Geological Hazard Prediction and Prevention: A Review of Mechanisms, Monitoring, and Mitigation
Article Sidebar
Main Article Content
As industrialization expands, modern infrastructure increasingly collides with volatile geological environments, driving a sharp escalation of complex, cascading geohazards. These destructive hazard chains represent a critical global threat to structural and economic resilience. Despite this escalating risk, current early warning systems predominantly rely on fragmented, static methodologies that fail to capture the dynamic reality of temporal hazard evolution. Furthermore, contemporary predictive models are bifurcated between deterministic physical models demanding exhaustive geotechnical parameters and data-driven artificial intelligence algorithms functioning as opaque black boxes devoid of physical interpretability. To resolve these limitations, this review systematically evaluates the modern geohazard landscape through a coupled active-passive conceptual model. This framework systematically evaluates how primary high-energy failures trigger subsequent instability in surrounding geomaterials. The synthesis reveals that integrating space-air-ground multi-scale monitoring including orbital InSAR, UAV photogrammetry, and distributed ground sensors establishes a vital surveillance continuum for early hazard identification. Because these heterogeneous data streams possess significant environmental noise, rigorous multi-source data fusion remains essential to minimize false alarms and resolve spatial discontinuities. Analytically, physically based models provide indispensable mechanical transparency by explicitly simulating material deformation, whereas data-driven ensemble algorithms excel at processing high-dimensional, nonlinear geospatial inputs. Ultimately, transitioning toward proactive, real-time risk reduction demands the integration of physics-informed neural networks (PINNs) that embed geomechanical constraints into computational pipelines. Coupling these hybrid architectures with interactive digital twin technologies will transform static hazard mapping into dynamic virtual environments, empowering engineers to successfully mitigate evolving disaster chains globally.
Downloads
References
Zhang, S., Song, J., Zhan, L., Xu, Q., Zhang, L., & Chen, Y. (2025). Geological-environmental hazard chain: Phenomena, process, and principle. In Science China Earth Sciences (Vol. 68, Number 10, pp. 3085–3107). Science China Press. https://doi.org/10.1007/s11430-024-1602-x
Gupta, K., Satyam, N., Falak, & Rahim, S. (2026). Mapping Co-seismic Landslide Susceptibility: An Overview of Current and Emerging Methods. Indian Geotechnical Journal. https://doi.org/10.1007/s40098-026-01495-5
Zhang, X., Huang, S., & Gao, B. (2026). Full-waveform CNN–transformer neural network for regional coseismic landslide susceptibility modeling: A case study of the 2022 Luding earthquake, China. Engineering Geology, 362. https://doi.org/10.1016/j.enggeo.2025.108520
Wang, H., Sun, P., Mao, J., Wang, T., & Li, K. (2026). Dynamic-process-based quantitative hazard assessment for earthquake-induced landslide clusters in the Northern Mountain of Tianshui. Landslides. https://doi.org/10.1007/s10346-026-02776-z
Huang, Y., Xu, C., Shao, X., He, X., Xiao, Z., Xu, X., Xie, Y., Nie, X., & Li, X. (2026). Landslide assessment research in the three gorges reservoir area: A review of methodological advances and future directions. In Bulletin of Engineering Geology and the Environment (Vol. 85, Number 2). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s10064-025-04733-x
Zhou, M., Mei, G., Ma, Z., Xu, N., & Peng, J. (2026). AI for geosafety: motivations, advances, challenges, and opportunities. In Bulletin of Engineering Geology and the Environment (Vol. 85, Number 5). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s10064-026-04961-9
