|
|
|
| A comprehensive review of key issues in landslide susceptibility prediction and their solutions using semi-supervised imbalanced theory |
| HUANG Faming1, 2, YANG Yang1, JIANG Shuihua1, ZHOU Chuangbing1, FAN Xuanmei3, PAN Lihan4, YAO Chi1, XIONG Haowen1, CHANG Zhilu1 |
(1. School of Infrastructure Engineering, Nanchang University, Nanchang, Jiangxi 330031, China; 2. Badong National Observation and Research Station of Geohazards, China University of Geosciences(Wuhan), Wuhan, Hubei 430074, China; 3. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan 610059, China;
4. Jiangxi Engineering Survey and Research Institute of Nuclear Industry Co., Ltd., Nanchang, Jiangxi 330006, China) |
|
|
|
|
Abstract As the foundation for regional landslide risk assessment, landslide susceptibility prediction (LSP) is a prominent and challenging topic in global landslide disaster prevention and control research. This paper systematically reviews critical issues in LSP, including model selection, identification and integration of conditioning factors, determination and classification of prediction units, methods for selecting non-landslide samples, optimization of the landslide-to-non-landslide sample ratio, assignment of label values to non-landslide samples, and evaluation methodologies for landslide susceptibility results. The literature review indicates that tree-based models, such as Decision Trees and Random Forests, demonstrate superior performance. The selection and integration of conditioning factors should adhere to principles of comprehensive typology and clear physical significance. Prediction units can be defined through multi-scale segmentation of slope units. Non-landslide samples should preferably be randomly selected from areas characterized by very low or low susceptibility. The optimal ratio of landslide to non-landslide samples can be established through experiments under various conditions. Specific small probability values should be assigned to non-landslide samples. Evaluation of LSP results necessitates a comprehensive consideration of multiple metrics, including ROC accuracy, prediction rate accuracy, and the mean and standard deviation of the susceptibility index. Furthermore, to enhance the accuracy of LSP and validate the cross-regional engineering applicability of the semi-supervised imbalanced theory, this study pioneers the application of the Random Forest model based on this theory in Anyuan-Xunwu County, Jiangxi Province. Experiments were conducted under various scenarios, involving different ratios of landslide to non-landslide samples (ranging from 1:1 to 1:260) and varying label assignments for non-landslide samples. Results indicate that as the ratio increases from 1:1 to 1:180, both ROC accuracy and prediction rate accuracy improve gradually from 0.905 and 0.898 to 0.957 and 0.937, respectively. However, no significant improvement is observed in either metric once the ratio exceeds 1:180. Consequently, the optimal ratio of landslide to non-landslide samples is established at 1:180. Additionally, this study collects data from six landslide events that occurred between 2022 and 2024 to validate the results obtained from the 1:180 ratio. The validation reveals that all six landslides are situated in areas of very high or high susceptibility. This not only confirms the effectiveness and engineering applicability of the semi-supervised imbalanced theory in the mountainous and hilly regions of southern Jiangxi but also provides new insights and technical support for the precise prevention and control of landslide risks.
|
|
|
|
|
|
[1] ZENG T R，GUO Z Z，WANG L F，et al. Tempo-spatial landslide susceptibility assessment from the perspective of Human engineering activity[J]. Remote Sensing，2023，15(16)：4 111.
[2] QI T，MENG X，ZHAO Y，et al. Formation and distribution of landslides controlled by thrust-strike-slip fault zones and fluvial erosion in the Western Qinling Mountains，China[J]. Engineering Geology，2023，323：107209.
[3] 王 悦，曹 颖，许方党，等. 考虑非滑坡样本选取和集成机器学习方法的水库滑坡易发性预测[J]. 地球科学，2024，49(5)：1 619–1 635.(WANG Yue，CAO Ying，XU Fangdang，et al. Reservoir landslide susceptibility prediction considering non?landslide sampling and ensemble machine learning methods[J]. Earth Science，2024，49(5)：1 619–1 635.(in Chinese))
[4] 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.
[5] 郭 飞，赖 鹏，黄发明，等. 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，2024，49(5)：1 584–1 606.(GUO Fei，LAI Peng，HUANG Faming，et al. Literature review and research progress of landslide susceptibility mapping based on knowledge graph[J]. Earth Science，2024，49(5)：1 584–1 606.(in Chinese))
[6] CHANG Z，DU Z，ZHANG F，et al. Landslide susceptibility prediction based on remote sensing images and GIS：Comparisons of supervised and unsupervised machine learning models[J]. Remote Sensing，2020，12(3)：502.
[7] WANG J，CHEN G，JABOYEDOFF M，et al. Loess landslides detection via a partially supervised learning and improved Mask-RCNN with multi-source remote sensing data[J]. Catena，2023，231：107371.
[8] HUANG F M，TAO S Y，CHANG Z L，et al. Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments[J]. Landslides，2021，18(11)：3 715–3 731.
[9] ZHU A X，MIAO Y M，YANG L，et al. Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping [J]. Catena，2018，171：222–233.
