|
Aleotti, P., Chowdhury, R., 1999. Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58(1):21-44. https://doi.org/10.1007/s100640050066. |
|
Al-Najjar, H., Pradhan, B., Beydoun, G.,et al., 2023. A novel method using explainable artificial intelligence (xai)-based shapley additive explanations for spatial landslide prediction using time-series sar dataset. Gondwana Research, 123:107-124. https://doi.org/10.1016/j.gr.2022.08.004. |
|
Baptista, M. L., Goebel, K., & Henriques, E., 2022. Relation between prognostics predictor evaluation metrics andlocal interpretability shap values. Artificial Intelligence, 306. https://doi.org/10.1016/j.artint.2022.103667. |
|
Broeckx, J., Vanmaercke, M., Duchateau, R., et al., 2018. A data-based landslide susceptibility map of africa. Earth-Science Reviews, 185: 102-121. https://doi.org/10.1016/j.earscirev.2018.05.002. |
|
Cao, S., Guo, C., Yang, W., et al., 2025. Development characteristics and evolutionary mechanisms of large high-elevation landslides in the Deqin section of the upper Lancang River. Earth Science, 1-29 (in Chinese with English abstract). |
|
Cascini, L. 2008. Applicability of landslide susceptibility and hazard zoning at different scales. Engineering Geology, 102(3-4): 164-177. https://doi.org/10.1016/j.enggeo.2008.03.016. |
|
Cemiloglu, A., Zhu, L. C., Mohammednour, A. B., et al., 2023. Landslide susceptibility assessment for maragheh county, iran, using the logistic regression algorithm. Land, 12(7). https://doi.org/10.3390/land12071397. |
|
Chen, W., Zhang, S., Li, R. W., et al., 2018. Performance evaluation of the gis-based data mining techniques of best-first decision tree, random forest, and naive bayes tree for landslide susceptibility modeling. Science of the Total Environment, 644: 1006-1018. https://doi.org/10.1016/j.scitotenv.2018.06.389. |
|
Chen, X. L., Liu, C. G., Chang, Z. F., et al., 2016. The relationship between the slope angle and the landslide size derived from limit equilibrium simulations. Geomorphology, 253: 547-550. https://doi.org/10.1016/j.geomorph.2015.01.036. |
|
Dragicevic, S., Lai, T., Balram, S, et al., 2015. Gis-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat International, 45: 114-125. https://doi.org/10.1016/j.habitatint.2014.06.031. |
|
Fell, R., Cororninas, 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. |
|
Feng, W., Zhao, J., Yi, X., et al., 2025. Characteristics and driving factors of rainfall-induced clustered landslides triggered by the “June 16” extreme rainfall event in the Fujian-Guangdong-Jiangxi border region. Earth Science, 50(10): 4111-4124 (in Chinese with English abstract). |
|
Günther, A., Van den Eeckhaut, M., Malet, J. P., et al., 2014. Climate-physiographically differentiated pan-european landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology, 224: 69-85. https://doi.org/10.1016/j.geomorph.2014.07.011. |
|
He, F., Tan, S. C., Liu, H. J., 2022. Mechanism of rainfall induced landslides in yunnan province using multi-scale spatiotemporal analysis and remote sensing interpretation. Microprocessors and Microsystems, 90. https://doi.org/10.1016/j.micpro.2022.104502. |
|
Huang, F. M., Cao, Z. S., Guo, J. F., et al., 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena, 191. https://doi.org/10.1016/j.catena.2020.104580. |
|
Lan, H. X., Zhou, C. H., Wang, L. J., et al., 2004. Landslide hazard spatial analysis and prediction using gis in the xiaojiang watershed, yunnan, china. Engineering Geology, 76(1): 109-128. https://doi.org/https://doi.org/10.1016/j.enggeo.2004.06.009. |
|
Lee, M. L., Ng, K. Y., Huang, Y. F., et al., 2014. Rainfall-induced landslides in hulu kelang area, malaysia. Natural Hazards, 70(1): 353-375. https://doi.org/10.1007/s11069-013-0814-8. |
|
Li, W. Y., Liu, C., Hong, Y., et al., 2016. A public cloud-based china's landslide inventory database (cslid): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011. Journal of Mountain Science, 13(7): 1275-1285. https://doi.org/10.1007/s11629-015-3659-7. |
|
Li, Z., 2022. Extracting spatial effects from machine learning model using local interpretation method: an example of shap and xgboost. Computers, Environment and Urban Systems, 96: 101845. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2022.101845. |
|
Lin, G. F., Chang, M. J., Huang, Y. C., et al., 2017. Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression. Engineering Geology, 224: 62-74. https://doi.org/10.1016/j.enggeo.2017.05.009. |
|
