Abstract
In subtropical typhoon-prone regions, landslides are triggered by short-duration intense rainfall and prolonged periods of elevated pore-water pressure. However, fast-moving landslides pose a significant challenge for timely warning because of insufficient data on rainfall triggers and the identification of potential failure sites. Thus, our study introduces an integrated approach that combines a double-index intensity-duration (I-D) threshold, accounting for daily rainfall (R0) and 5-d effective rainfall (R5), with the MC-TRIGRS, a probabilistic physically based model, to analyze fast-moving landslide hazards at a regional scale. This approach is characterized by its innovative features: (i) it employs a double-index model to categorize rainfall events, differentiating between long-term continuous rainfall and short-term intense precipitation; (ii) it utilizes a comprehensive dataset from extensive field investigations to implement the grey wolf optimizer (GWO) -enhanced long short-term memory neural network (LSTM) to predict soil thickness distributions across the study area; and (iii) it adopts the classical Monte Carlo method to calculate failure probabilities under various rainfall scenarios, incorporating randomness in key soil parameters, such as cohesion and internal friction angle. By leveraging geotechnical data from both field and laboratory tests and integrating the accumulated knowledge, these models can be applied to the coastal mountainous basins of Eastern China, a region highly prone to landslides. Our goal was to augment the effectiveness of landslide early warning systems. Particularly, the synergistic use of rainfall empirical statistics and probabilistic physically based slope stability models is poised to bolster real-time control and risk mitigation strategies, providing a robust solution for short-term preparedness.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Avila FF, Alvala RC, Mendes RM, Amore DJ (2021) The influence of land use/land cover variability and rainfall intensity in triggering landslides; a back-analysis study via physically based models. Nat Hazards (Dordrecht) 105(1):1139–1161. https://doi.org/10.1007/s11069-020-04324-x
Baum RL, Godt JW (2010) Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides 7(3):259–272. https://doi.org/10.1007/s10346-009-0177-0
Baum RL, Savage WZ, Godt JW (2002) TRIGRS — a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis. US Geological Survey Open-File Report 02-0424. http://pubs.usgs.gov/of/2002/ofr-02-424/. Accessed 7 Dec 2016
Baum RL, Savage WZ, Godt JW (2008) TRIGRS—a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, version 2.0. US Geological Survey Open-File Report 2008–1159. http://pubs.usgs.gov/of/2008/1159/. Accessed 7 Dec 2016
Baum RL, Savage WZ, Godt JW (2010) TRIGRS: a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, version 2.0. Colorado: US Department of the Interior and US Geological Survey
Baumann V, Bonadonna C, Cuomo S, Moscariello M, Manzella I (2018) Slope stability models for rainfall-induced lahars during long-lasting eruptions. J Volcanol Geother Res 359:78–94. https://doi.org/10.1016/j.jvolgeores.2018.06.018
Canli E, Mergili M, Thiebes B, Glade T (2018) Probabilistic landslide ensemble prediction systems; lessons to be learned from hydrology. Nat Hazard 18(8):2183–2202. https://doi.org/10.5194/nhess-18-2183-2018
Capparelli G, Tiranti D (2010) Application of the MoniFLaIR early warning system for rainfall-induced landslides in Piedmont region (Italy). Landslides 7(4):401–410. https://doi.org/10.1007/s10346-009-0189-9
Cascini L, Ciurleo M, Di Nocera S (2017) Soil depth reconstruction for the assessment of the susceptibility to shallow landslides in fine-grained slopes. Landslides 14(2):459–471. https://doi.org/10.1007/s10346-016-0720-8
Chen S, Mulder VL, Martin MP, Walter C, Lacoste M, Richer-de-Forges AC, Saby NPA, Loiseau T, Hu B, Arrouays D (2019) Probability mapping of soil thickness by random survival forest at a national scale. Geoderma 344:184–194. https://doi.org/10.1016/j.geoderma.2019.03.016
Ciurleo M, Mandaglio MC, Moraci N (2019) Landslide susceptibility assessment by TRIGRS in a frequently affected shallow instability area. Landslides 16(1):175–188. https://doi.org/10.1007/s10346-018-1072-3
Ciurleo M, Mandaglio MC, Moraci N (2021) A quantitative approach for debris flow inception and propagation analysis in the lead up to risk management. Landslides 18(6):2073–2093. https://doi.org/10.1007/s10346-021-01630-8
