Literature Review and Research Progress of Landslide Susceptibility Mapping Based on Knowledge Graph
-
摘要: 滑坡易发性评价是滑坡风险评估的基础和核心内容，开展滑坡易发性文献计量分析可以定量化地分析其研究进展及发展趋势，为国内开展地灾风险评估工作提供参考.利用Web of Science和CNKI数据库，基于CiteSpace可视化知识图谱分析工具对1985-2022年已发表的滑坡易发性文献进行计量分析，并对摘要部分进行了LDA分析，来细分该领域内的研究.结果表明：（1）滑坡易发性评价仍然是当前的研究热点，中国是滑坡易发性研究较为活跃的国家且国际间合作较多；（2）滑坡易发性领域发文量前10的作者中4位来自中国，中国科学院成为发文最多的机构，接收易发性评价类文章最多的中英文期刊分别是《中国地质灾害与防治学报》和《Natural Hazard》，中国国家自然科学基金和国土资源大调查项目大力资助了滑坡易发性课题的研究；（3）近5年来，机器学习模型（包括深度学习等）在滑坡易发性的应用快速增长，已成为最热门的研究方法；（4）为了实现滑坡易发性建模的精简化和智能化，并进一步提高滑坡易发性评价结果的精度和实用性，滑坡易发性在滑坡编目、指标体系、评价单元、评价模型、联接方法和精度评价等方面还需开展深入探索.Abstract: Landslide susceptibility mapping (LSM) is the foundation and critical part of landslide risk assessment. The bibliometric analysis of LSM literature can be applied to quantitatively analyze the research progress and development trend. The result will provide references for geological hazard risk assessment in China. In this study, based on the Web of Science and CNKI databases, the CiteSpace visual knowledge graph analysis tool has been used to carry out bibliometric analysis of LSM literature from 1985 to 2022. Moreover, the LDA analysis has been conducted on the abstract to subdivide the research in this field. The results show that: (1) LSM is still a research hotspot at present. In China, there are a large number of studies and international cooperation about LSM. (2) Four of the top 10 authors in the number of published papers on LSM are from China. The institution that has published the most papers on LSM is the Chinese Academy of Sciences. The Chinese Journal of Geological Hazard and Control is the most popular Chinese journal and the Natural Hazardsis the most popular English journals to publish LSM papers. The research on the subject of LSM has been substantially funded by the National Natural Science Foundation of China and the National Land and Resources Survey Project. (3) In the past five years, machine learning models (including deep learning, etc.) have been widely used as the most popular LSM models. (4) In order to achieve the simplification and intelligence of landslide susceptibility modeling and to improve the accuracy and practicability of the LSM results, the following parts of LSM, including the landslide inventory, conditioning factors, assessment unit, assessment model, connection methods and accuracy verification, need to be deeply explored in further studies.
-
图 1 滑坡易发性年发文量变化情况（数据截至2022年9月30日）
Fig. 1. Changes in landslide susceptibility annual publication volume (data as at 30 September 2022)
图 4 WOS数据库中机构之间的合作关系网络
Fig. 4. Cooperative relationship network between institutions in WOS database
图 5 CNKI数据库中机构之间的合作关系网络
Fig. 5. Cooperative relationship network between institutions in CNKI database
图 6 不同国家/地区发表的滑坡易发性文献数量
Fig. 6. Number of landslide susceptibility literature published in different countries/regions
图 8 每年与模型相关的关键词数量的变化情况
Fig. 8. Changes in the number of model-related keywords per year
表 1 在滑坡易发性研究领域中发表文献最多的10位作者
Table 1. The top 10 authors in the field of landslide susceptibility research
WOS数据库 CNKI数据库 作者 发文量 作者 发文量 Pradhan B 186 黄发明 17 Lee S 136 牛瑞卿 13 Pourghasemi HR 110 殷坤龙 8 Bui DT 108 范宣梅 7 Chen W（陈伟） 101 孙德亮 6 Pham BT 92 郭有金 6 Shahabi H 69 文海家 5 Wang Y（王毅） 66 付圣 4 Hong HY（洪浩源） 64 张庭瑜 4 Xu C（许冲） 63 晏鄂川 4 
下载: 导出CSV
表 2 滑坡易发性研究领域中发表文献最多的国家/地区、机构
Table 2. Countries/regions and institutions with the most published papers on landslide susceptibility research
WOS数据库 CNKI数据库 机构 国家/地区 发文量 机构 发文量 Chinese Academy of Sciences 中国 235 中国地质大学(武汉) 83 Consiglio Nazionale delle Ricerche 意大利 191 成都理工大学 67 China University of Geosciences 中国 173 中国地质大学(北京) 43 Korea Institute of Geoscience Mineral Resources 韩国 148 长安大学 41 Chengdu University of Technology 中国 123 中国地质科学院 26 Duy Tan University 越南 118 南昌大学 23 Tarbiat Modares University 伊朗 112 昆明理工大学 23 Universiti Putra Malaysia 马来西亚 105 西安科技大学 23 Shiraz University 伊朗 102 西南科技大学 15 Sejong University South 韩国 101 中国地质调查局武汉地质调查中心 11
下载: 导出CSV
表 3 滑坡易发性研究领域中发表文献最多的10本期刊
Table 3. Top 10 journals in landslide susceptibility research