Alonso-Díaz, A., Fontes, M., Teixeira, A. C., Wdowinski, S., & Sousa, J. J. (2026). Multi-Temporal InSAR and Machine Learning for Geohazard Monitoring: A Systematic Review with Emphasis on Noise Mitigation and Model Transferability. In Remote Sensing (Vol. 18, Number 9). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/rs18091356
Okegbola, M. O., Owolabi, O., Sheng, Z., & Liu, Y. (2026). Integrating Multi-Temporal LiDAR and AHP for Landslide Detection and Susceptibility Mapping in Prince George’s County, Maryland. Journal of Safety Science and Resilience, 100311. https://doi.org/10.1016/j.jnlssr.2026.100311
Naskar, D. C. (2026). Advances in Engineering Geophysics: Concepts and Applications (Vol. 9). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-28000-8
Eze, K. N., Ilesanmi, O. O., Igah, G. C., Abidola, A. Q., Ojefia, F. E., & Adekoya, A. M. (2025). Seismic rockfall risk assessments and mitigation strategies for transportation infrastructure in high-risk regions. Discover Geoscience, 3(1). https://doi.org/10.1007/s44288-025-00182-x
Yao, X., Huang, M., Shi, F., & Yu, L. (2025). Intelligent and Sustainable Classification of Tunnel Water and Mud Inrush Hazards with Zero Misjudgment of Major Hazards: Integrating Large-Scale Models and Multi-Strategy Data Enhancement. Sustainability (Switzerland), 17(24). https://doi.org/10.3390/su172411286
Xing, K., Li, S., Dou, J., Chen, F., Wang, L., Dong, A., Daud, H., & Zhang, L. (2026). Integrating multi-geometry InSAR and explainable machine learning for mapping potential landslide areas in reservoir regions. Remote Sensing of Environment, 341. https://doi.org/10.1016/j.rse.2026.115442
Ma, T., Duan, Y., Duan, W., Wang, H., Tang, C., Wang, K., & Cheng, G. (2025). Research on Intelligent Early Warning System and Cloud Platform for Rockburst Monitoring. Applied Sciences (Switzerland), 15(20). https://doi.org/10.3390/app152011098
Kuang, Z., Li, S., Qiu, S., Huang, Y., & Chang, S. (2026). Identifying zones of rockburst-collapse compound hazards in deep tunnels. Journal of Rock Mechanics and Geotechnical Engineering. https://doi.org/10.1016/j.jrmge.2026.03.019
Zhang, Y., Sun, Y., Yan, Y., Wang, S., & Ge, L. (2026). Research Status, Challenges and Future Perspectives of Geological Hazard Monitoring Methods in Mining Areas. In Remote Sensing (Vol. 18, Number 9). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/rs18091333
Zhang, X., Duan, X., Khalil, U., Muhammadi, M. A., Elmannai, H., Algarni, A. D., & Kucher, D. E. (2026). Multi-sensor deep learning approach for landslide susceptibility mapping under climate and anthropogenic pressures. Physics and Chemistry of the Earth, 143. https://doi.org/10.1016/j.pce.2026.104305
Goli Mokhtari, L. (2026). Optimizing graph neural networks for rockfall susceptibility mapping: a feature selection and hazard prediction approach. Natural Hazards, 122(1). https://doi.org/10.1007/s11069-025-07855-3
Ehsan, M., Anees, M. T., Bakar, A. F. B. A., & Ahmed, A. (2025). A review of geological and triggering factors influencing landslide susceptibility: artificial intelligence-based trends in mapping and prediction. In International Journal of Environmental Science and Technology (Vol. 22, Number 16, pp. 17347–17382). Springer Nature. https://doi.org/10.1007/s13762-025-06741-6
Liao, Z., Wu, J., Deng, Z., Lü, Q., & Yu, Y. (2026). Tectonic and microtopographic controls on aspect-dependent distribution of rainfall-induced shallow landslides. Geomorphology, 511, 110416. https://doi.org/10.1016/j.geomorph.2026.110416
Li, Y., Yang, H., Zheng, L., Qi, W., Chongyi, E., & Gao, T. (2026). Formation mechanism and stability assessment of the Attabad landslide-dammed lake along the China-Pakistan Karakoram Highway using remote sensing technology. Landslides. https://doi.org/10.1007/s10346-026-02697-x
Zhang, J. W., Xu, W. Z., Song, Z. X., Zhang, Y., Yang, J. N., Dong, X. K., Wu, S. K., Bai, X. Y., Zhang, S. L., & Li, X. (2026). An overview of coal-rock-gas compound dynamic disasters in China and the structural classification of risk-prone areas. In Journal of Central South University (Vol. 33, Number 3, pp. 997–1027). Central South University. https://doi.org/10.1007/s11771-026-6221-6
