Review on Forecasting of Mass Shallow Landslides and Debris Flows

DOI: https://doi.org/10.62517/jcte.202506401

Author(s)

Junfeng Jiang1, Qunhua Zhu1, Juan Han2, Weimin Huang1,*, Cunfen Yang3

Affiliation(s)

1Architecture College, Guangdong Songshan Polytechnic, Shaoguan, Guangdong, China 2Sichuan Earthquake Administration, Chengdu, China 3Yunnan Construction Investment First Survey and Design Co., LTD, Kunming, China *Corresponding Author

Abstract

As a highly destructive natural disaster, the accurate prediction of clustered shallow landslide-debris flow hazards is of great significance for ensuring the safety of people's lives and property and reducing economic losses. In recent years, with the continuous advancement of science and technology, significant progress has been made in the research of forecasting models for clustered shallow landslides and debris flows. Scholars have conducted in-depth studies from multiple perspectives, including the formation mechanisms of landslide-debris flow disaster chains, numerical simulation methods, artificial intelligence prediction technologies, and the integrated application of multi-source monitoring and early warning systems. This paper systematically reviews and summarizes the current research status in the above fields, identifies key future research directions for landslide-debris flow disaster forecasting, and provides a theoretical foundation and technical support for developing more accurate and efficient forecasting systems.

Keywords

Cluster Occurrence; Landslide-Debris Flow; Forecast

References

[1]Gao Y, Yin Y, Li B, Feng Z, Wang W, Zhang N., & Xing A. Characteristics and numerical runout modeling of the heavy rainfall-induced catastrophic landslide-debris flow at Sanxicun, Dujiangyan, China, following the Wenchuan Ms 8.0 earthquake. Landslides,2017, 14(4), 1361-1374. [2]Harada N, Satofuka Y, Mizuyama T. The Impacts of River Channel Blockages Caused by Sliding Embankment Collapses during Earthquakes. Water, 2024, 16(6): 822. [3]Hu K, Cui P, Tian M, et al. Review on the Dynamic Model and Numerical Simulation of Landslides. Journal of Hydraulic Engineering, 2012, 43(S2): 79-84. [4]Zhou J, Cui P, Yang X. Dynamic process analysis for the initiation and movement of the Donghekou landslide-debris flow triggered by the Wenchuan earthquake. Journal of Asian Earth Sciences, 2013, 76: 70-84. [5]Wang H, Liu S, Xu W, et al. Numerical investigation on the sliding process and deposit feature of an earthquake-induced landslide: a case study. Landslides, 2020, 17(11): 2671-2682. [6]Zhu Q, Jiang N, Chen Q, et al. DEM-SPH simulation for the formation and breaching of a landslide-dammed lake triggered by the 2022 Lushan earthquake. Landslides, 2023, 20(9): 1925-1941. [7]Zhang W, Xue Y, Wang X, et al. Analysis of the runout distance and impact force of an actual landslide considering the effect of single rigid barrier using an SPH approach. Acta Geotechnica, 2025, 20(3): 1297-1313. [8]Yang L, Cui Y, Xu C, et al. Application of coupling physics–based model TRIGRS with random forest in rainfall-induced landslide-susceptibility assessment. Landslides, 2024, 21(9): 2179-2193. [9]Liu C. Optimization of negative sample selection for landslide susceptibility map based on machine learning using K-means-KNN algorithm. Earth Science Informatics, 2023, 16(4): 4131-4152. [10]Zhang L, Chen X, Zhang Y, et al. Application of GWO-ELM model to prediction of Caojiatuo landslide displacement in the Three Gorge Reservoir Area. Water, 2020, 12(7): 1860. [11]Banihabib M E, Jurik L, Kazemi M S, et al. A hybrid intelligence model for the prediction of the peak flow of debris floods. Water, 2020, 12(8): 2246. [12]Wang F, Dong S, Feng X. Artificial intelligence based flood and landslide disaster monitoring and dynamic numerical prediction system. Journal of Electrical Systems, 2024, 20(2): 560-572. [13]Deng L, Yuan H, Chen J, et al. Prefabricated acoustic emission array system for landslide Monitoring. Engineering Geology, 2023, 323: 107185. [14]Hao S, Hao W, Jie F, et al. Landslide monitoring and early warning system based on edge computing//ISRM International Symposium-Asian Rock Mechanics Symposium. ISRM, 2021: ISRM-ARMS11-2021-127. [15]Vivaldi V, Bordoni M, Mineo S, et al. Airborne combined photogrammetry—infrared thermography applied to landslide remote Monitoring. Landslides, 2022, 20(2): 297–313. [16]Lissak C, Bartsch A, De Michele M, et al. Remote sensing for assessing landslides and associated hazards. Surveys in Geophysics, 2020, 41(6): 1391-1435. [17]Wang D, Huang G, Zhang Q, et al. A multi-source landslide early warning model based on dynamic monitoring data and the SAAHP-FCE method. Geomatics, Natural Hazards and Risk, 2025, 16(1): 2513536. [18]Zhang W. Geological disaster monitoring and early warning system based on big data analysis. Arabian Journal of Geosciences, 2020, 13(18): 946. [19]Hui C, Zhigang T A O, Xiushan Q, et al. A new early-warning technology based on microseism and deep sliding force comprehensive monitoring for high-steep slope landslide disaster//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2020, 570(4): 042045. [20]Nie W, Tian C, Song D, et al. Disaster process andmultisource information monitoring and warning method for rainfall-triggered landslide: a case study in the southeastern coastal area of China. Natural Hazards, 2025, 121(3): 2535-2564.