Liu, B., Guo, H. X., Li, J. L., et al., 2024. Application and interpretability of ensemble learning for landslide susceptibility mapping along the three gorges reservoir area, china. Natural Hazards, 120(5): 4601-4632. https://doi.org/10.1007/s11069-023-06374-3. |
|
Liu, S. L., Wang, L. Q., Zhang, W. A., et al., 2023. A comprehensive review of machine learning-based methods in landslide susceptibility mapping. Geological Journal, 58(6): 2283-2301. https://doi.org/10.1002/gj.4666. |
|
Medina, V., Hürlimann, M., Guo, Z. Z., et al., 2021. Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale. Catena, 201. https://doi.org/10.1016/j.catena.2021.105213. |
|
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. |
|
Riaz, M. T., Basharat, M., Hameed, N., et al., 2018. A data-driven approach to landslide-susceptibility mapping in mountainous terrain: case study from the northwest himalayas, pakistan. Natural Hazards Review, 19(4). https://doi.org/10.1061/(ASCE)NH.1527-6996.0000302. |
|
Sun, D. L., Chen, D. L., Zhang, J. L., et al., 2023. Landslide susceptibility mapping based on interpretable machine learning from the perspective of geomorphological differentiation. Land, 12(5). https://doi.org/10.3390/land12051018. |
|
Sun, D. L., Gu, Q. Y., Wen, H. J., et al., 2023. Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization. Gondwana Research, 123: 89-106. https://doi.org/10.1016/j.gr.2022.07.013. |
|
Sun, D. L., Xu, J. H., Wen, H. J., et al., 2021. Assessment of landslide susceptibility mapping based on bayesian hyperparameter optimization: a comparison between logistic regression and random forest. Engineering Geology, 281. https://doi.org/10.1016/j.enggeo.2020.105972. |
|
Sun, X. L., Liu, M. X., Sima, Z. Q., 2020. A novel cryptocurrency price trend forecasting model based on lightgbm. Finance Research Letters, 32. https://doi.org/10.1016/j.frl.2018.12.032. |
|
Wang, H. J., Liang, Q. X., Hancock, J. T., et al., 2024. Feature selection strategies: a comparative analysis of shap-value and importance-based methods. Journal of Big Data, 11(1). https://doi.org/10.1186/s40537-024-00905-w. |
|
Wang, X. L., Zhang, L. Q., Wang, S. J., et al., 2014. Regional landslide susceptibility zoning with considering the aggregation of landslide points and the weights of factors. Landslides, 11(3): 399-409. https://doi.org/10.1007/s10346-013-0392-6. |
|
Xing, Y., Yue, J. P., Guo, Z. Z., et al., 2021. Large-scale landslide susceptibility mapping using an integrated machine learning model:a case study in the lvliang mountains of china. Frontiers in Earth Science, 9. https://doi.org/10.3389/feart.2021.722491. |
|
Ye, H., Bai, D., Tan, S. C., et al., 2024. Vulnerability assessment of landslides along the yunnan section of the northern tropic of cancer based on fuzzy evidence weight model. Natural Hazards, 120(14): 12705-12727. https://doi.org/10.1007/s11069-024-06696-w. |
|
Youssef, K., Shao, K., Moon, S., et al., 2023. Landslide susceptibility modeling by interpretable neural network. Communications Earth & Environment, 4(1). https://doi.org/10.1038/s43247-023-00806-5. |
|
Zhang, J. Y., Ma, X. L., Zhang, J. L., et al., 2023. Insights into geospatial heterogeneity of landslide susceptibility based on the shap-xgboost model. Journal of Environmental Management, 332. https://doi.org/10.1016/j.jenvman.2023.117357. |
|
Zhang, S., Li, C., Peng, J., et al., 2023. Fatal landslides in china from 1940 to 2020: occurrences and vulnerabilities. Landslides, 20(6),: 1243-1264. https://doi.org/10.1007/s10346-023-02034-6. |
|
Zhang, W. A., Gu, X., Tang, L. B., et al., 2022. Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: comprehensive review and future challenge. Gondwana Research, 109: 1-17. https://doi.org/10.1016/j.gr.2022.03.015. |
|
Zhao, X., Zhao, Z. F., Huang, F. M., et al., 2023. Application of environmental variables in statistically-based landslide susceptibility mapping:a review. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1147427. |
|
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. |
|
Zheng, Y. K., Chen, J. G., Wang, C. B., 2020. Application of Certainty Factor and Random Forest Models for Landslide Susceptibility Assessment in Mangshi, Yunnan, China. Bulletin of Geological Science and Technology, 39(6): 131-144 (in Chinese with English abstract). |
|
附中文参考文献 |
|
曹世超, 郭长宝, 杨为民, 等, 2025. 澜沧江上游德钦段大型高位滑坡发育特征与形成演化机制研究. 地球科学, 1-29. |
|
冯文凯, 赵家琛, 易小宇, 等, 2025. 闽粤赣边区“6·16”强降雨诱发群发滑坡特征与驱动因素. 地球科学, 50(10): 4111-4124. |
|
杨一光, 1991. 云南省综合自然区划.北京: 高等教育出版社, 253 |
|
郑迎凯, 陈建国, 王成彬, 等, 2020. 确定性系数与随机森林模型在云南芒市滑坡易发性评价中的应用. 地质科技通报, 39(6): 131-144. |