WOS数据库 CNKI数据库 期刊/会议期刊 发文量 期刊/会议期刊 发文量 Natural Hazards 303 中国地质灾害与防治学报 28 Landslides 284 科学技术与工程 19 Geomorphology 247 工程地质学报 15 Environmental Earth Sciences 242 岩石力学与工程学报 14 Remote Sensing 188 地质科技通报 13 Natural Hazards and Earth System Sciences 177 自然灾害学报 12 Engineering Geology 169 人民长江 12 Bulletin of Engineering Geology and the Environment 145 安全与环境工程 12 Catena 114 地球科学 12 Geocarto International 113 水文地质工程地质 11
下载: 导出CSV
表 4 在滑坡易发性研究领域中发表文献最多的10家基金/资助机构
Table 4. The top 10 foundations/funding agencies with the most publications on landslide susceptibility research
WOS数据库 CNKI数据库 基金 国家/地区 发文量 基金 发文量 National Natural Science Foundation of China 中国 625 国家自然科学基金 182 European Commission 欧洲 108 国土资源大调查项目 72 National Key Research and Development Program of China 中国 102 国家重点研发计划 35 Fundamental Research Funds for The Central Universities 中国 82 国家科技支撑计划 26 National Basic Research Program of China 中国 67 中国博士后科学基金 13 China Postdoctoral Science Foundation 中国 66 国家高技术研究发展计划（863计划） 11 Ministry of Education Culture Sports Science and Technology 日本 58 国家重点基础研究发展计划（973计划） 11 UK Research Innovation 英格兰 57 中央高校基本科研业务费 10 Chinese Academy of Sciences 中国 53 江西省自然科学基金 8 Japan Society for The Promotion of Science 日本 48 江西省博士后科研项目资助 7
下载: 导出CSV
表 5 特征词表和主题分类结果
Table 5. Feature vocabulary and topic classification results
特征词 主题 篇数 Landslide-susceptibility-factors-using-distance-model-index-slope-study-models 评价因子 626 Model-models-landslide-svm-machine-prediction-auc-performance-study-learning 机器学习 610 Landslide-susceptibility-map-study-hazard-factors-landslides-method-maps-gis 易发性区划 591 Susceptibility-model-landslide-models-based-results-data-study-analysis-spatial 易发性模型 577 Landslides-landslide-slope-susceptibility-study-spatial-occurrence-distribution-factors-land 区域易发性 485 Risk-hazard-vulnerability-natural-hazards-assessment-damage-management-flood-urban 易损性评价 303 Landslide-data-information-hazard-landslides-analysis-assessment-susceptibility-based-paper 滑坡数据库 280 Rainfall-landslide-landslides-model-time-data-based-event-warning-system 滑坡早起预警 221 Landslide-resolution-data-using-dem-digital-mapping-remote-images-satellite 遥感 185 Soil-slope-stability-model-water-failure-shallow-parameters-results-strength 浅层滑坡 182 Debris-flow-flows-rockfall-rock-slope-mass-source-cliff-hazard 崩塌和泥石流 156 Slope-river-rock-deposits-landslides-volcanic-basin-failures-sediment-km 火山活动 137 Earthquake-landslides-seismic-triggered-ground-induced-landslide-slope-earthquakes-fault 地震 133 Landslide-landslides-study-model-susceptibility-factors-typhoon-taiwan-method-rainfall 降雨 81 Root-vegetation-slope-reinforcement-roots-soil-stability-tree-wildfire-surface 植被根系 39 Erosion-soil-processes-sediment-water-weathering-mass-basin-geological-weathered 侵蚀和风化 36 Seismic-resistivity-site-electrical-geophysical-microzonation-response-local-wave-turbidites 地球物理 24 Geohazards-habitat-rock-highway-study-potential-fall-cdt-landscape-zone 公路沿线滑坡易发性 23 Forest-fire-forests-ecological-species-potential-tree-protection-trees-water 生态多样性 16 Subsidence-collapse-sinkhole-sinkholes-karst-coal-land-underground-ground-mine 岩溶和地面塌陷 16
下载: 导出CSV
表 6 不同制图单元优缺点及适用性
Table 6. Advantages, disadvantages and applicability of different mapping units
评价单元 优点 缺点 空间比例尺 小 中 大 栅格单元 技术方法成熟，易于获取和处理各类影响因素的数据 与地质、地貌或其他地形信息关联性不强 适用 基本适用 基本适用 地貌单元 能提供较多地貌相关信息 评价因子间的数学关系较难度量，划分单元具主观性 适用 适用 适用 斜坡单元 考虑了地质灾害孕育环境，与地形地质实际紧密结合，评价结果精度较高 对数据精度要求较高，不适用于较大的凹陷盆地和开阔山谷地区 不适用 适用 基本适用 流域单元 适用于泥石流易发性评价 流域的自动剖分方法实现较困难 适用 适用 不适用 地形单元 对于浅表层地质灾害易发性评价精度较高 对于中厚层以上的地质灾害易发性评价精度不高 不适用 适用 适用 均一条件单元 叠加考虑各类地质、地貌等指标 图层叠加前对因子分类具有较大的主观性 不适用 适用 适用 行政单元 适用于政府行政管理与规划 与影响因子关联性不强 不适用 适用 适用
下载: 导出CSV
表 7 不同斜坡单元划分方法及特点
Table 7. The shortcomings of different slope unit division methods
划分方法 特点 水文分析法（Xie et al., 2004） 划分过程中会出现较多的细小破碎面和不合理长条状面，后期需要进行大量手动修改工作. 地表曲率分水岭法（颜阁等，2017） 缺乏对地形、地质界线和人为活动等要素的考虑，且划分的单元数量多、面积小. 遥感影像光谱与地形剖面曲率相结合(Philip et al., 1998) 受限于遥感影像和DEM的精度，该方法对1:10 000图的划分不易发挥较好的效果. r.slopeunits方法（Alvioli et al., 2016） 仍属于水文过程提取方法的范畴，因此提取结果仍然不能满足landslide稳定分析均一性基本假定. MIA-HSU方法（Wang et al., 2019） 对于缺乏高精DEM的地区，斜坡单元提取数目较少，提取结果较为粗糙，影响后续的分析. GRASS方法（Luo and Liu, 2018） 以DEM为基础，采用地貌学方法将DEM中目标单元的高程与其周围单元的高程进行比较，并采用“三元模式”将其分为10种典型的地貌，最后对DEM及其生成的地貌进行流向分析生成斜坡单元.该方法划分结果好且效率高，缺点是该方法的参数较多. 面向对象的斜坡单元划分方法（Huang et al., 2021） 以研究区的坡向和地形阴影为基础，在斜坡内差异性最小和斜坡间差异最大的基础上，对栅格单元进行合并，生成自然斜坡单元.该方法需要较少的人工检修，划分效率高.