[10] SHAO X Y，MA S Y，XU C，et al. Effects of sampling intensity and non-slide/slide sample ratio on the occurrence probability of coseismic landslides[J]. Geomorphology，2020，363：10777.
[11] SHIRZADI A，SOLAIMANI K，KAVIAN A，et al. Uncertainties of prediction accuracy in shallow landslide modeling：Sample size and raster resolution[J]. Catena，2019，178：172–188.
[12] PHOON K K，ZHANG W. Future of machine learning in geotechnics[J]. Georisk：Assessment and Management of Risk for Engineered Systems and Geohazards，2023，17(1)：7–22.
[13] HUANG F M，XIONG H W，JIANG S H，et al. Modelling landslide susceptibility prediction：A review and construction of semi-supervised imbalanced theory[J]. Earth-Science Reviews，2024，250：104700.
[14] YOUSSEF A M，POURGHASEMI H R. Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin，Asir Region，Saudi Arabia[J]. Geoscience Frontiers，2021，12(2)：639–655.
[15] BUI D T，PRADHAN B，LOFMAN O，et al. Landslide susceptibility assessment in the Hoa Binh province of Vietnam：a comparison of the Levenberg-Marquardt and Bayesian regularized neural networks[J]. Geomorphology，2012，171：12–29.
[16] FU Z，WANG F，DOU J，et al. Enhanced absence sampling technique for data-driven landslide susceptibility mapping：a case study in Songyang County，China[J]. Remote Sensing，2023，15(13)：3 345.
[17] 贾雨霏，魏文豪，陈 稳，等. 基于 SOM-I-SVM 耦合模型的滑坡易发性评价[J]. 水文地质工程地质，2023，50(3)：125–137.(JIA Yufei，WEI Wenhao，CHEN Wen，et al. Landslide susceptibility assessment based on the SOM-I-SVM model[J]. Hydrogeology and Engineering Geology，2023，50(3)：125–137.(in Chinese))
[18] 黄发明，陈 彬，毛达雄，等. 基于自筛选深度学习的滑坡易发性预测建模及其可解释性[J]. 地球科学，2023，48(5)：1 696–1 710. (HUANG Faming，CHEN Bin，MAO Daxiong，et al. Landslide susceptibility prediction modeling and interpretability based on self‐screening deep learning model[J]. Earth Science，2023，48(5)：1 696–1 710.(in Chinese))
[19] 朱崇浩，张建经，马东华，等. 基于 DInSAR-BP 神经网络的震后区域滑坡危险性综合评价研究[J]. 工程地质学报，2020，28(3)：530–540.(ZHU Chonghao，ZHANG Jianjing，MA Donghua，et al. Comprehensive analysis on risk of landslides in post-earthquake area based on DInSAR-BP neural networks[J]. Journal of Engineering Geology，2020，28(3)：530–540.(in Chinese))
[20] 毕结昂，徐佩华，宋盛渊，等. 基于信息量–逻辑回归耦合模型的玛纳斯河流域地质灾害易发性评价[J]. 工程地质学报，2022，30(5)：1 549–1 560.(BI Jiaang，XU Peihua，SONG Shengyuan，et al. Assessment of the susceptibility to geological hazards in the Manas River Basin based on the coupled information value-logistic regression model[J]. Journal of Engineering Geology，2022，30(5)：1549–1 560.(in Chinese))
[21] ZHANG W G，HE Y W，WANG L Q，et al. Landslide Susceptibility mapping using random forest and extreme gradient boosting：A case study of Fengjie，Chongqing[J]. Geological Journal，2023，58(6)：2 372–2 387.
[22] MERGHADI A，YUNUS A P，DOU J，et al. Machine learning methods for landslide susceptibility studies：A comparative overview of algorithm performance[J]. Earth-Science Reviews，2020，207：103225.
[23] 赵占骜，王继周，毛 曦，等. 多维CNN耦合的滑坡易发性评价方法[J]. 武汉大学学报：信息科学版，2024，49(8)：1 466–1 481.(ZHAO Zhanao，WANG Jizhou，MAO Xi，et al. A multi-dimensional CNN coupled landslide susceptibility assessment method[J]. Geomatics and Information Science of Wuhan University，2024，49(8)：1 466–1 481.(in Chinese))
[24] ZHANG W G，LIU S L，WANG L Q，et al. The improvement of large-scale-region landslide susceptibility mapping accuracy by transfer learning[J]. Journal of Central South University，2024，31(11)：3 823–3 837.
[25] 付智勇，李典庆，王 顺，等. 基于多时空滑坡编录和TrAdaBoost迁移学习的滑坡易发性评价[J]. 地球科学，2023，48(5)：1 935–1 947.(FU Zhiyong，LI Dianqing，WANG Shun，et al. Landslide susceptibility assessment based on multitemporal landslide inventories and TrAdaBoost transfer learning[J]. Earth Science，2023，48(5)：1 935–1 947.(in Chinese))
[26] JIANG Y H，WANG W，ZOU L F，et al. Investigating landslide data balancing for susceptibility mapping using generative and machine learning models[J]. Landslides，2025，22(1)：189–204.