Shi, W., Chen, G., Meng, X., Bian, S., Jin, J., Wu, J., Huang, F., & Chong, Y. (2023). Formation and Hazard Analysis of Landslide Damming Based on Multi-Source Remote Sensing Data. Remote Sensing, 15(19). https://doi.org/10.3390/rs15194691
Bukhari, S. N. U. H., Ahmed, K. S., Ikram, N., Usmani, N. A., Sadiq, S., & Gardezi, S. A. H. (2026). Post-earthquake landslide hazard evolution: Spatio-temporal analysis of active fault zone in Western Himalayas. Journal of Mountain Science, 23(5), 2088–2110. https://doi.org/10.1007/s11629-025-0204-1
Ran, S., Zhou, W., Weng, Y., Chi, F., Wang, D., Cao, X., & Ma, G. (2026). Causative environment-informed active landslide detection with InSAR monitoring: Empowering reservoir slope hazard management. International Journal of Applied Earth Observation and Geoinformation, 146. https://doi.org/10.1016/j.jag.2025.105079
Malik, B. A., & Koner, R. (2024). Comprehensive review of the monitoring and sensing system in slopes with a special focus on the mining sector. In Environmental Science and Pollution Research (Vol. 31, Number 59, pp. 66588–66614). Springer. https://doi.org/10.1007/s11356-024-35693-6
Nan, K., Luo, Y., Xu, Q., Zhao, B., & Song, H. (2025). Reactivation mechanisms of the ancient Dahekou landslide in Hanzhong City, Shaanxi Province, China. Journal of Mountain Science, 22(4), 1245–1260. https://doi.org/10.1007/s11629-024-9130-x
Dolojan, N. L. J., Takahashi, T., Hashimoto, M., Shibayama, A., Nomura, R., Terada, K., & Moriguchi, S. (2025). Integrated multihazard study combining qualitative and quantitative analyses of floods, landslides, and debris flows: A case study on the impacts of Typhoon Yun-Yeung on Iwaki City, Fukushima. International Journal of Disaster Risk Reduction, 127. https://doi.org/10.1016/j.ijdrr.2025.105647
Moradi, S., Heinze, T., Budler, J., Gunatilake, T., Kemna, A., & Huisman, J. A. (2021). Combining site characterization, monitoring and hydromechanical modeling for assessing slope stability. Land, 10(4). https://doi.org/10.3390/land10040423
Lee, D. H., Lee, S. R., & Park, J. Y. (2025). Coupled modeling framework for proactive design of debris-flow barrier placements. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-15290-4
You, Q., Wang, F., Ma, H., Fu, Z., & Feng, Y. (2026). Lithological controls on clustered landslides: a case study of landslides triggered by Typhoon Gaemi (2024) in Zixing, Hunan Province, China. Landslides. https://doi.org/10.1007/s10346-026-02762-5
Wang, J. H., & Xu, W. J. (2025). Slope stability and failure dynamics of rainfall-induced landslide: Algorithm and applications. Computers and Geotechnics, 177. https://doi.org/10.1016/j.compgeo.2024.106919
Zhang, L., Wang, Y., & Liu, J. (2024). Mechanical interpretation of retrogressive failure in mild dip rock slopes induced by slope cutting and rainfall: a case study in Yunnan, China. Bulletin of Engineering Geology and the Environment, 83(1). https://doi.org/10.1007/s10064-023-03503-x
Li, K., Sun, P., Wang, H., & Ren, J. (2024). Insight into failure mechanisms of rainfall induced mudstone landslide controlled by structural planes: From laboratory experiments. Engineering Geology, 343. https://doi.org/10.1016/j.enggeo.2024.107774
Li, Y., Hu, X., Zhang, H., Zheng, H., & Li, N. (2025). Displacement prediction and failure mechanism analysis of rainfall-induced colluvial landslides. Journal of Hydrology, 660. https://doi.org/10.1016/j.jhydrol.2025.133361
Fusco, F., Bordoni, M., Tufano, R., Vivaldi, V., Meisina, C., Valentino, R., Bittelli, M., & De Vita, P. (2022). Hydrological regimes in different slope environments and implications on rainfall thresholds triggering shallow landslides. Natural Hazards, 114(1), 907–939. https://doi.org/10.1007/s11069-022-05417-5