下载: 导出CSV
表 8 不同比例尺下易发性评价模型的适用性
Table 8. Applicability of susceptibility assessment models at different scales
不同空间尺度 评价方法 定性分析 定量分析 知识驱动模型 数据驱动模型 机制驱动模型 国家（< 1∶250 000） 适用 不适用 不适用 区域（1∶250 000~1∶25 000） 不适用 适用 不适用 城镇（1∶25 000~1∶5 000） 不适用 适用 基本适用 特定场地（> 1∶5 000） 不适用 基本适用 不适用
下载: 导出CSV
-
Abedini, M., Ghasemyan, B., Rezaei Mogaddam, M. H., 2017. Landslide Susceptibility Mapping in Bijar City, Kurdistan Province, Iran: A Comparative Study by Logistic Regression and AHP Models. Environmental Earth Sciences, 76(8): 308. https://doi.org/10.1007/s12665-017-6502-3 Abedini, M., Tulabi, S., 2018. Assessing LNRF, FR, and AHP Models in Landslide Susceptibility Mapping Index: A Comparative Study of Nojian Watershed in Lorestan Province, Iran. Environmental Earth Sciences, 77(11): 405. https://doi.org/10.1007/s12665-018-7524-1 Ado, M., Amitab, K., Maji, A. K., et al., 2022. Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey. Remote Sensing, 14(13): 3029. https://doi.org/10.3390/rs14133029 Ai, L., Yang, B. Y., Guo, L., 2020. Landslide Ease Based on Different Models and Elements Comparison of Development Evaluation. Geospatial Information, 18(6): 117-121 (in Chinese with English abstract). doi: 10.3969/j.issn.1672-4623.2020.06.030 Alvioli, M., Marchesini, I., Reichenbach, P., et al., 2016. Automatic Delineation of Geomorphological Slope Units with R. Slopeunits V1.0 and Their Optimization for Landslide Susceptibility Modeling. Geoscientific Model Development, 9(11): 3975-3991. https://doi.org/10.5194/gmd-9-3975-2016 Bourenane, H., Bouhadad, Y., 2021. Impact of Land Use Changes on Landslides Occurrence in Urban Area: The Case of the Constantine City (NE Algeria). Geotechnical and Geological Engineering, 39(6): 1-21. https://doi.org/10.1007/s10706-021-01768-1 Brabb, E. E., 1985. Innovative Approaches to Landslide Hazard and Risk Mapping. In: 4th International Symposium on Landslides. Japan Landslide Society, Tokyo, 17-22. Bui, D. T., Tsangaratos, P., Nguyen, V. T., et al., 2020. Comparing the Prediction Performance of a Deep Learning Neural Network Model with Conventional Machine Learning Models in Landslide Susceptibility Assessment. CATENA, 188: 104426. https://doi.org/10.1016/j.catena.2019.104426 Calderón-Guevara, W., Sánchez-Silva, M., Nitescu, B., et al., 2022. Comparative Review of Data-Driven Landslide Susceptibility Models: Case Study in the Eastern Andes Mountain Range of Colombia. Natural Hazards, 113(2): 1105-1132. https://doi.org/10.1007/s11069-022-05339-2 Cama, M., Conoscenti, C., Lombardo, L., et al., 2016. Exploring Relationships between Grid Cell Size and Accuracy for Debris-Flow Susceptibility Models: A Test in the Giampilieri Catchment (Sicily, Italy). Environmental Earth Sciences, 75(3): 238. https://doi.org/10.1007/s12665-015-5047-6 Chang, M., Zhou, Y., Zhou, C., et al., 2021. Coseismic Landslides Induced by the 2018 Mw 6.6 Iburi, Japan, Earthquake: Spatial Distribution, Key Factors Weight, and Susceptibility Regionalization. Landslides, 18(2): 755-772. https://doi.org/10.1007/s10346-020-01522-3 Chang, Z. L., Huang, F. M., Jiang, S. H., et al., 2023. Slope Unit Extraction and Landslide Susceptibility Prediction Using Multi-Scale Segmentation Method. Advanced Engineering Sciences, 55(1): 184-195 (in Chinese with English abstract). Chen, T., Zhu, L., Niu, R. Q., et al., 2020. Mapping Landslide Susceptibility at the Three Gorges Reservoir, China, Using Gradient Boosting Decision Tree, Random Forest and Information Value Models. Journal of Mountain Science, 17(3): 670-685. https://doi.org/10.1007/s11629-019-5839-3 Chen, W., Yan, X. S., Zhao, Z., et al., 2019. Spatial Prediction of Landslide Susceptibility Using Data Mining-Based Kernel Logistic Regression, Naive Bayes and RBF Network Models for the Long County Area (China). Bulletin of Engineering Geology and the Environment, 78(1): 247-266. https://doi.org/10.1007/s10064-018-1256-z Chen, Y., Dong, J. L., Guo, F., et al., 2022. Review of Landslide Susceptibility Assessment Based on Knowledge Mapping. Stochastic Environmental Research and Risk Assessment, 36(9): 2399-2417. https://doi.org/10.1007/s00477-021-02165-z Chiessi, V., Toti, S., Vitale, V., 2016. Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy). Journal of Geoscience and Environment Protection, 4(12): 1-21. https://doi.org/10.4236/gep.2016.412001 Chung, C. J. F., Fabbri, A. G., 2003. Validation of Spatial Prediction Models for Landslide Hazard Mapping. Natural Hazards, 30(3): 451-472. https://doi.org/10.1023/B: NHAZ.0000007172.62651.2b doi: 10.1023/B:NHAZ.0000007172.62651.2b Corominas, J., van Westen, C., Frattini, P., et al., 2014. Recommendations for the Quantitative Analysis of Landslide Risk. Bulletin of Engineering Geology and the Environment, 73(2): 209-263. https://doi.org/10.1007/s10064-013-0538-8 Di Napoli, M., Carotenuto, F., Cevasco, A., et al., 2020. Machine Learning Ensemble Modelling as a Tool to Improve Landslide Susceptibility Mapping Reliability. Landslides, 17(8): 1897-1914. https://doi.org/10.1007/s10346-020-01392-9 do Pinho, T. M., Augusto Filho, O., 2022. Landslide Susceptibility Mapping Using the Infinite Slope, SHALSTAB, SINMAP, and TRIGRS Models in Serra do Mar, Brazil. Journal of Mountain Science, 19(4): 1018-1036. https://doi.org/10.1007/s11629-021-7057-z Dou, J., Xiang, Z. L., Xu, Q., et al., 2023. Application and Development Trend of Machine Learning in Landslide Intelligent Disaster Prevention and Mitigation. Earth Science, 48(5): 1657-1674 (in Chinese with English abstract). Dou, J., Yunus, A. P., Bui, D. T., et al., 