[27] AL-NAJJAR H A H，PRADHAN B. Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks[J]. Geoscience Frontiers，2021，12(2)：625–637.
[28] HUANG F M，CAO Z S，GUO J F，et al. Comparisons of heuristic，general statistical and machine learning models for landslide susceptibility prediction and mapping[J]. Catena，2020，191：104580.
[29] ZHOU C，YIN K L，CAO Y，et al. Landslide susceptibility modeling applying machine learning methods：a case study from Longju in the Three Gorges Reservoir area，China[J]. Computers and Geosciences，2018，112：23–37.
[30] BUI D T，TSANGARATOS P，NGUYEN V T，et al. Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment[J]. Catena，2020，188：104426.
[31] NGO P T T，PANAHI M，KHOSRAVI K，et al. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran[J]. Geoscience Frontiers，2021，12(2)：505–519.
[32] CHEN W，PENG J B，HONG H Y，et al. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County，Jiangxi Province，China[J]. Science of the Total Environment，2018，626：1121–1135.
[33] CHANG Z L，DU Z，ZHANG F，et al. Landslide susceptibility prediction based on remote sensing images and GIS：comparisons of supervised and unsupervised machine learning models[J]. Remote Sensing，2020，12(3)：502.
[34] 陈芯宇，师 芸，温永啸，等. 基于确定性系数与支持向量机的滑坡易发性评价[J]. 科学技术与工程，2023，23(2)：518–527.(CHEN Xinyu，SHI Yun，WEN Yongxiao，et al. Landslide susceptibility evaluation based on certainty factor and support vector machine[J]. Science Technology and Engineering，2023，23(2)：518–527.(in Chinese))
[35] 高秉海，何 毅，张立峰，等. 顾及In SAR形变的CNN滑坡易发性动态评估——以刘家峡水库区域为例[J]. 岩石力学与工程学报，2023，42(2)：450–465.(GAO Binghai，HE Yi，ZHANG Lifeng，et al. Dynamic evaluation of landslide susceptibility by CNN considering InSAR deformation：A case study of Liujiaxia reservoir[J]. Chinese Journal of Rock Mechanics and Engineering，2023，42(2)：450–465.(in Chinese))
[36] SUN D L，XU J H，WEN H J，et al. Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization：A comparison between logistic regression and random forest[J]. Engineering Geology，2021，281：105972.
[37] DAVIRAN M，SHAMEKHI M，GHEZELBASH R，et al. Landslide susceptibility prediction using artificial neural networks，SVMs and random forest：hyperparameters tuning by genetic optimization algorithm[J]. International Journal of Environmental Science and Technology，2023，20(1)：259–276.
[38] CHEN C Z，SHEN Z Q，WENG Y H，et al. Modeling landslide susceptibility in forest-covered areas in Lin?an，China，using logistical regression，a decision tree，and random forests[J]. Remote Sensing，2023，15(18)：4 378.
[39] SONG Y Z，YANG D G，WU W C，et al. Evaluating landslide susceptibility using sampling methodology and multiple machine learning models[J]. Isprs International Journal of Geo-Information，2023，12(5)：197.
[40] 王本栋，李四全，许万忠，等. 基于3种不同机器学习算法的滑坡易发性评价对比研究[J]. 西北地质，2024，57(1)：34–43.(WANG Bendong，LI Siquan，XU Wanzhong，et al. A comparative study of landslide susceptibility evaluation based on three different machine learning algorithms[J]. Northwestern Geology，2024，57(1)：34–43.(in Chinese))
[41] 刘山东，李 军，江兴元，等. 基于数学统计与机器学习模型耦合的滑坡易发性评价方法优化[J]. 科学技术与工程，2025，25(5)：1 827–1 839.(LIU Shandong，LI Jun，JIANG Xingyuan，et al. Optimization of landslide susceptibility assessment method coupling mathematical statistics and machine learning models[J]. Science Technology and Engineering，2025，25(5)：1 827–1 839.(in Chinese))
[42] 黄武彪，丁明涛，王 栋，等. 基于层数自适应加权卷积神经网络的川藏交通廊道沿线滑坡易发性评价[J]. 地球科学，2022，47(6)：2 015–2 030.(HUANG Wubiao，DING Mingtao，WANG Dong，et al. Evaluation of landslide susceptibility based on layer adaptive weighted convolutional neural network model along Sichuan-Tibet traffic corridor[J]. Earth Science，2022，47(6)：2 015–2 030.(in Chinese))
[43] PHAM B T，BUI D T，PRAKASH I，et al. Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS[J]. Catena，2017，149：52–63.
[44] 黄发明，刘科技，曾子强，等. 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，2024，32(1)：49–71.(HUANG Faming，LIU Keji，ZENG Ziqiang，et al. Influence of environmental factor selection and combination on landslide susceptibility prediction modeling[J]. Journal of Basic Science and Engineering，2024，32(1)：49–71.(in Chinese))
[45] HUANG F M，TENG Z K，YAO C，et al. Uncertainties of landslide susceptibility prediction：Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method[J]. Journal of Rock Mechanics and Geotechnical Engineering，2024，16(1)：213–230.