Du, H., Wan, Y., Wang, D., Cao, B., & Liu, G. (2025). Research on deformation characteristics and mechanisms of an open pit coal mine landslide event in extremely cold region. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-27509-5
An, H., & Ouyang, C. (2026). From sky to ground: A multi-source observations-physical model cascade for debris flow forecasting in inaccessible alpine regions. Engineering Geology, 369. https://doi.org/10.1016/j.enggeo.2026.108810
Li, Z., Chen, J. P., Cao, C., Zhang, W., Xu, P., Zheng, L. J., Jing, Y., & Hu, J. Y. (2025). Temperature and deformation response under the influence of continuous typhoons in seasonal permafrost rainfall-induced landslide evolution. Natural Hazards, 121(11), 13093–13116. https://doi.org/10.1007/s11069-025-07312-1
Weng, M. C., Chao, W. A., Yang, C. M., Teo, T. A., Li, K. W., & Hsiao, Y. T. (2026). Assessing earthquake-induced displacements of large-scale landslide by in-situ monitoring and discrete element analysis. Engineering Geology, 368. https://doi.org/10.1016/j.enggeo.2026.108775
Minghui, M., Hongsheng, M., Ningfei, L., Dong, S., Liang, Q., Mingli, L., Xu, Z., & Wulin, M. (2025). The research on the evolution characteristics of multi-geneses Dashui gully high-locality landslide in the upper reaches of Minjiang river. Bulletin of Engineering Geology and the Environment, 84(7). https://doi.org/10.1007/s10064-025-04389-7
He, N., Qu, X., Yang, Z., Xu, L., & Gurkalo, F. (2023). Disaster Mechanism and Evolution Characteristics of Landslide–Debris-Flow Geohazard Chain Due to Strong Earthquake—A Case Study of Niumian Gully. Water (Switzerland), 15(6). https://doi.org/10.3390/w15061218
Zhao, X., Fen, W., Dai, Z., Jiao, W., Geng, J., Xiong, Q., & Zhang, N. (2026). Dynamic rockfall risk assessment using multi-source data fusion and 3D simulation: a case study of Jiaohua rock. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-36769-8
Zhang, M., Xing, A., Li, K., Zhuang, Y., Chang, W., & Liu, Y. (2023). Debris flows in Lebai gully along the Yarlung Tsangpo River in Tibet: characterization, causes, and dynamic prediction of potential debris flows. Environmental Earth Sciences, 82(1). https://doi.org/10.1007/s12665-022-10694-1
Wang, F., Ren, Q., Wu, K., & Zheng, C. (2025). Formation and Evolutionary Mechanisms of Slope Instability Disasters at Exposed Surfaces of Deep-Large Collapse Pit Induced by Underground Mining Activities. Indian Geotechnical Journal. https://doi.org/10.1007/s40098-025-01319-y
Xia, K., Chen, C., Zheng, Y., Zhang, H., Liu, X., Deng, Y., & Yang, K. (2019). Engineering geology and ground collapse mechanism in the Chengchao Iron-ore Mine in China. Engineering Geology, 249, 129–147. https://doi.org/10.1016/j.enggeo.2018.12.028
Yang, Y., Li, J., Shi, W., Yang, C., & Yan, L. (2024). Numerical Investigation of Failure Characteristics and Debris Movement in the Madaling Landslide Using Coupled FDM–DEM. Geotechnical and Geological Engineering, 42(4), 2745–2765. https://doi.org/10.1007/s10706-023-02703-2
Zeng, T., Jin, B., Liu, Y., Glade, T., Wang, F., Yin, K., & Peduto, D. (2024). Cut slope hazard analysis and management based on a double-index precipitation threshold: a case study in the Miaoyuan area (Eastern China). Environmental Earth Sciences, 83(24). https://doi.org/10.1007/s12665-024-11987-3
Yang, F., & Hasan, M. (2026). Comprehensive risk mapping and management of rainfall-induced slope failures in urbanizing subtropical regions. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-49114-w
Fan, J., Zhang, Y., Peng, Y., Xing, Z., Yuan, K., Cui, J., Liu, B., & Zhou, W. (2025). Study on the disaster mechanism and prevention technology of embankment slip-collapse after extreme rainfall in the loess area. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-04920-6
Guo, J., Meng, F., & Guo, J. (2024). Gradual Failure of a Rainfall-Induced Creep-Type Landslide and an Application of Improved Integrated Monitoring System: A Case Study. Sensors, 24(22). https://doi.org/10.3390/s24227409