2020a. Improved Landslide Assessment Using Support Vector Machine with Bagging, Boosting, and Stacking Ensemble Machine Learning Framework in a Mountainous Watershed, Japan. Landslides, 17(3): 641-658. https://doi.org/10.1007/s10346-019-01286-5 Dou, J., Yunus, A. P., Merghadi, A., et al., 2020b. Different Sampling Strategies for Predicting Landslide Susceptibilities are Deemed Less Consequential with Deep Learning. Science of the Total Environment, 720: 137320. https://doi.org/10.1016/j.scitotenv.2020.137320 Dou, J., Yunus, A. P., Tien Bui, D., et al., 2019a. Assessment of Advanced Random Forest and Decision Tree Algorithms for Modeling Rainfall-Induced Landslide Susceptibility in the Izu-Oshima Volcanic Island, Japan. Science of the Total Environment, 662: 332-346. https://doi.org/10.1016/j.scitotenv.2019.01.221 Dou, J., Yunus, A. P., Tien Bui, D., et al., 2019b. Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM. Remote Sensing, 11(6): 638. https://doi.org/10.3390/rs11060638 Dou, J., Yunus, A. P., Xu, Y. R., et al., 2019c. Torrential Rainfall-Triggered Shallow Landslide Characteristics and Susceptibility Assessment Using Ensemble Data-Driven Models in the Dongjiang Reservoir Watershed, China. Natural Hazards, 97(2): 579-609. https://doi.org/10.1007/s11069-019-03659-4 Du, J., Glade, T., Woldai, T., et al., 2020. Landslide Susceptibility Assessment Based on an Incomplete Landslide Inventory in the Jilong Valley, Tibet, Chinese Himalayas. Engineering Geology, 270: 105572. https://doi.org/10.1016/j.enggeo.2020.105572 Du, Z. Q., Li, Y., Zhang, Y. T., et al., 2020. Knowledge Graph Construction Method on Natural Disaster Emergency. Geomatics and Information Science of Wuhan University, 45(9): 1344-1355 (in Chinese with English abstract). Đurić, U., Marjanović, M., Radić, Z., et al., 2019. Machine Learning Based Landslide Assessment of the Belgrade Metropolitan Area: Pixel Resolution Effects and a Cross-Scaling Concept. Engineering Geology, 256: 23-38. https://doi.org/10.1016/j.enggeo.2019.05.007 Fang, Z. C., Wang Y., Duan, H. X., et al., 2022. Comparison of General Kernel, Multiple Kernel, Infinite Ensemble and Semi-Supervised Support Vector Machines for Landslide Susceptibility Prediction, 36: 3535-3556. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-022-02208-z Fang, Z. C., Wang, Y., Peng, L., et al., 2020. Integration of Convolutional Neural Network and Conventional Machine Learning Classifiers for Landslide Susceptibility Mapping. Computers and Geosciences, 139: 104470. https://doi.org/10.1016/j.cageo.2020.104470 Fell, R., Corominas, J., Bonnard, C., et al., 2008. Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning. Engineering Geology, 102(3/4): 85-98. https://doi.org/10.1016/j.enggeo.2008.03.022 Fleuchaus, P., Blum, P., Wilde, M., et al., 2021. Retrospective Evaluation of Landslide Susceptibility Maps and Review of Validation Practice. Environmental Earth Sciences, 80(15): 485. https://doi.org/10.1007/s12665- 021-09770-9 doi: 10.1007/s12665-021-09770-9 Garosi, Y., Sheklabadi, M., Pourghasemi, H. R., et al., 2018. Comparison of Differences in Resolution and Sources of Controlling Factors for Gully Erosion Susceptibility Mapping. Geoderma, 330: 65-78. https://doi.org/10.1016/j.geoderma.2018.05.027 Gholami, M., Ghachkanlu, E. N., Khosravi, K., et al., 2019. Landslide Prediction Capability by Comparison of Frequency Ratio, Fuzzy Gamma and Landslide Index Method. Journal of Earth System Science, 128(2): 42. https://doi.org/10.1007/s12040-018-1047-8 Gholamy, A., Kreinovich, V., Kosheleva, O., 2018. Why 70/30 or 80/20 Relation between Training and Testing Sets: A Pedagogical Explanation. Departmental Technical Reports (CS), 1209. Guo, F., Lai, P., Chen, Y., et al., 2022. Influence of Different Environemental Factor Connection Methods on Bengagng Susceptibility Assessment. Bulletin of Soil and Water Conservation, 42(5): 123-130 (in Chinese with English abstract). Guo, J. Y., Guan, J., 2018. Global Research Output in Geological Engineering: A Bibliometric Analysis of Web of Science Publications. Journal of Engineering Geology, 26(5): 1397-1407 (in Chinese with English abstract). Guru, B., Veerappan, R., Sangma, F., et al., 2017. Comparison of Probabilistic and Expert-Based Models in Landslide Susceptibility Zonation Mapping in Part of Nilgiri District, Tamil Nadu, India. Spatial Information Research, 25(6): 757-768. https://doi.org/10.1007/s41324-017-0143-1 Hong, H. Y., Tsangaratos, P., Ilia, I., et al., 2020. Introducing a Novel Multi-Layer Perceptron Network Based on Stochastic Gradient Descent Optimized by a Meta-Heuristic Algorithm for Landslide Susceptibility Mapping. Science of the Total Environment, 742: 140549. https://doi.org/10.1016/j.scitotenv.2020.140549 Huang, F. M., Cao, Z. S., Guo, J. F., et al., 2020a. Comparisons of Heuristic, General Statistical and Machine Learning Models for Landslide Susceptibility Prediction and Mapping. CATENA, 191: 104580. https://doi.org/10.1016/j.catena.2020.104580 Huang, F. M., Cao, Z. S., Jiang, S. H., et al., 2020b. Landslide Susceptibility Prediction Based on a Semi- Supervised Multiple-Layer Perceptron Model. Landslides, 17: 2919-2930. https://doi.org/10.1007/s10346-020-01473-9 Huang, F. M., Cao, Y., Fan, X. M., et al., 2021a. Influence of Different Landslide Boundaries and Their Spatial Shapes on the Uncertainty of Landslide Susceptibility Prediction. Chinese Journal of Rock Mechanics and Engineering, 40(S2): 3227-3240 (in Chinese with English abstract). Huang, F. M., Chen, J. W., Tang, Z. P., et al., 