[46] CHEN W W，ZHANG S. GIS-based comparative study of Bayes network，Hoeffding tree and logistic model tree for landslide susceptibility modeling[J]. Catena，2021，203：105344.
[47] SHAHABI H，AHMADI R，ALIZADEH M，et al. Landslide susceptibility mapping in a mountainous area using machine learning algorithms[J]. Remote Sensing，2023，15(12)：3 112.
[48] YOUSSEF K，SHAO K，MOON S，et al. Landslide susceptibility modeling by interpretable neural network[J]. Communications Earth and Environment，2023，4(1)：162.
[49] YU H W，PEI W J，ZHANG J Y，et al. Landslide susceptibility mapping and driving mechanisms in a vulnerable region based on multiple machine learning models[J]. Remote Sensing，2023，15(7)：1 886.
[50] HUANG F，LIU K，JIANG S，et al. Optimization method of conditioning factors selection and combination for landslide susceptibility prediction[J]. Journal of Rock Mechanics and Geotechnical Engineering，2024，17(2)：722–746.
[51] KAVZOGLU T，SAHIN E K，COLKESEN I. Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm[J]. Engineering Geology，2015，192：101–112.
[52] 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，2021，40(6)：1 226–1 235.(WU Yuchen，ZHOU Hanxu，CHE Ailan. Susceptibility of landslides caused by IBURI earthquake based on rough set-neural network[J]. Chinese Journal of Rock Mechanics and Engineering，2021，40(6)：1 226–1 235.(in Chinese))
[53] TANG Y，FENG F，GUO Z，et al. 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，2020，277：124159.
[54] SUN D，SHI S，WEN H，et al. A hybrid optimization method of factor screening predicated on geodetector and random forest for landslide susceptibility mapping[J]. Geomorphology，2021，379：107623.
[55] LUCCHESE L V，DE OLIVEIRA G G，PEDROLLO O C. Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment [J]. Environmental monitoring and assessment，2020，192(2)：129.
[56] 陈 涛，钟子颖，牛瑞卿，等. 利用深度信念网络进行滑坡易发性评价[J]. 武汉大学学报：信息科学版，2020，45(11)：1 809–1 817. (CHEN Tao，ZHONG Ziying，NIU Ruiqing，et al. Mapping landslide susceptibility based on deep belief network[J]. Geomatics and Information Science of Wuhan University，2020，45(11)：1 809–1 817.(in Chinese))
[57] ZHU L，WANG G，HUANG F，et al. Landslide susceptibility prediction using sparse feature extraction and machine learning models based on GIS and remote sensing[J]. IEEE Geoscience and Remote Sensing Letters，2021，PP(99)：1–5.
[58] SUN X H，CHEN J P，HAN X D，et al. Application of a GIS-based slope unit method for landslide susceptibility mapping along the rapidly uplifting section of the upper Jinsha River，South-Western China[J]. Bulletin of Engineering Geology and the Environment，2020，79(1)：533–549.
[59] 薛 强，张茂省，李 林. 基于斜坡单元与信息量法结合的宝塔区黄土滑坡易发性评价[J]. 地质通报，2015，34(11)：2 108–2 115. (XUE Qiang，ZHANG Maosheng，LI Lin. Loess landslide susceptibility evaluation based on slope unit and information value method in Baota District，Yan’an[J]. Geological Bulletin of China，2015，34(11)：2 108–2 115.(in Chinese))
[60] CHANG Z L，CATANI F，HUANG F M，et al. Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors[J]. Journal of Rock Mechanics and Geotechnical Engineering，2023，15(5)：1 127–1 143.
[61] MA S Y，SHAO X Y，XU C. Landslide susceptibility mapping in terms of the slope-unit or raster-unit，which is better?[J]. Journal of Earth Science，2023，34(2)：386–397.
[62] ZHANG J Y，MA X L，ZHANG J L，et al. Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model[J]. Journal of Environmental Management，2023，332：117357.
[63] 许 烈，巨能攀，邓明东，等. 不同评价单元的滑坡易发性评价精度对比分析[J]. 工程地质学报，2024，32(5)：1 640–1 653.(XU Lie，JU Nengpan，DENG Mingdong，et al. Comparative analysis of landslide susceptibility assessment accuracy in different evaluation units[J]. Journal of Engineering Geology，2024，32(5)：1 640–1 653.(in Chinese))
[64] THAM L G. Landslide susceptibility mapping based on Support Vector Machine：A case study on natural slopes of Hong Kong，China[J]. Geomorphology，2008，101(4)：572–582.
[65] SUN X H，CHEN J P，HAN X D，et al. Landslide susceptibility mapping along the upper Jinsha River，south-western China：a comparison of hydrological and curvature watershed methods for slope unit classification[J]. Bulletin of Engineering Geology and the Environment，2020，79(9)：4 657–4 670.
[66] ALVIOLI M，MARCHESINI I，REICHENBACH P，et al. Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling[J]. Geoscientific Model Development，2016，9(11)：3 975–3 991.