Fei, P., Yi, Q., Deng, M., Wang, B., Song, Y., & Liu, L. (2025). Study on the Deformation Mechanism of Shallow Soil Landslides Under the Coupled Effects of Crack Development, Road Loading, and Rainfall. Water (Switzerland), 17(8). https://doi.org/10.3390/w17081196
Zhao, W., Cong, P., Ma, X., Yi, M., Liu, C., Gao, J., & Zhang, Y. (2026). Landslide Hazard Identification and Prediction in Complex Mountainous Areas Using Ascending and Descending Orbits InSAR Technology. Sensors, 26(8). https://doi.org/10.3390/s26082455
Yu, Z., Zhan, J., Yao, Z., & Peng, J. (2024). Characteristics and mechanism of a catastrophic landslide-debris flow disaster chain triggered by extreme rainfall in Shaanxi, China. Natural Hazards, 120(8), 7597–7626. https://doi.org/10.1007/s11069-024-06518-z
Zhao, F., Hu, B., Wu, H., Li, W., Zhang, C., Hei, L., Wang, L., Wu, W., & Yang, L. (2026). Landslide susceptibility assessment based on a frequency ratio-ensemble learning framework and InSAR constraints. Advances in Space Research. https://doi.org/10.1016/j.asr.2026.05.039
Guan, X., Chen, P., Liu, H., Li, Z., Qiu, L., Zhu, C., Hong, Y., Yao, Y., & Li, Z. (2026). Dynamic landslide susceptibility assessment in Zhouqu County, China, based on SBAS-InSAR and a Stacking ensemble learning model. Advances in Space Research. https://doi.org/10.1016/j.asr.2026.04.081
Zhou, H., Lu, M., Liu, F., Jiang, K., & Wang, Y. (2026). Landslide Hazard InSAR Monitoring and Stability Evaluation Method for Large Open-Pit Mines: A Case Study of the Baiyinhua Open-Pit Mine, China. Processes, 14(12), 1844. https://doi.org/10.3390/pr14121844
Cheng, X., Zeng, B., Tang, L., Yuan, J., Ai, D., & Huang, W. (2026). Multi-model landslide integrated hazard detection using SBAS-InSAR and U-Net segmentation. Bulletin of Engineering Geology and the Environment, 85(3). https://doi.org/10.1007/s10064-026-04780-y
Yang, A., Wang, Y., Yin, K., Gui, L., Liu, X., Liu, Y., Li, X., & Zhao, B. (2026). Progressive deformation and post-failure residual displacement of the 16 October 2025 Tongren loess landslide, Qinghai, China: insights for long-term extra-high voltage transmission line monitoring. Landslides. https://doi.org/10.1007/s10346-026-02786-x
Guo, Z., Zeng, T., Zhang, Y., Yu, W., Wang, L., Guo, Z., & Glade, T. (2025). A novel hybrid model integrating high resolution remote sensing and stacking ensemble techniques for landslide susceptibility mapping: Application to event-based landslide inventory. Geomorphology, 486. https://doi.org/10.1016/j.geomorph.2025.109886
Ahmadi, P., Valadan Zoej, M. J., Mokhtarzade, M., Kardan, N., Ahmadi, P., & Ghaderpour, E. (2026). TLE-FEDformer: A Frequency-Domain Transformer Framework for Multi-Sensor Multi-Temporal Flood Inundation Mapping. Remote Sensing, 18(6). https://doi.org/10.3390/rs18060895
Gurung, B., Chen, N., Hu, G., Khadka, N., Sapkota, L., Gouli, M. R., & Tian, S. (2026). Integrating machine learning and numerical methods for enhanced landslide susceptibility and hazard mapping in the Bhotekoshi watershed, central Nepal. Journal of Mountain Science. https://doi.org/10.1007/s11629-025-9951-2
Dong, X., Li, S., Ma, R., Tian, W., Zhao, K., Xiang, H., Zhu, J., & Qiu, Y. (2025). Cloud-based slope risk monitoring and early warning system for open-pit coal mines: a case study of Zhonglian Runshi. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-28190-4
Pornbunyanon, T., Jitpat, P., & Suwanno, P. (2026). Rainfall-conditioned landslide susceptibility mapping using an FR–CART hybrid framework in southern Thailand. Progress in Disaster Science, 29. https://doi.org/10.1016/j.pdisas.2026.100530
Rezaie, F., Eghbali, M., Panahi, M., Shafapourtehrany, M., Batur, M., Moeini, H., Özener, H., & Kalantari, Z. (2025). Advanced deep learning–based approaches for semantic segmentation in precise landslide detection and susceptibility assessment. Ecological Informatics, 92. https://doi.org/10.1016/j.ecoinf.2025.103447