2021b. Uncertainties of Landslide Susceptibility Prediction due to Different Spatial Resolutions and Different Proportions of Training and Testing Datasets. Chinese Journal of Rock Mechanics and Engineering, 40(6): 1155-1169 (in Chinese with English abstract). Huang, F. M., Pan, L. H., Yao, C., et al., 2021c. Landslide Susceptibility Prediction Modelling Based on Semi- Supervised Machine Learning. Journal of Zhejiang University (Engineering Science), 55(9): 1705-1713 (in Chinese with English abstract). Huang, F. M., Chen, B., Mao, D. X., et al., 2023. Landslide Susceptibility Prediction Modeling and Interpretability Based on Self-Screening Deep Learning Model. Earth Science, 48(5): 1696-1710 (in Chinese with English abstract). Huang, F. M., Hu, S. Y., Yan, X. Y., et al., 2022a. Landslide Susceptibility Prediction and Identification of Its Main Environmental Factors Based on Machine Learning Models. Bulletin of Geological Science and Technology, 41(2): 79-90 (in Chinese with English abstract). Huang, F. M., Li, J. F., Wang, J. Y., et al., 2022b. Modelling Rules of Landslide Susceptibility Prediction Considering the Suitability of Linear Environmental Factors and Different Machine Learning Models. Bulletin of Geological Science and Technology, 41(2): 44-59 (in Chinese with English abstract). Huang, F. M., Shi, Y., Ouyang, W. P., et al., 2022c. Landslide Susceptibility Prediction Modeling Based on Weight of Evidence and Chi-Square Automatic Interactive Detection Decision Tree. Journal of Civil and Environmental Engineering, 44(5): 16-28 (in Chinese with English abstract). Huang, F. M., Tao, S. Y., Chang, Z. L., et al., 2021. Efficient and Automatic Extraction of Slope Units Based on Multi-Scale Segmentation Method for Landslide Assessments. Landslides, 18(11): 3715-3731. https://doi.org/10.1007/s10346-021-01756-9 Huang, F. M., Ye, Z., Yao, C., et al., 2020. Uncertainties of Landslide Susceptibility Prediction: Different Attribute Interval Divisions of Environmental Factors and Different Data-Based Models. Earth Science, 45(12): 4535-4549 (in Chinese with English abstract). Huang, Q. L., Chen, W., Tang, X. B., et al., 2017. Study on the Method of Slope Unit Zoning in Regional Geo- Hazards Risk Assessment. Journal of Natural Disasters, 26(5): 157-164 (in Chinese). Huang, W. B., Ding, M. T., Wang, D., et al., 2022. Evaluation of Landslide Susceptibility Based on Layer Adaptive Weighted Convolutional Neural Network Model along Sichuan-Tibet Traffic Corridor. Earth Science, 47(6): 2015-2030 (in Chinese with English abstract). Huang, Y. D., Xu, C., Zhang, X. J., et al., 2022. Bibliometric Analysis of Landslide Research Based on the WOS Database. Natural Hazards Research, 2(2): 49-61. https://doi.org/10.1016/j.nhres.2022.02.001 Jacobs, L., Kervyn, M., Reichenbach, P., et al., 2020. Regional Susceptibility Assessments with Heterogeneous Landslide Information: Slope Unit-Vs. Pixel-Based Approach. Geomorphology, 356: 107084. https://doi.org/10.1016/j.geomorph.2020.107084 Kadirhodjaev, A., Kadavi, P. R., Lee, C. W., et al., 2018. Analysis of the Relationships between Topographic Factors and Landslide Occurrence and Their Application to Landslide Susceptibility Mapping: A Case Study of Mingchukur, Uzbekistan. Geosciences Journal, 22(6): 1053-1067. https://doi.org/10.1007/s12303-018-0052-x Lee, J. H., Sameen, M. I., Pradhan, B., et al., 2018. Modeling Landslide Susceptibility in Data-Scarce Environments Using Optimized Data Mining and Statistical Methods. Geomorphology, 303: 284-298. https://doi.org/10.1016/j.geomorph.2017.12.007 Lee, S., 2019. Current and Future Status of GIS-Based Landslide Susceptibility Mapping: A Literature Review. Korean Journal of Remote Sensing, 35(1): 179-193. Li, J., Zhou, Ch. H., 2003. Appropriate Grid Size for Terrain Based Landslide Risk Assessment in Lantau Island, Hong Kong. National Remote Sensing Bulletin, 7(2): 86-92, 161 (in Chinese). Li, P., Ye, H., Tan, S. C., 2021. Evaluation of Geological Hazards in Yongde County Based on Analytic Hierarchy Process. Research of Soil and Water Conservation, 28(5): 394-399, 406 (in Chinese with English abstract). Li, W. B., Fan, X. M., Huang, F. M., et al., 2021. Uncertainties of Landslide Susceptibility Modeling under Different Environmental Factor Connections and Prediction Models. Earth Science, 46(10): 3777-3795 (in Chinese with English abstract). Li, Y. W., Wang, X. M., Mao, H., 2020. Influence of Human Activity on Landslide Susceptibility Development in the Three Gorges Area. Natural Hazards, 104(3): 2115-2151. https://doi.org/10.1007/s11069-020-04264-6 Lian, Z. P., Xu, Y., Fu, S., et al., 2020. Landslide Susceptibility Assessment Based on Multi-Model Fusion Method: A Case Study in Wufeng County, Hubei Province. Bulletin of Geological Science and Technology, 39(3): 178-186 (in Chinese with English abstract). Liang, Z., Wang, C. M., Duan, Z. J., et al., 2021. A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping. Remote Sensing, 13(8): 1464. https://doi.org/10.3390/rs13081464 Lima, P., Steger, S., Glade, T., 2021. Counteracting Flawed Landslide Data in Statistically Based Landslide Susceptibility Modelling for Very Large Areas: A National-Scale Assessment for Austria. Landslides, 18(11): 3531-3546. https://doi.org/10.1007/s10346-021-01693-7 Lima, P., Steger, S., Glade, T., et al., 2022. Literature Review and Bibliometric Analysis on Data-Driven Assessment of Landslide Susceptibility. Journal of Mountain Science, 19(6): 1670-1698. https://doi.org/10.1007/s11629-021-7254-9 Liu, T., Tan, J. M., Guo, F., et al., 