[67] CHANG Z，HUANG J，HUANG F，et al. Uncertainty analysis of non-landslide sample selection in landslide susceptibility prediction using slope unit-based machine learning models[J]. Gondwana Research，2023，117：307–320.
[68] KHABIRI S，CRAWFORD M M，KOCH H J，et al. An assessment of negative samples and model structures in landslide susceptibility characterization based on bayesian network models[J]. Remote Sensing，2023，15(12)：3 200.
[69] 苏志萍，杨成生，王子倩. 结合负样本优化与机器学习模型的怒江洲峡谷段滑坡易发性评价[J]. 地球信息科学学报，2025，27(4)：979–993.(SU Zhiping，YANG Chengsheng，WANG Ziqian. Landslide susceptibility assessment in Nujiang Canyon using negative sample optimization and machine learning models[J]. Journal of Geo-information Science，2025，27(4)：979–993.(in Chinese))
[70] YAO X，THAM L G，DAI F C. Landslide susceptibility mapping based on Support Vector Machine：A case study on natural slopes of Hong Kong，China[J]. Geomorphology，2008，101(4)：572–582.
[71] WANG J J，JABOYEDOFF M，CHEN G，et al. Landslide susceptibility prediction and mapping using the LD-BiLSTM model in seismically active mountainous regions[J]. Landslides，2024，21(1)：17–34.
[72] KAVZOGLU T，SAHIN E K，COLKESEN I. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis，support vector machines，and logistic regression[J]. Landslides，2014，11(3)：425–439.
[73] WANG J N，WANG Y Q，LI M Y，et al. Improved landslide susceptibility assessment：A new negative sample collection strategy and a comparative analysis of zoning methods[J]. Ecological Indicators，2024，169：112948.
[74] HU Q，ZHOU Y，WANG S X，et al. Machine learning and fractal theory models for landslide susceptibility mapping：Case study from the Jinsha River Basin[J]. Geomorphology，2020，351：106975.
[75] LIU M，LIU J，XU S，et al. Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy：A case study from Shaanxi Province[J]. International Journal of Image，2021，12(4)：349–366.
[76] 崔玉龙，朱路路，徐 敏，等. 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，2024，43(3)：192–199.(CUI Yulong，ZHU Lulu，XU Min，et al. Opimizing TSES method based on the environmental factors to select negative samples and its application in landslide susceptibility evaluation[J]. Bulletin of Geological Science and Technology，2024，43(3)：192–199.(in Chinese))
[77] HU Q，ZHOU Y，WANG S，et al. Machine learning and fractal theory models for landslide susceptibility mapping：Case study from the Jinsha River Basin[J]. Geomorphology，2020，351：106975.
[78] ZHOU X，HUANG F，WU W，et al. Regional landslide susceptibility prediction based on negative sample selected by coupling information value method[J]. Advanced Engineering Sciences，2022，54(3)：25–35.
[79] 郭衍昊，窦 杰，向子林，等. 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，2024，43(3)：251–265.(GUO Yanhao，DOU Jie，XIANG Zilin，et al. Susceptibility evaluation of Wenchuan coseismic landslides by gradient boosting decision tree and random foret based on optimal negative sample sampling strategies[J]. Bulletin of Geological Science and Technology，2024，43(3)：251–265.(in Chinese))
[80] SU X L，XIA X L，LIANG Q H，et al. A coupled discrete element and depth-averaged model for dynamic simulation of flow-like landslides[J]. Computers and Geotechnics，2022，141：104537.
[81] 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. (HUANG Faming，YIN Kunlong，JIANG Shuihua，et al. Landslide susceptibility assessment based on clustering analysis and support vector machine[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(1)：156–167.(in Chinese))
[82] VAN DAO D，JAAFARI A，BAYAT M，et al. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility[J]. Catena，2020，188：104451.
[83] HUANG F，CAO Z，JIANG S H，et al. Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model[J]. Landslides，2020，17(12)：2919–2930.
[84] CHEN S，MIAO Z，WU L，et al. Application of an Incomplete Landslide Inventory and One Class Classifier to Earthquake-induced Landslide Susceptibility Mapping[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2020，13：1649–1660.
[85] STEGER S，BRENNING A，BELL R，et al. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps[J]. Geomorphology，2016，262：8–23.
[86] HUANG F，ZHANG J，ZHOU C，et al. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction[J]. Landslides，2020，17(1)：217–229.
[87] 武雪玲，杨经宇，牛瑞卿. 一种结合SMOTE和卷积神经网络的滑坡易发性评价方法[J]. 武汉大学学报：信息科学版，2020，45(8)：1 223–1 232.(WU Xyeling，YANG Jingyu，NIU Ruiqing. A landslide susceptibility assessment method using smote and convolutional neural network[J]. Geomatics and Information Science of Wuhan University，2020，45(8)：1 223–1 232.(in Chinese))
[88] POURGHASEMI H R，KORNEJADY A，KERLE N，et al. Investigating the effects of different landslide positioning techniques，landslide partitioning approaches，and presence-absence balances on landslide susceptibility mapping[J]. Catena，2020，187：104364.