Kulkarni, S., Jasani, S., Kulkarni, S., & Wadmare, J. (2025). Datasets, Features, and Advanced Techniques in Landslide Susceptibility Prediction: a Review. In SN Computer Science (Vol. 6, Number 7). Springer. https://doi.org/10.1007/s42979-025-04395-2
Luo, W., Zheng, J., Miao, Y., & Gao, L. (2024). Raspberry Pi-Based IoT System for Grouting Void Detection in Tunnel Construction. Buildings, 14(11). https://doi.org/10.3390/buildings14113349
Cacciuttolo, C., Atencio, E., Komarizadehasl, S., & Lozano-Galant, J. A. (2024). Internet of Things Long-Range-Wide-Area-Network-Based Wireless Sensors Network for Underground Mine Monitoring: Planning an Efficient, Safe, and Sustainable Labor Environment. In Sensors (Vol. 24, Number 21). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/s24216971
Kondo, A., & Matsushi, Y. (2026). Mechanisms of rainfall-induced shallow landslides regulated by hydrological subsurface structures: Cases in granite and granodiorite areas in Northern Abukuma Mountains, Japan. Engineering Geology, 366. https://doi.org/10.1016/j.enggeo.2026.108692
Pei, X. (2022). Design of a Wireless Sensor Network-Based Risk Assessment Algorithm for Cave Collapse. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/8568181
Su, G., Huang, J., Jiang, J., Niu, W., & Liu, Z. (2026). Real-time microseismic monitoring and intelligent early warning of collapse in the shallow-buried tunnel in relatively fractured hard rocks. Engineering Failure Analysis, 185. https://doi.org/10.1016/j.engfailanal.2025.110364
Ramya, H. N., & Nagesh, M. A. (2026). Advanced generative adversarial network for rainfall-induced landslide hazard mapping. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 48(4). https://doi.org/10.1007/s40430-025-06288-0
Wu, P., Yang, L., Li, W., Huang, J., & Xu, Y. (2023). Construction Safety Risk Assessment and Early Warning of Nearshore Tunnel Based on BIM Technology. Journal of Marine Science and Engineering, 11(10). https://doi.org/10.3390/jmse11101996
Li, W., Li, Y., Zhao, Y., & Xu, D. (2025). Optimization of monitoring and early warning technology for mine water disasters using microservices and long short-term memory algorithm. Journal of Supercomputing, 81(4). https://doi.org/10.1007/s11227-025-07033-z
Satyaningsih, R., Jetten, V., Ettema, J., Sopaheluwakan, A., Lombardo, L., & Nuryanto, D. E. (2023). Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia. Natural Hazards, 119(3), 2133–2158. https://doi.org/10.1007/s11069-023-06208-2
Feng, B., Zeng, P., Li, T., Sun, X., Zhu, X., Fan, X., & Xu, Q. (2025). Acceleration stage detection and dynamic model selection for real-time landslide time-of-failure predictions. Acta Geotechnica. https://doi.org/10.1007/s11440-025-02878-3
Pitilakis, K., Fotopoulou, S., Manakou, M., Karafagka, S., Petridis, C., Pitilakis, D., & Raptakis, D. (2024). Towards seismic risk reduction of critical facilities combining earthquake early warning and structural monitoring: a demonstration study. Bulletin of Earthquake Engineering, 22(14), 6893–6927. https://doi.org/10.1007/s10518-024-02046-0
Wang, C., Guo, M., Ji, Z., Gong, Y., Lu, C., Qi, P., Zhu, K., Zhu, C., Gao, M., & Chen, X. (2026). Degradation-buckling-shear mechanism of earthquake-induced bedding rock landslides in the Three-rivers basin: Insights from field investigation and shaking table test. Engineering Geology, 367. https://doi.org/10.1016/j.enggeo.2026.108722
Kuang, Z., Qiu, S., Li, S., Xiao, Y., Feng, G., Huang, Y., & Chang, S. (2025). A new early warning method for rockbursts and compound rockburst–collapse hazards in deeply-buried tunnels based on energy density. Tunnelling and Underground Space Technology, 164. https://doi.org/10.1016/j.tust.2025.106828
Sharma, A., Sajjad, H., Roshani, & Rahaman, M. H. (2024). A systematic review for assessing the impact of climate change on landslides: research gaps and directions for future research. In Spatial Information Research (Vol. 32, Number 2, pp. 165–185). Springer Science and Business Media B.V. https://doi.org/10.1007/s41324-023-00551-z
