Huang, H., Wang, Y., Li, Y., Zhou, Y. & Zeng, Z. Debris flow susceptibility assessment in china: A comparison between traditional statistical and machine learning methods. Remote Sens. 14, 4475. https://doi.org/10.3390/rs14184475 (2022).
Google Scholar
Gao, R. Y. et al. A research on cross-regional debris flow susceptibility mapping based on transfer learning. Remote Sens. 14, 4829. https://doi.org/10.3390/rs14194829 (2022).
Google Scholar
Zhao, Y. et al. Extracting more features from rainfall data to analyze the conditions triggering debris flows. Landslides 19, 2091–2099. https://doi.org/10.1007/s10346-022-01893-9 (2022).
Google Scholar
Liu, X., Yu, C., Shi, P. & Fang, W. Debris flow and landslide hazard mapping and risk analysis in China. Front. Earth Sci. 6, 306–313. https://doi.org/10.1007/s11707-012-0328-9 (2012).
Google Scholar
Guo, Y., Feng, Z., Wang, L., Tian, Y. & Chen, L. Hazard assessment of debris flow: A case study of the Huiyazi debris flow. Water 16, 1349. https://doi.org/10.3390/w16101349 (2024).
Google Scholar
Ji, Y. et al. An interpretable framework for the hazard assessment of debris flow based on an improved AHP-EWM method and the SHAP model: A case study of Heishuiwan gully. Bull. Eng. Geol. Environ. 82, 447. https://doi.org/10.1007/s10064-023-03462-3 (2023).
Google Scholar
Wang, T. et al. A novel method for predicting debris flow hazard: A multi-strategy fusion approach based on the light gradient boosting machine framework. Stoch. Environ. Res. Risk Assess. https://doi.org/10.1007/s00477-025-02955-9 (2025).
Google Scholar
Hürlimann, M., Copons, R. & Altimir, J. Detailed debris flow hazard assessment in andorra: A multidisciplinary approach. Geomorphology 78, 359–372. https://doi.org/10.1016/j.geomorph.2006.02.003 (2006).
Google Scholar
Zhang, A. et al. Risk assessment of the Xigou debris flow in the three Gorges reservoir area. Front. Ecol. Evol. 11, 1264936. https://doi.org/10.3389/fevo.2023.1264936 (2023).
Google Scholar
Hungr, O. & McDougall, S. Two numerical models for landslide dynamic analysis. Comput. Geosci. 35, 978–992. https://doi.org/10.1016/j.cageo.2007.12.003 (2009).
Google Scholar
Peng, S. H. & Lu, S. C. FLO-2D simulation of mudflow caused by large landslide due to extremely heavy rainfall in southeastern Taiwan during typhoon Morakot. J. Mt. Sci. 10, 207–218. https://doi.org/10.1007/s11629-013-2510-2 (2013).
Google Scholar
Franco-Ramos, O. et al. Modelling the 2012 Lahar in a sector of Jamapa gorge (Pico de Orizaba volcano, Mexico) using RAMMS and tree-ring evidence. Water 12, 333. https://doi.org/10.3390/w12020333 (2020).
Google Scholar
Horton, A. J., Hales, T. C., Ouyang, C. & Fan, X. Identifying post-earthquake debris flow hazard using massflow. Eng. Geol. 258, 105134. https://doi.org/10.1016/j.enggeo.2019.05.011 (2019).
Google Scholar
Choi, S. K., Park, J. Y., Lee, D. H. & Fan, X. Assessment of barrier location effect on debris flow based on smoothed particle hydrodynamics (SPH) simulation on 3D terrains. Landslides 18, 217–234. https://doi.org/10.1007/s10346-020-01477-5 (2021).
Google Scholar
Nguyen, H. H. D. et al. A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide. Landslides 22, 149–168. https://doi.org/10.1007/s10346-024-02366-x (2025).
Google Scholar
Song, C. H. et al. Development and empirical application of a debris flow entrainment rate prediction model utilizing generative AI. EGU General Assembly (2025). https://doi.org/10.5194/egusphere-egu25-5281.