[89] LAI J S，TSAI F. Improving GIS-Based landslide susceptibility assessments with multi-temporal remote sensing and machine learning[J]. Sensors，2019，19(17)：3 717.
[90] HONG H Y，MIAO Y M，LIU J Z，et al. Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping[J]. Catena，2019，176：45–64.
[91] GUPTA S K，SHUKLA D P. Handling data imbalance in machine learning based landslide susceptibility mapping：A case study of Mandakini River Basin，North-Western Himalayas[J]. Landslides，2023，20(5)：933–949.
[92] YANG C，LIU L L，HUANG F M，et al. Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples[J]. Gondwana Research，2023，123：198–216.
[93] HUANG F M，CAO Z S，JIANG S H，et al. Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model[J]. Landslides，2020，17(12)：2 919–2 930.
[94] ACHU A，AJU C，DI NAPOLI M，et al. Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis[J]. Geoscience Frontiers，2023，14(6)：101657.
[95] 周 超，殷坤龙，曹 颖，等. 基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价[J]. 地球科学，2020，45(6)：1 865–1 876. (ZHOU Chao，YIN Kunlong，CAO Ying，et al. Landslide susceptibility assessment by applying the coupling method of radial basis Neural Network and Adaboost：a case study from the Three Gorges Reservoir Area[J]. Earth Science，2020，45(6)：1 865–1 876.(in Chinese))
[96] CUI H Z，JI J，HUERLIMANN M，et al. Probabilistic and physically-based modelling of rainfall-induced landslide susceptibility using integrated GIS-FORM algorithm[J]. Landslides，2024，21(6)：1 461–1 481.
[97] TYAGI A，TIWARI R K，JAMES N. Prediction of the future landslide susceptibility scenario based on LULC and climate projections[J]. Landslides，2023，20(9)：1 837–1 852.
[98] WEI Y D，QIU H J，LIU Z J，et al. Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models[J]. Geoscience Frontiers，2024，15(6)：101890.
[99] WU B，SHI Z M，ZHENG H C，et al. Impact of sampling for landslide susceptibility assessment using interpretable machine learning models[J]. Bulletin of Engineering Geology and the Environment，2024，83(11)：461.
[100] LI L P，LAN H X. Bivariate landslide susceptibility analysis：clarification，optimization，open software，and preliminary comparison[J]. Remote Sensing，2023，15(5)：1 418.
[101] VAKHSHOORI V，ZARE M. Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps?[J]. Geomatics Natural Hazards and Risk，2018，9(1)：249–266.
[102] HUANG F M，YE Z，JIANG S H，et al. Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models[J]. Catena，2021，202：105250.
[103] ZHOU X Z，WEN H J，ZHANG Y L，et al. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization[J]. Geoscience Frontiers，2021，12(5)：101211.
[104] SUN D L，XU J H，WEN H J，et al. An optimized random forest model and its generalization ability in landslide susceptibility mapping：application in two areas of Three Gorges Reservoir，China[J]. Journal of Earth Science，2020，31(6)：1 068–1 086.
[105] WANG X M，NIU R Q. Landslide intelligent prediction using object-oriented method[J]. Soil Dynamics and Earthquake Engineering，2010，30(12)：1 478–1 486.
[106] LI W B，FAN X M，HUANG F M，et al. Uncertainties analysis of collapse susceptibility prediction based on remote sensing and GIS：influences of different data-based models and connections between collapses and environmental factors[J]. Remote Sensing，2020，12(24)：4 134.
[107] RIAZ M T，BASHARAT M，BRUNETTI M T. Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain[J]. Progress in Physical Geography：Earth Environmental Geology，2023，47(3)：315–347.
[108] CASAGLI N，INTRIERI E，TOFANI V，et al. Landslide detection，monitoring and prediction with remote-sensing techniques[J]. Nature Reviews Earth Environmental Geology，2023，4(1)：51–64.
[109] HUANG F，XIONG H，YAO C，et al. Uncertainties of landslide susceptibility prediction considering different landslide types[J]. Journal of Rock Mechanics and Geotechnical Engineering，2023，15(11)：2 954–2 972.
[110] 李铁锋，徐岳仁，潘 懋，等. 基于多期SPOT–5影像的降雨型浅层滑坡遥感解译研究[J]. 北京大学学报，2007，43(2)：204–210.(LI Tiefeng，XU Yueren，PAN Mao，et al. Study on interpretation of rain- induced group shallow landslides based on multi-period SPOT-5 remote sensing images[J]. Acta Scientiarum Naturalium Universitatis Pekinensis，2007，43(2)：204–210.(in Chinese))
[111] 马思远，许 冲，田颖颖，等. 基于逻辑回归模型的九寨沟地震滑坡危险性评估[J]. 地震地质，2019，41(1)：162–177. (MA Siyuan，XU Chong，TIAN Yingying，et al. Application of logistic regression model for hazard assessment of earthquake-triggered landslides：a case study of 2017 Jiu Zhai Gou (China) MS7.0 event[J]. Seismology and Geology，2019，41(1)：162–177.(in Chinese))
[112] HUANG F M，XIONG H W，ZHOU X T，et al. Modelling uncertainties and sensitivity analysis of landslide susceptibility prediction under different environmental factor connection methods and machine learning models[J]. Ksce Journal of Civil Engineering，2024，28(1)：45–62.