Abdelkader, M. M., Daoud, A. M. A., Abdel-Gawad, A. G., & Csámer, Á. (2025). Improving landslide susceptibility mapping: The role of non-landslide sample selection in machine learning models. Remote Sensing Applications: Society and Environment, 40. https://doi.org/10.1016/j.rsase.2025.101759
Wang, L., Wang, X., Zhang, H., Xu, Y., Chen, Q., Wu, J., Wu, W., & Ma, D. (2026). Regional landslide susceptibility assessment via coupled modeling and sample optimization: A case study of Dujiangyan, China. Journal of Hydrology: Regional Studies, 66. https://doi.org/10.1016/j.ejrh.2026.103570
Zhang, Y., Yang, Z., Liu, J., Zeng, Y., Sun, Y., & Tan, J. (2026). Vulnerability of mountain road networks to rainfall-induced landslide hazards. Journal of Mountain Science, 23(1), 188–202. https://doi.org/10.1007/s11629-025-9673-5
Singh, G., Kumar, S., Karmakar, R., & Mishra, A. K. (2025). Rapid assessment of landslide exposure to elements at risk for decision support in regional landslide forecasting. Discover Geoscience, 3(1). https://doi.org/10.1007/s44288-025-00299-z
Joshi, B. R., Bhandary, N. P., Acharya, I. P., Niraj, K. C., & Bhandari, C. (2026). Integration of information value with machine learning method for an enhanced predictive performance in landslide susceptibility mapping. Discover Geoscience, 4(1), 152. https://doi.org/10.1007/s44288-026-00517-2
Shen, L., Zhao, X., Liu, H., Sheng, H., Chi, Z., & Zhang, C. (2026). Enhancing landslide susceptibility mapping analysis through neighborhood feature aggregation and interpretable machine learning: Implications for disaster prevention in valley-type urban planning. Journal of Environmental Management, 402. https://doi.org/10.1016/j.jenvman.2026.129117
Nguyen, H. H. D., Song, C. H., & Kim, Y. T. (2025). Semi-quantitative risk assessment: From rainfall-induced landslides to the risk of persons in buildings. Bulletin of Engineering Geology and the Environment, 84(9). https://doi.org/10.1007/s10064-025-04420-x
Dolojan, N. L. J., Moriguchi, S., Hashimoto, M., & Terada, K. (2021). Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis: A case study in Marumori, Miyagi, Japan, during the October 2019 Typhoon Hagibis. Landslides, 18(6), 2039–2057. https://doi.org/10.1007/s10346-020-01617-x
Musaib, A., Aparna, V., & Divya, P. V. (2026). Integrating TRIGRS and RAMMS for the spatiotemporal prediction of rainfall induced landslides and landslide trajectory: a case study. Natural Hazards, 122(3). https://doi.org/10.1007/s11069-025-07779-y
Chavali, R. V. P., Bayati, Z., Tremblay, A., Saeidi, A., Lévesque, Y., & Lambert, M. (2026). Integrated Remote Sensing and Slope Stability Modeling for Back Analysis of Sensitive Clay Landslides. Geotechnical and Geological Engineering, 44(2). https://doi.org/10.1007/s10706-026-03628-2
Meng, G., Li, H., Wu, B., Liu, G., Ye, H., & Zuo, Y. (2023). Prediction of the Tunnel Collapse Probability Using SVR-Based Monte Carlo Simulation: A Case Study. Sustainability (Switzerland), 15(9). https://doi.org/10.3390/su15097098
Sahin, E. K., Demir, S., Ozturk, M., & Duzce, M. S. (2025). Geoscience in the era of generative artificial intelligence (Geo[AI]-LSM): understanding the potential benefits of Google Gemini in producing landslide susceptibility mapping. Advances in Space Research. https://doi.org/10.1016/j.asr.2025.11.048
Utthasini, M., Ilampooranan, I., Singh, S. K., Kanga, S., Kumar, P., Halder, K., Pradhan, B., Srivastava, A. K., Chatterjee, R. S., Chakrabortty, R., Ali, T., & Meraj, G. (2026). Enhancing landslide susceptibility mapping in the Himalayas: geospatial and machine learning with explainable AI (XAI). Gondwana Research, 149, 262–290. https://doi.org/10.1016/j.gr.2025.08.003
Ou, L., Zhang, Y., & Chen, Y. (2026). Intelligent Triggering of Safety Risk Warning in Metro Tunnel Construction: A Two-Stage Framework Integrating Static and Dynamic Data. Buildings, 16(8). https://doi.org/10.3390/buildings16081550
