,Chi-Wen Chen,Khabat Khosravi,Yong Yang,Binh Thai Pham.Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan[J].Science of the Total Environment,2019.
Suchita Shrestha,Tae-Seob Kang.Assessment of seismically-induced landslide susceptibility after the 2015 Gorkha earthquake, Nepal[J].Bulletin of Engineering Geology and the Environment,2019.
Wei Chen,Mahdi Panahi,Paraskevas Tsangaratos,Himan Shahabi,Ioanna Ilia,Somayeh Panahi,Shaojun Li,Abolfazl Jaafari,Baharin Bin Ahmad.Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility[J].Catena,2019.
Jintao Yang,Chao Song,Yang Yang,Chengdong Xu,Fei Guo,Lei Xie.New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China[J].Geomorphology,2018.
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia,Physical Sciences and Engineering (PSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.Presenting logistic regression-based landslide susceptibility results[J].Engineering Geology,2018.
Luigi Borrelli,Mariantonetta Ciurleo,Giovanni Gullà.Shallow landslide susceptibility assessment in granitic rocks using GIS-based statistical methods: the contribution of the weathering grade map[J].Landslides,2018.
Paola Reichenbach,Mauro Rossi,Bruce D. Malamud,Monika Mihir,Fausto Guzzetti.A review of statistically-based landslide susceptibility models[J].Earth-Science Reviews,2018.
Hamid Reza Pourghasemi,Omid Rahmati.Prediction of the landslide susceptibility: Which algorithm, which precision?[J].Catena,2018.
original> .Landslide susceptibility modelling using the quantitative random forest method along the northern portion of the Yukon Alaska Highway Corridor, Canada[J].Natural Hazards,2018.
R. Schlögel,I. Marchesini,M. Alvioli,P. Reichenbach,M. Rossi,J.-P. Malet.Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models[J].Geomorphology,2018.
Faming Huang,Chi Yao,Weiping Liu,Yijing Li,Xiaowen Liu.Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine[J].Geomatics, Natural Hazards and Risk,2018.
Hyun-Joo Oh,Prima Riza Kadavi,Chang-Wook Lee,Saro Lee.Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models[J].Geomatics, Natural Hazards and Risk,2018.
Wouter Dorigo,Wolfgang Wagner,Clement Albergel,Franziska Albrecht,Gianpaolo Balsamo,Luca Brocca,Daniel Chung,Martin Ertl,Matthias Forkel,Alexander Gruber,Eva Haas,Paul D. Hamer,Martin Hirschi,Jaakko Ikonen,Richard de Jeu,Richard Kidd,William Lahoz,Yi Y. Liu,Diego Miralles,Thomas Mistelbauer,Nadine Nicolai-Shaw,Robert Parinussa,Chiara Pratola,Christoph Reimer,Robin van der Schalie,Sonia I. Seneviratne,Tuomo Smolander,Pascal Lecomte.ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions[J].Remote Sensing of Environment,2017.
Binh Thai Pham,Dieu Tien Bui,Indra Prakash,M.B. Dholakia.Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS[J].Catena,2017.
Omid Rahmati,Naser Tahmasebipour,Ali Haghizadeh,Hamid Reza Pourghasemi,Bakhtiar Feizizadeh.Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework[J].Science of the Total Environment,2017.
J.N. Goetz,A. Brenning,H. Petschko,P. Leopold.Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling[J].Computers and Geosciences,2015.
Taskin Kavzoglu,Emrehan Kutlug Sahin,Ismail Colkesen.Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm[J].Engineering Geology,2015.
Markus Meinhardt,Manfred Fink,Hannes Tünschel.Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics[J].Geomorphology,2015.
Zhi-hua Yang,Heng-xing Lan,Xing Gao,Lang-ping Li,Yun-shan Meng,Yu-ming Wu.Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China[J].Natural Hazards,2015.
Cheng-Zhi Qin,Li-Li Bao,A-Xing Zhu,Rong-Xun Wang,Xue-Mei Hu.Uncertainty due to DEM error in landslide susceptibility mapping[J].International Journal of Geographical Information Science,2013.
Stefan Carpentier,Markus Konz,Ria Fischer,Grigorios Anagnostopoulos,Katrin Meusburger,Konrad Schoeck.Geophysical imaging of shallow subsurface topography and its implication for shallow landslide susceptibility in the Urseren Valley, Switzerland[J].Journal of Applied Geophysics,2012.
Hamid Reza Pourghasemi,Biswajeet Pradhan,Candan Gokceoglu.Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran[J].Natural Hazards,2012.
0
浏览量
628
下载量
10
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621