[113] CHANG Z，HUANG F，JIANG S，et al. Slope unit extraction and landslide susceptibility prediction using multi-scale segmentation method[J]. Advanced Engineering Sciences，2023，55(1)：184–195.
[114] CANTARINO I，CARRION M A，GOERLICH F，et al. A ROC analysis-based classification method for landslide susceptibility maps[J]. Landslides，2019，16(2)：265–282.
[115] KE C Y，SUN P，ZHANG S，et al. Influences of non-landslide sampling strategies on landslide susceptibility mapping：a case of Tianshui city，Northwest of China[J]. Bulletin of Engineering Geology and the Environment，2025，84(3)：123.
[116] CHEN W，XIE X，WANG J，et al. A comparative study of logistic model tree，random forest，and classification and regression tree models for spatial prediction of landslide susceptibility[J]. Catena，2017，151：147–160.
[117] FANG Z C，WANG Y，DUAN H X，et al. Comparison of general kernel，multiple kernel，infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction[J]. Stochastic Environmental Research and Risk Assessment，2022，36(10)：3 535–3 556.
[118] JIANG Y H，WANG W，ZOU L F，et al. Regional landslide susceptibility assessment based on improved semi-supervised clustering and deep learning[J]. Acta Geotechnica，2024，19(1)：509–529.
[119] 黄发明，潘李含，姚 池，等. 基于半监督机器学习的滑坡易发性预测建模[J]. 浙江大学学报：工学版，2021，55(9)：1 705–1 713. (HUANG Faming，PAN Lihan，YAO Chi，et al. Landslide susceptibility prediction modeling based on semi-supervised machine learning[J]. Journal of Zhejiang University：Engineering Science，2021，55(9)：1 705–1 713.(in Chinese))
[120] WANG C H，LIN Q G，WANG L B，et al. The influences of the spatial extent selection for non-landslide samples on statistical-based landslide susceptibility modelling：a case study of Anhui Province in China[J]. Natural Hazards，2022，112(3)：1 967–1 988.
[121] 黄发明，曾诗怡，姚 池，等. 滑坡易发性预测建模的不确定性：不同“非滑坡样本”选择方式的影响[J]. 工程科学与技术，2024，56(1)：169–182.(HUANG Faming，ZENG Siyi，YAO Chi，et al. Uncertainties in landslide susceptibility prediction modeling：Influence of different selection methods of“non-landslide samples”[J]. Advanced Engineering Sciences，2024，56(1)：169–182.(in Chinese))
[122] 王世宝，庄建琦，郑 佳，等. 基于深度学习的 CZ 铁路康定—理塘段滑坡易发性评价[J]. 工程地质学报，2022，30(3)：908–919. (WANG Shibao，ZHUANG Jianqi，ZHENG Jia，et al. Landslide susceptibility evaluation based on deep learning along Kangding—Litang section of CZ Railway[J]. Journal of Engineering Geology，2022，30(3)：908–919.(in Chinese))
[123] AL-NAJJAR H A，PRADHAN B，BEYDOUN G，et al. A novel method using explainable artificial intelligence(XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset[J]. Gondwana Research，2023，123：107–124.
[124] CHEN C，FAN L. An attribution deep learning interpretation model for landslide susceptibility mapping in the three gorges reservoir area[J]. Ieee Transactions on Geoscience and Remote Sensing，2023，61：3000515.
[125] LIU S L，WANG L Q，ZHANG W A，et al. A physics-informed data-driven model for landslide susceptibility assessment in the Three Gorges Reservoir area[J]. Geoscience Frontiers，2023，14(5)：101621.
[126] YU L B，PRADHAN B，WANG Y. A comparative study of various combination strategies for landslide susceptibility mapping considering landslide types[J]. Geoscience Frontiers，2025，16(2)：101999.
[127] YANG Z L，LIU C，NIE R H，et al. Research on uncertainty of landslide susceptibility prediction-bibliometrics and knowledge graph analysis[J]. Remote Sensing，2022，14(16)：3 879.
[128] 苏晓军，张 毅，贾 俊，等. 基于 InSAR 技术的秦岭南部略阳县潜在滑坡灾害识别研究[J]. 山地学报，2021，39(1)：59–70.(SU Xiaojun，ZHANG Yi，JIA Jun，et al. InSAR-based monitoring and identification of potential landslides in Lueyang County，the Southern Qinling Mountains，China[J]. Mountain Research，2021，39(1)：59–70.(in Chinese))
[129] HUANG W B，DING M T，LI Z H，et al. Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms[J]. Catena，2023，222：106866.
[130] XIAO X，ZOU Y，HUANG J C，et al. An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest[J]. Geomatics Natural Hazards and Risk，2024，15(1)：2347421.