Peng, Y., Li, Q., Wang, H., Liu, Z., & Xia, Y. (2026). Automated TBM tunnel-face collapse detection system based on spatiotemporal fusion and attention mechanism with muck characteristics and operational data. Tunnelling and Underground Space Technology, 174. https://doi.org/10.1016/j.tust.2026.107688
Yang, X., Yan, Y., Zhou, X., Zhu, L., Ma, M., Zhang, J., Chen, Y., & Gao, L. (2025). Risk of Compound Typhoon Disaster Chains: Insights from Southeastern China. International Journal of Disaster Risk Science, 16(5), 870–887. https://doi.org/10.1007/s13753-025-00674-x
Sarna, S., Gutierrez, M., Mooney, M., & Zhu, M. (2022). Predicting Upcoming Collapse Incidents During Tunneling in Rocks with Continuation Length Based on Influence Zone. Rock Mechanics and Rock Engineering, 55(10), 5905–5931. https://doi.org/10.1007/s00603-022-02971-z
Liu, H., Ma, T., Lin, Y., Peng, K., Hu, X., Xie, S., & Luo, K. (2024). Deep Learning in Rockburst Intensity Level Prediction: Performance Evaluation and Comparison of the NGO-CNN-BiGRU-Attention Model. Applied Sciences (Switzerland), 14(13). https://doi.org/10.3390/app14135719
Yu, X., Zhang, X., Feng, X. T., Wang, F., Shi, M., Yang, C., Luo, H., Yang, Y., & Wang, Y. (2026). Fracture evolution and differential mechanical response of surrounding rock in deep tunnel excavation: A case study under complex geological conditions. Engineering Geology, 362. https://doi.org/10.1016/j.enggeo.2025.108534
Calvi, A. (n.d.). Tunnels Collapses: Case Studies and Key Failure Mechanisms.
Capobianco, V., Palau, R. M., Solheim, A., Gisnås, K., Gilbert, G., Danielsson, P., & van der Keur, P. (2024). The potential use of nature-based solutions as natural hazard mitigation measure for linear infrastructure in the Nordic Countries. In Geoenvironmental Disasters (Vol. 11, Number 1). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1186/s40677-024-00287-4
Mondragón-Rodríguez, A., & Quesada-Román, A. (2026). Integrating geomorphological and socio-spatial factors for landslide risk zonation in Atenas, Costa Rica. Discover Hazards, 2(1), 8. https://doi.org/10.1007/s44475-026-00012-9
Jain, S. K., Jain, S. K., & Ahmad, R. (2025). Glacial lake outburst floods (GLOFs): causes, modeling, and mitigation in Indian Himalayan region. GeoJournal , 90(2). https://doi.org/10.1007/s10708-025-11326-4
Gong, Z., Fu, X., & Cheng, X. (2026). Excavation Stability of Tunnel Structure in Water-Rich Areas. Applied Sciences, 16(2), 902. https://doi.org/10.3390/app16020902
Nguyen, H. S., Khau, T. L., & Huynh, T. T. (2025). Investigation of Natural and Human-Induced Landslides in Red Basaltic Soils. Water (Switzerland), 17(9). https://doi.org/10.3390/w17091320
He, X., Xu, C., Huang, Y., Yang, Q., Wang, W., & Xiao, Z. (2026). Evolution of Geological Hazard Prevention in the Three Gorges Reservoir Area: Insights from Planning and Implementation. Earthquake Research Advances, 100464. https://doi.org/10.1016/j.eqrea.2026.100464
Ling, Q., Chen, W., Zhang, Q., Wang, L., Qu, W., Shu, B., Ren, J., Guo, Q., Qu, J., Li, W., Yang, C., Chen, Y., & Sha, J. (2026). A PINN-based hybrid model with APIDr for interpretable and cross-scenario landslide displacement prediction. Journal of Hydrology, 674. https://doi.org/10.1016/j.jhydrol.2026.135493
Xu, L., Zhanping, S., Xiaole, S., Lianbaichao, L., Tong, W., & Shengyuan, F. (2026). Damage quantification and response surface prediction of blasting-induced slope stability based on DFN. Bulletin of Engineering Geology and the Environment, 85(1). https://doi.org/10.1007/s10064-025-04723-z
Li, Y., Zhang, C., & Zhang, G. (2025). The development characteristics and formation modes of rainstorm-triggered flash flood disasters in the Hengduan Mountains. Journal of Geographical Sciences, 35(3), 619–640. https://doi.org/10.1007/s11442-025-2337-0
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.