2021. Research on the Method of Weight Correction for Landslide Susceptibility with Artificial Cutting Slope: A Case Study of Shadi Town, Ganzhou City. Journal of Natural Disasters, 30(5): 217-225 (in Chinese with English abstract). Lombardo, L., Mai, P. M., 2018. Presenting Logistic Regression-Based Landslide Susceptibility Results. Engineering Geology, 244: 14-24. https://doi.org/10.1016/j.enggeo.2018.07.019 Lucchese, L. V., de Oliveira, G. G., Pedrollo, O. C., 2020. Attribute Selection Using Correlations and Principal Components for Artificial Neural Networks Employment for Landslide Susceptibility Assessment. Environmental Monitoring and Assessment, 192(2): 129. https://doi.org/10.1007/s10661-019-7968-0 Luo, W., Liu, C. C., 2018. Innovative Landslide Susceptibility Mapping Supported by Geomorphon and Geographical Detector Methods. Landslides, 15(3): 465-474. https://doi.org/10.1007/s10346-017-0893-9 Merghadi, A., Yunus, A. P., Dou, J., et al., 2020. Machine Learning Methods for Landslide Susceptibility Studies: A Comparative Overview of Algorithm Performance. Earth-Science Reviews, 207: 103225. https://doi.org/10.1016/j.earscirev.2020.103225 Moragues, S., Lenzano, M. G., Lanfri, M., et al., 2021. Analytic Hierarchy Process Applied to Landslide Susceptibility Mapping of the North Branch of Argentino Lake, Argentina. Natural Hazards, 105(1): 915-941. https://doi.org/10.1007/s11069-020-04343-8 Pham, B. T., Van Dao, D., Acharya, T. D., et al., 2021. Performance Assessment of Artificial Neural Network Using Chi-Square and Backward Elimination Feature Selection Methods for Landslide Susceptibility Analysis. Environmental Earth Sciences, 80(20): 686. https://doi.org/10.1007/s12665-021-09998-5 Pourghasemi, H. R., Teimoori Yansari, Z., Panagos, P., et al., 2018. Analysis and Evaluation of Landslide Susceptibility: A Review on Articles Published during 2005-2016 (Periods of 2005-2012 and 2013-2016). Arabian Journal of Geosciences, 11(9): 193. https://doi.org/10.1007/s12517-018-3531-5 Prakash, N., Manconi, A., Loew, S., 2020. Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sensing, 12(3): 346. https://doi.org/10.3390/rs12030346 Reichenbach, P., Rossi, M., Malamud, B. D., et al., 2018. A Review of Statistically-Based Landslide Susceptibility Models. Earth-Science Reviews, 180: 60-91. https://doi.org/10.1016/j.earscirev.2018.03.001 Robinson, T. R., Rosser, N. J., Densmore, A. L., et al., 2017. Rapid Post-Earthquake Modelling of Coseismic Landslide Intensity and Distribution for Emergency Response Decision Support. Natural Hazards and Earth System Sciences, 17(9): 1521-1540. https://doi.org/10.5194/nhess-17-1521-2017 Sheng, M. Q., Liu, Z. X., Zhang, X. Q., et al., 2021. Landslide Susceptibility Prediction Based on Frequency Ratio Analysis and Support Vector Machine. Science Technology and Engineering, 21(25): 10620-10628 (in Chinese with English abstract). doi: 10.3969/j.issn.1671-1815.2021.25.009 Shinoda, M., Miyata, Y., Kurokawa, U., et al., 2019. Regional Landslide Susceptibility Following the 2016 Kumamoto Earthquake Using Back-Calculated Geomaterial Strength Parameters. Landslides, 16(8): 1497-1516. https://doi.org/10.1007/s10346-019-01171-1 Shirzadi, A., Solaimani, K., Roshan, M. H., et al., 2019. Uncertainties of Prediction Accuracy in Shallow Landslide Modeling: Sample Size and Raster Resolution. CATENA, 178: 172-188. https://doi.org/10.1016/j.catena.2019.03.017 Sun, D. L., Shi, S. X., Wen, H. J., et al., 2021. A Hybrid Optimization Method of Factor Screening Predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping. Geomorphology, 379: 107623. https://doi.org/10.1016/j.geomorph.2021.107623 Sun, D. L., Wen, H. J., Wang, D. Z., et al., 2020a. A Random Forest Model of Landslide Susceptibility Mapping Based on Hyperparameter Optimization Using Bayes Algorithm. Geomorphology, 362: 107201. https://doi.org/10.1016/j.geomorph.2020.107201 Sun, D. L., Xu, J. H., Wen, H. J., et al., 2020b. An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China. Journal of Earth Science, 31(6): 1068-1086. https://doi.org/10.1007/s12583-020-1072-9 Tang, H. M., Li, C. D., Hu, X. L., et al., 2015. Evolution Characteristics of the Huangtupo Landslide Based on in Situ Tunneling and Monitoring. Landslides, 12(3): 511-521. https://doi.org/10.1007/s10346-014-0500-2 Tanyu, B. F., Abbaspour, A., Alimohammadlou, Y., et al., 2021. Landslide Susceptibility Analyses Using Random Forest, C4.5, and C5.0 with Balanced and Unbalanced Datasets. CATENA, 203: 105355. https://doi.org/10.1016/j.catena.2021.105355 Tehrani, F. S., Calvello, M., Liu, Z. Q., et al., 2022. Machine Learning and Landslide Studies: Recent Advances and Applications. Natural Hazards, 114(2): 1197-1245. https://doi.org/10.1007/s11069-022-05423-7 Tian, N. M., Lan, H. X., Wu, Y. M., et al., 2020. Performance Comparison of BP Artificial Neural Network and CART Decision Tree Model in Landslide Susceptibility Prediction. Journal of Geo-Information Science, 22(12): 2304-2316 (in Chinese with English abstract). Tian, Y., Ju, N. P., Xie, M. L., et al., 2022. Analysis of the Impact of Landslide Cataloguing Expression Patterns on the Evaluation Results of Landslide Susceptibility. Journal of Chengdu University of Technology (Science & Technology Edition), 49(5): 606-615 (in Chinese with English abstract). doi: 10.3969/j.issn.1671-9727.2022.05.10 Tie, Y. B., Xu, W., Xiang, B. L., et al., 2022. The Thoughts on Construction of "Double-Control of Point and Zone" System of Geological Hazard Risk in Southwest China. The Chinese Journal of Geological