[131] 陈丹璐，孙德亮，文海家，等. 基于不同因子筛选方法的LightGBM-SHAP滑坡易发性研究[J]. 北京师范大学学报：自然科学版，2024，60(1)：148–158.(CHEN Danlu，SUN Deliang，WEN Haijia，et al. LightGBM-SHAP landslide susceptibility by different factor screening methods[J]. Journal of Beijing Normal University：Natural Science，2024，60(1)：148–158.(in Chinese))
[132] CHANG Z，CATANI F，HUANG F，et al. Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors[J]. Journal of Rock Mechanics Geotechnical Engineering，2023，15(5)：1 127–1 143.
[133] DENG N，LI Y，MA J，et al. A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit[J]. Frontiers in Environmental Science，2022，10：1009433.
[134] YANG L，CUI Y L，XU C，et al. Application of coupling physics-based model TRIGRS with random forest in rainfall-induced landslide-susceptibility assessment[J]. Landslides，2024，21(9)：2 179–2 193.
[135] ZHANG Q，LIANG Z，LIU W，et al. Landslide susceptibility prediction：improving the quality of landslide samples by isolation forests[J]. Sustainability，2022，14(24)：16692.
[136] 王 涛，吴树仁，石菊松. 国际滑坡风险评估与管理指南研究综述[J]. 地质通报，2009，28(8)：1 006–1 019.(WANG Tao，WU Shuren，SHI Jusong. A review of international landslide risk assessment and management guidelines[J]. Geological Bulletin of China，2009，28(8)：1 006–1 019.(in Chinese))
[137] PANCHAL S，SHRIVASTAVA A K. Landslide hazard assessment using analytic hierarchy process(AHP)：A case study of National Highway 5 in India[J]. Ain Shams Engineering Journal，2022，13(3)：101626.
[138] 黄发明，吴敦筱，常志璐，等. 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，2025，44(3)：584–601.(HUANG Faming，WU Dunxiao，CHANG Zhilu，et al. Landslide susceptibility law under the absence of landslide sample and the susceptibility-InSAR multi-source information method considering the potential landslide identification[J]. Chinese Journal of Rock Mechanics and Engineering，2025，44(3)：584–601.(in Chinese))
[139] JIANG W G，RAO P Z，CAO R，et al. Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation[J]. Journal of Geographical Sciences，2017，27(4)：439–462.
[140] ZOU Q，JIANG H，CUI P，et al. A new approach to assess landslide susceptibility based on slope failure mechanisms[J]. Catena，2021，204：105388.
[141] WANG Y，ZHOU C，CAO Y，et al. Utilizing deep learning approach to develop landslide susceptibility mapping considering landslide types[J]. Bulletin of Engineering Geology and the Environment，2024，83(11)：430.
[142] LOCHE M，ALVIOLI M，MARCHESINI I，et al. Landslide susceptibility maps of Italy：Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory[J]. Earth-Science Reviews，2022，232：104125.
[143] HUANG F，YIN K，HUANG J，et al. Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine[J]. Engineering Geology，2017，223：11–22.
[144] YAO J M，YAO X，ZHAO Z，et al. Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation：a case study of the upper Jinsha River[J]. Geomatics Natural Hazards and Risk，2023，14(1)：2212833.
[145] AKTAS H，SAN B T. Landslide susceptibility mapping using an automatic sampling algorithm based on two level random sampling[J]. Computers and Geosciences，2019，133：104329.
[146] JIANG Z，WANG M，LIU K. Comparisons of convolutional neural network and other machine learning methods in landslide susceptibility assessment：a case study in Pingwu[J]. Remote Sensing，2023，15(3)：798.
[147] 张向营，张春山，孟华君，等. 基于GIS和信息量模型的京张高铁滑坡易发性评价[J]. 地质力学学报，2018，24(1)：96–105.(ZHANG Xiangying，ZHANG Chunshan，MENG Huajun，et al. Landslide susceptibility assessment of new Jing-Zhang high-speed railway based on GIS and information value model[J]. Journal of Geomechanics，2018，24(1)：96–105.(in Chinese))
[148] ZHANG Y J，AYYUB B M，GONG W P，et al. Risk assessment of roadway networks exposed to landslides in mountainous regions-a case study in Fengjie County，China[J]. Landslides，2023，20(7)：1 419–1 431.
[149] NING S，GE Y G，BAI S B，et al. I-D threshold analysis of rainfall-triggered landslides based on TRMM precipitation data in wudu，china[J]. Remote Sensing，2023，15(15)：3 892.
[150] 付 杰，张 青，仉文岗. 基于多源数据与机器学习的降雨型滑坡灾害危险性动态预警[J]. 地质论评，2024，70(增1)：217–218.(FU Jie，ZHANG Qing，ZHANG Wwengang. Dynamic warning of rainfall-induced landslide hazard based on multi-source data and machine learning[J]. Geological Review，2024，70(Supp.1)：217–218.(in Chinese)) |
|
|
|
2025, Vol. 44 