Gao, R. Y., Wang, C. M. & Liang, Z. Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area, flowing area and accumulation area of debris flow watersheds. J. Mt. Sci. 18, 1476–1488. https://doi.org/10.1007/s11629-020-6471-y (2021).
Google Scholar
Meng, Z. et al. Effects of frequent debris flows on barrier lake formation, sedimentation and vegetation disturbance, Palongzangbo river, Tibetan plateau. Catena 220, 106697. https://doi.org/10.1016/j.catena.2022.106697 (2023).
Google Scholar
Notti, D. et al. Debris flow and rockslide analysis with advanced photogrammetry techniques based on high-resolution RPAS data. Ponte Formazza case study (NW Alps). Remote Sens. 13, 1797. https://doi.org/10.3390/rs13091797 (2021).
Google Scholar
Chang, M., Tang, C., Van Asch, T. W. J. & Cai, F. Hazard assessment of debris flows in the Wenchuan earthquake-stricken area, South West China. Landslides 14, 1783–1792. https://doi.org/10.1007/s10346-017-0824-9 (2017).
Google Scholar
Zhang, H. W., Liu, F. Z. & Wang, J. C. Hazard assessment of debris flows in Kongpo gyamda, Tibet based on FLO-2D numerical simulation. J. Geomech. 28, 306–318. https://doi.org/10.12090/j.issn.1006-6616.2021117 (2022).
Google Scholar
Martini, M., Baggio, T. & D’Agostino, V. Comparison of two 2-D numerical models for snow avalanche simulation. Sci. Total Environ. 896, 165221. https://doi.org/10.1016/j.scitotenv.2023.165221 (2023).
Google Scholar
Wu, F., Zhang, J., Liu, D., Andreas, M. & Vincent, C. Deep learning-based debris flow hazard detection and recognition system: A case study. Sci. Rep. 15, 6789. https://doi.org/10.1038/s41598-025-86471-4 (2025).
Google Scholar
Li, Y. et al. Debris flow forecasting: disastrous rainfall threshold matters. Landslides https://doi.org/10.1007/s10346-025-02492-0 (2025).
Google Scholar
Hong, H., Pradhan, B., Xu, C. & Bui, D. T. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133, 266–281. https://doi.org/10.1016/j.catena.2015.05.019 (2015).
Google Scholar
Liang, Z., Wang, C., Han, S., Kaleem, U. J. K. & Liu, Y. Classification and susceptibility assessment of debris flow based on a semiquantitative method combination of the fuzzy C-means algorithm, factor analysis and efficacy coefficient. Nat. Hazards Earth Syst. Sci. 20, 1287–1304. https://doi.org/10.5194/nhess-2020-5 (2020).
Google Scholar
Oh, H. J. & Pradhan, B. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput. Geosci. 37, 1264–1276. https://doi.org/10.1016/j.cageo.2010.10.012 (2011).
Google Scholar
Pourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C. & Gokceoglu, C. Landslide susceptibility mapping using support vector machine and GIS at the Golestan province. Iran. J. Earth Syst. Sci. 122, 349–369. https://doi.org/10.1007/s12040-013-0282-2 (2013).
Google Scholar
Zhang, P., Liu, X. & Shu, H. Hazard assessment of debris flow by using FLO-2D and hazard matrix: A case study of Qingshui gully in the Southern Gansu province, China Desalin Water Treat. 315, 650–662. https://doi.org/10.5004/dwt.2023.30108 (2023).
Google Scholar
Erena, S. H., Worku, H. & De Paola, F. Flood hazard mapping using FLO-2D and local management strategies of dire Dawa city, Ethiopia. J. Hydrol. Reg. Stud. 19, 224–239. https://doi.org/10.1016/j.ejrh.2018.09.005 (2018).
Google Scholar
Zeng, P., Chen, J., Chang, M., Sun, X. & Li, T. Uncertainty characterization, propagation, and evaluation in debris flow run-out hazard assessment. Landslides 22, 1275–1290. https://doi.org/10.1007/s10346-024-02423-5 (2025).