Hazard and Control, 33(3): 106-113 (in Chinese with English abstract). Tie, Y. B., Xu, Y., Zhang, Y., et al., 2020. Main Progresses and Achievements of Geological Hazards Survey in Hilly Area of Southern China. Geological Survey of China, 7(2): 1-12 (in Chinese with English abstract). Tsangaratos, P., Ilia, I., 2016. Comparison of a Logistic Regression and Naïve Bayes Classifier in Landslide Susceptibility Assessments: The Influence of Models Complexity and Training Dataset Size. CATENA, 145: 164-179. https://doi.org/10.1016/j.catena.2016.06.004 Ullah, K., Wang, Y., Fang, Z. C., et al., 2022. Multi-Hazard Susceptibility Mapping Based on Convolutional Neural Networks. Geoscience Frontiers, 13: 101425. http://doi-org/10.1016/j.gsf.2022.101425 doi: 10.1016/j.gsf.2022.101425 Wang, K., Zhang, S. J., Delgado-Téllez, R., et al., 2019a. A New Slope Unit Extraction Method for Regional Landslide Analysis Based on Morphological Image Analysis. Bulletin of Engineering Geology and the Environment, 78(6): 4139-4151. https://doi.org/10.1007/s10064-018-1389-0 Wang, Y., Fang, Z. C., Hong, H. Y., 2019b. Comparison of Convolutional Neural Networks for Landslide Susceptibility Mapping in Yanshan County, China. Science of the Total Environment, 666: 975-993. https://doi.org/10.1016/j.scitotenv.2019.02.263 Wang, K., Zhang, S. J., Wei, F. Q., 2020. Research Progress and Prospects of Extraction Methods for Slope Elements. Journal of Yangtze River Scientific Research Institute, 37(6): 85-93 (in Chinese with English abstract). Wang, Y., Fang, Z. C., Niu, R. Q., et al., 2021. Landslide Susceptibility Analysis Based on Deep Learning. Journal of Geo-Information Science, 23(12): 2244-2260 (in Chinese with English abstract). Wang, Y., Fang, Z. C., Wang, M., et al., 2020. Comparative Study of Landslide Susceptibility Mapping with Different Recurrent Neural Networks. Computers & Geosciences, 138: 104445. https://doi.org/10.1016/j.cageo.2020.104445 Weidner, L., Oommen, T., Escobar-Wolf, R., et al., 2018. Regional-Scale Back-Analysis Using TRIGRS: an Approach to Advance Landslide Hazard Modeling and Prediction in Sparse Data Regions. Landslides, 15(12): 2343-2356. https://doi.org/10.1007/s10346-018-1044-7 Wu, R. Z., Hu, X. D., Mei, H. B., et al., 2021. Spatial Susceptibility Assessment of Landslides Based on Random Forest: A Case Study from Hubei Section in the Three Gorges Reservoir Area. Earth Science, 46(1): 321-330 (in Chinese with English abstract). Wu, X. L., Ren, F., Niu, R. Q., et al., 2013. Landslide Spatial Prediction Based on Slope Units and Support Vector Machines. Geomatics and Information Science of Wuhan University, 38(12): 1499-1503 (in Chinese). Wu, X. L., Yang, J. Y., Niu, R. Q., 2020. A Landslide Susceptibility Assessment Method Using SMOTE and Convolutional Neural Network. Geomatics and Information Science of Wuhan University, 45(8): 1223-1232 (in Chinese with English abstract). Xia, D., Tang, H. M., Sun, S. X., et al., 2022. Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification. Remote Sensing, 14(11): 2707. https://doi.org/10.3390/rs14112707 Xie, M. W., Esaki, T., Zhou, G. Y., 2004. GIS-Based Probabilistic Mapping of Landslide Hazard Using a Three-Dimensional Deterministic Model. Natural Hazards, 33(2): 265-282. https://doi.org/10.1023/B: NHAZ.0000037036.01850.0d doi: 10.1023/B:NHAZ.0000037036.01850.0d Xu, G. Q., Zhou, Y., 2017. Study on the Subdivision Method of Geological Disasters in the Residential Area Based on Geomorphology Unit. World Nonferrous Metals, (11): 137-138 (in Chinese with English abstract). Xu, Q., Dong, X. J., Li, W. L., 2019. Integrated Space-Air-Ground Early Detection, Monitoring and Warning System for Potential Catastrophic Geohazards. Geomatics and Information Science of Wuhan University, 44(7): 957-966 (in Chinese with English abstract). Yan, G., Liang, S. Y., Zhao, H. L., 2017. An Approach to Improving Slope Unit Division Using GIS Technique. Scientia Geographica Sinica, 37(11): 1764-1770 (in Chinese with English abstract). Yang, C., 2016. Assessing the Landslide Susceptibility of Watershed Units of Fujian Province Based on GIS (Dissertation). Fujian Normal University, Fuzhou (in Chinese with English abstract). Ye, R. Q., Li, S. Y., Guo, F., et al., 2021. Rs and GIS Analysis on Relationship between Landslide Susceptibility and Land Use Change in Three Gorges Reservoir Area. Journal of Engineering Geology, 29(3): 724-733 (in Chinese with English abstract). Zhang, M. S., Xue, Q., Jia, J., et al., 2019. Methods and Practices for the Investigation and Risk Assessment of Geo-Hazards in Mountainous Towns. Northwestern Geology, 52(2): 125-135 (in Chinese with English abstract). Zhang, Q., Zhao, C. Y., Chen, X. R., 2022. Technical Progress and Development Trend of Geological Hazards Early Identification with Multi-Source Remote Sensing. Acta Geodaetica et Cartographica Sinica, 51(6): 885-896 (in Chinese with English abstract). Zhang, W. G., He, Y. W., Wang, L. Q., et al., 2023. A Machine Learning Method for Landslide Susceptibility Analysis Based on Water System Zoning: A Case Study of Fengjie County, Chongqing. Earth Science, 48(5): 2024-2038 (in Chinese with English abstract). Zhao, Y., Wang, R., Jiang, Y. J., et al., 2019. GIS-Based Logistic Regression for Rainfall-Induced Landslide Susceptibility Mapping under Different Grid Sizes in Yueqing, Southeastern China. Engineering Geology, 259: 105147. https://doi.org/10.1016/j.enggeo.2019.105147 Zhou, X. T., 2023. Recognition and Dynamic Susceptibility Assessment of Landslides Based on Multi-Source Data (Dissertation). East China University of Technology, Nanchang (in Chinese with English abstract). Zhou, X. Z., Wen, H. J., Li, Z. W., et al., 2022. An Interpretable Model for the Susceptibility of Rainfall-Induced Shallow Landslides Based on SHAP and XGBoost. Geocarto International, 37(26): 13419-13450. https://doi.org/10.1080/10106049.2022.2076928 艾力, 杨冰玉, 郭丽, 2020. 