Google Scholar
Yang, K. et al. Dynamic hazard assessment of rainfall-induced landslides using gradient boosting decision tree with Google Earth engine in three Gorges reservoir area, China. Water 16, 1638. https://doi.org/10.3390/w16121638 (2024).
Google Scholar
Song, Y. et al. Landslide susceptibility mapping based on weighted gradient boosting decision tree in Wanzhou section of the three Gorges reservoir area (China). ISPRS Int. J. Geo-Inf. 8, 4. https://doi.org/10.3390/ijgi8010004 (2018).
Google Scholar
Chu, Y., Song, W. & Chen, D. Risk identification of mountain torrent hazard using machine learning and bayesian model averaging techniques. Water 16, 1556. https://doi.org/10.3390/w16111556 (2024).
Google Scholar
Cabral, V., Reis, F., Veloso, V., Ogura, A. & Zarft, C. A multi-step hazard assessment for debris-flow prone areas influenced by hydroclimatic events. Eng. Geol. 313, 106961. https://doi.org/10.1016/j.enggeo.2022.106961 (2023).
Google Scholar
Ding, G. L., Wang, Y. H., Yao, K., Liu, H. H. & Wang, Q. Q. Application of early warning technology to Multi-parameter and dynamic monitoring of debris flow—A case study of Nanjiao gully in Fangshan district, Beijing. Urban Geol. 12, 111–116. https://doi.org/10.3969/j.issn.1007-1903.2017.01.020 (2017).
Google Scholar
Nguyen, H. H. D. et al. Explainable artificial intelligence model for the prediction of undrained shear strength. Theor. Appl. Mech. Lett. 15, 100578. https://doi.org/10.1016/j.taml.2025.100578 (2025).
Google Scholar
Do, T. H. et al. Interpretable ensemble learning approaches for predicting unconfined compressive strength of expansive soils. Transp. Infrastruct. Geotech. 12, 152. https://doi.org/10.1007/s40515-025-00609-5 (2025).
Google Scholar
Wang, J., Yu, Y., Wei, X., Gong, Q. & Xiong, H. Run-out effects of debris flows based on numerical simulation. Open. Civ. Eng. J. 10, 848–858. https://doi.org/10.2174/1874149501610010848 (2016).
Google Scholar
Pallàs, R. et al. A pragmatic approach to debris flow hazard mapping in areas affected by hurricane mitch: Example from NW Nicaragua. Eng. Geol. 72, 57–72. https://doi.org/10.1016/j.enggeo.2003.06.002 (2004).
Google Scholar
Mudashiru, R. B., Sabtu, N., Abustan, I. & Balogun, W. Flood hazard mapping methods: A review. J. Hydrol. 603, 126846. https://doi.org/10.1016/j.jhydrol.2021.126846 (2021).
Google Scholar
Amellah, O., Mignosa, P., Prost, F. & Aureli, F. Assessment of flood hazard mapping using a DEM-based approach and 2D hydrodynamic modeling. Water 16, 1844. https://doi.org/10.3390/w16131844 (2024).
Google Scholar
Ennouini, W., Fenocchi, A., Petaccia, G., Persi, E. & Sibilla, S. A complete methodology to assess hydraulic risk in small ungauged catchments based on HEC-RAS 2D rain-on-grid simulations. Nat. Hazards. 120, 7381–7409. https://doi.org/10.1007/s11069-024-06515-2 (2024).
Google Scholar
Huang, J. et al. A hybrid machine-learning model to estimate potential debris-flow volumes. Geomorphology 367, 107333. https://doi.org/10.1016/j.geomorph.2020.107333 (2020).
Google Scholar
Yang, L., Ge, Y., Chen., Wu, Y. & Fu, R. Machine-learning-based prediction modeling for debris flow occurrence: A meta-analysis. Water 16, 923. https://doi.org/10.3390/w16070923 (2024).
Google Scholar