基于不同模型和单元的滑坡易发性评价比较. 地理空间信息, 18(6): 117-121. doi: 10.3969/j.issn.1672-4623.2020.06.030 常志璐, 黄发明, 蒋水华, 等, 2023. 基于多尺度分割方法的斜坡单元划分及滑坡易发性预测. 工程科学与技术, 55(1): 184-195. https://www.cnki.com.cn/Article/CJFDTOTAL-SCLH202301016.htm 窦杰, 向子林, 许强, 等, 2023. 机器学习在滑坡智能防灾减灾中的应用与发展趋势. 地球科学, 48(5): 1657-1674. doi: 10.3799/dqkx.2022.419 杜志强, 李钰, 张叶廷, 等, 2020. 自然灾害应急知识图谱构建方法研究. 武汉大学学报(信息科学版), 45(9): 1344-1355. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202009004.htm 郭飞, 赖鹏, 陈洋, 等, 2022. 不同环境因子联接方法对崩岗易发性评价的影响. 水土保持通报, 42(5): 123-130. https://www.cnki.com.cn/Article/CJFDTOTAL-STTB202205016.htm 郭静芸, 关静, 2018. 基于Web of Science数据库的地质工程研究文献计量分析. 工程地质学报, 26(5): 1397-1407. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202106015.htm 黄发明, 曹昱, 范宣梅, 等, 2021a. 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. 岩石力学与工程学报, 40(S2): 3227-3240. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2021S2023.htm 黄发明, 陈佳武, 唐志鹏, 等, 2021b. 不同空间分辨率和训练测试集比例下的滑坡易发性预测不确定性. 岩石力学与工程学报, 40(6): 1155-1169. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX202106008.htm 黄发明, 潘李含, 姚池, 等, 2021c. 基于半监督机器学习的滑坡易发性预测建模. 浙江大学学报(工学版), 55(9): 1705-1713. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202109012.htm 黄发明, 陈彬, 毛达雄, 等, 2023. 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. 地球科学, 48(5): 1696-1710. doi: 10.3799/dqkx.2022.247 黄发明, 胡松雁, 闫学涯, 等, 2022a. 基于机器学习的滑坡易发性预测建模及其主控因子识别. 地质科技通报, 41(2): 79-90. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202202008.htm 黄发明, 李金凤, 王俊宇, 等, 2022b. 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. 地质科技通报, 41(2): 44-59. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202202005.htm 黄发明, 石雨, 欧阳慰平, 等, 2022c. 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. 土木与环境工程学报(中英文), 44(5): 16-28. https://www.cnki.com.cn/Article/CJFDTOTAL-JIAN202205003.htm 黄发明, 叶舟, 姚池, 等, 2020. 滑坡易发性预测不确定性: 环境因子不同属性区间划分和不同数据驱动模型的影响. 地球科学, 45(12): 4535-4549. doi: 10.3799/dqkx.2020.247 黄启乐, 陈伟, 唐绪波, 等, 2017. 区域地质灾害评价中斜坡单元划分方法研究. 自然灾害学报, 26(5): 157-164. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH201705018.htm 黄武彪, 丁明涛, 王栋, 等, 2022. 基于层数自适应加权卷积神经网络的川藏铁路沿线滑坡易发性评价. 地球科学, 47(6): 2015-2030. 李军, 周成虎, 2003. 基于栅格GIS滑坡风险评价方法中格网大小选取分析. 遥感学报, 7(2): 86-92, 161. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200302001.htm 李萍, 叶辉, 谈树成, 2021. 基于层次分析法的永德县地质灾害易发性评价. 水土保持研究, 28(5): 394-399, 406. https://www.cnki.com.cn/Article/CJFDTOTAL-STBY202105055.htm 李文彬, 范宣梅, 黄发明, 等, 2021. 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. 地球科学, 46(10): 3777-3795. doi: 10.3799/dqkx.2021.042 连志鹏, 徐勇, 付圣, 等, 2020. 采用多模型融合方法评价滑坡灾害易发性: 以湖北省五峰县为例. 地质科技通报, 39(3): 178-186. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202003022.htm 刘婷, 谭建民, 郭飞, 等, 2021. 人工切坡下滑坡易发性评价中权重修正方法研究: 以赣州市沙地镇为例. 自然灾害学报, 30(5): 217-225. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH202105021.htm 盛明强, 刘梓轩, 张晓晴, 等, 2021. 基于频率比联接法和支持向量机的滑坡易发性预测. 科学技术与工程, 21(25): 10620-10628. doi: 10.3969/j.issn.1671-1815.2021.25.009 田乃满, 兰恒星, 伍宇明, 等, 2020. 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. 地球信息科学学报, 22(12): 2304-2316. doi: 10.12082/dqxxkx.2020.190766 田媛, 巨能攀, 解明礼, 等, 2022. 滑坡编录表达模式对易发性评价结果的影响. 成都理工大学学报(自然科学版), 49(5) : 606-615. doi: 10.3969/j.issn.1671-9727.2022.05.10 铁永波, 徐伟, 向炳霖, 等, 2022. 西南地区地质灾害风险"点面双控" 体系构建与思考. 中国地质灾害与防治学报, 33(3): 106-113. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202203012.htm 铁永波, 徐勇, 张勇, 等, 2020. 南方山地丘陵区地质灾害调查工程主要进展与成果. 中国地质调查, 7(2): 1-12. https://www.cnki.com.cn/Article/CJFDTOTAL-DZDC202002001.htm 王凯, 张少杰, 韦方强, 2020. 斜坡单元提取方法研究进展和展望. 长江科学院院报, 37(6): 85-93. https://www.cnki.com.cn/Article/CJFDTOTAL-CJKB202006019.htm 王毅, 方志策, 牛瑞卿, 等, 2021. 基于深度学习的滑坡灾害易发性分析. 地球信息科学学报, 23(12): 2244-2260. doi: 10.12082/dqxxkx.2021.210057 吴润泽, 胡旭东, 梅红波, 等, 2021. 基于随机森林的滑坡空间易发性评价: 以三峡库区湖北段为例. 地球科学, 46(1): 321-330. doi: 10.3799/dqkx.2020.032 武雪玲, 任福, 牛瑞卿, 等, 2013. 斜坡单元支持下的滑坡易发性评价支持向量机模型. 武汉大学学报(信息科学版), 38(12): 1499-1503. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201312025.htm 武雪玲, 杨经宇, 牛瑞卿, 2020. 一种结合SMOTE和卷积神经网络的滑坡易发性评价方法. 武汉大学学报(信息科学版), 45(8): 1223-1232. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202008013.htm 许国庆, 周宇, 2017. 基于地貌单元的小区地质灾害易发性分区方法研究. 世界有色金属, (11): 137-138. https://www.cnki.com.cn/Article/CJFDTOTAL-COLO201711082.htm 许强, 董秀军, 李为乐, 2019. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警. 武汉大学学报(信息科学版), 44(7): 957-966. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907002.htm 颜阁, 梁收运, 赵红亮, 2017. 基于GIS的斜坡单元划分方法改进与实现. 地理科学, 37(11): 1764-1770. https://www.cnki.com.cn/Article/CJFDTOTAL-DLKX201711019.htm 杨城, 2016. 基于GIS的福建省流域单元滑坡敏感性研究(硕士学位论文). 福州: 福建师范大学. 叶润青, 李士垚, 郭飞, 等, 2021. 基于RS和GIS的三峡库区滑坡易发程度与土地利用变化的关系研究. 工程地质学报, 29(3): 724-733. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202103015.htm 张茂省, 薛强, 贾俊, 等, 2019. 山区城镇地质灾害调查与风险评价方法及实践. 西北地质, 52(2): 125-135. https://www.cnki.com.cn/Article/CJFDTOTAL-XBDI201902017.htm 张勤, 赵超英, 陈雪蓉, 2022. 多源遥感地质灾害早期识别技术进展与发展趋势. 测绘学报, 51(6): 885-896. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202206010.htm 仉文岗, 何昱苇, 王鲁琦, 等, 2023. 基于水系分区的滑坡易发性机器学习分析方法: 以重庆市奉节县为例. 地球科学, 48(5): 2024-2038. doi: 10.3799/dqkx.2022.309 周晓亭, 2023. 基于多源数据的滑坡识别及其易发性动态评价(博士学位论文). 南昌: 东华理工大学. -
点击查看大图
图(10) / 表(8)
计量
- 文章访问数: 1240
- HTML全文浏览量: 300
- PDF下载量: 159
- 被引次数: 0