Dikshit A, Sarkar R, Pradhan B, Acharya S, Dorji K (2019) Estimating rainfall thresholds for landslide occurrence in the Bhutan Himalayas. Water 11(8):1616. https://doi.org/10.3390/w11081616
Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C, Han Z, Pham BT (2020) 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
Escobar-Wolf R, Sanders JD, Vishnu CL, Oommen T, Sajinkumar KS (2021) A GIS tool for infinite slope stability analysis (GIS-TISSA). Geosci Front 12(2):756–768. https://doi.org/10.1016/j.gsf.2020.09.008
Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102(3–4):85–98. https://doi.org/10.1016/j.enggeo.2008.03.022
Fu S, Chen L, Wolda T, Yin K, Gui L, Li D, Du J, Zhou C, Xu Y, Lian Z (2020) Landslide hazard probability and risk assessment at the community level: a case of western Hubei, China. Nat Hazards Earth Syst Sci. https://doi.org/10.5194/nhess-20-581-2020
Gioia E, Speranza G, Ferretti M, Godt JW, Baum RL, Marincioni F (2016) Application of a process-based shallow landslide hazard model over a broad area in central Italy. Landslides 13(5):1197–1214. https://doi.org/10.1007/s10346-015-0670-6
Guo Z, Ferrer JV, Hürlimann M, Medina V, Puig-Polo C, Yin K, Huang D (2023) Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China. Geosci Front 14(4):101542. https://doi.org/10.1016/j.gsf.2023.101542
Guzzetti F, Guzzetti F, Peruccacci S, Peruccacci S, Rossi M, Rossi M, Stark CP, Stark CP (2008) The rainfall intensity–duration control of shallow landslides and debris flows: an update. Landslides 5(1):3–17. https://doi.org/10.1007/s10346-007-0112-1
Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Comput 8(9):1735–1780
Huang F, Cao Z, Jiang S, Zhou C, Huang J, Guo Z (2020) Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model. Landslides 17(12):2919–2930. https://doi.org/10.1007/s10346-020-01473-9
Huang F, Tao S, Chang Z, Huang J, Fan X, Jiang S, Li W (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, Chen J, Liu W, Huang J, Hong H, Chen W (2022a) Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold. Geomorphology 408:108236. https://doi.org/10.1016/j.geomorph.2022.108236
Huang F, Yan J, Fan X, Yao C, Huang J, Chen W, Hong H (2022b) Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions. Geosci Front 13(2):101317. https://doi.org/10.1016/j.gsf.2021.101317
Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11(2):167–194. https://doi.org/10.1007/s10346-013-0436-y
Hwang I, Park H, Lee J (2023) Probabilistic analysis of rainfall-induced shallow landslide susceptibility using a physically based model and the bootstrap method. Landslides 20(4):829–844. https://doi.org/10.1007/s10346-022-02014-2
Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 7(36):1897–1910
Ji J, Cui H, Zhang T, Song J, Gao Y (2022) A GIS-based tool for probabilistic physical modelling and prediction of landslides: GIS-FORM landslide susceptibility analysis in seismic areas. Landslides 19(9):2213–2231. https://doi.org/10.1007/s10346-022-01885-9
Jin B, Yin K, Li Q, Gui L, Yang T, Zhao B, Guo B, Zeng T, Ma Z (2022) Susceptibility Analysis of Land Subsidence along the Transmission Line in the Salt Lake Area Based on Remote Sensing Interpretation. Remote Sensing 14:3229. https://doi.org/10.3390/rs14133229
Jin B, Zeng T, Yang T, Gui L, Yin K, Guo B, Zhao B, Lu Q (2023) The prediction of transmission towers’ foundation ground subsidence in the Salt Lake area based on multi-temporal interferometric synthetic aperture radar and deep learning. Remote Sens 15(16):4805. https://doi.org/10.3390/rs15194805
Keefer DK, Wilson RC, Mark RK, Brabb EE, Brown WM, Ellen SD, Harp EL, Wieczorek GF, Alger CS, Zatkin RS (1987) Real-time landslide warning during heavy rainfall. Science 238(4829):921–925
Kim D, Im S, Lee SH, Hong Y, Cha K (2010) Predicting the rainfall-triggered landslides in a forested mountain region using TRIGRS model. J Mt Sci 7(1):83–91. https://doi.org/10.1007/s11629-010-1072-9
Kim H, Lee J, Park H, Heo J (2021) Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis. Eng Geol 294:106372. https://doi.org/10.1016/j.enggeo.2021.106372
Kong VWW, Kwan JSH, Pun WK (2020) Hong Kong’s landslip warning system—40 years of progress. Landslides 17(6):1453–1463. https://doi.org/10.1007/s10346-020-01379-6
Li A, Tan X, Wu W, Liu H, Zhu J (2017) Predicting active-layer soil thickness using topographic variables at a small watershed scale. PLoS ONE 12(9):e0183742. https://doi.org/10.1371/journal.pone.0183742
Li X, Luo J, Jin X, He Q, Niu Y (2020) Improving Soil Thickness Estimations based on multiple environmental variables with stacking ensemble methods. Remote Sens 12(21):3609. https://doi.org/10.3390/rs12213609
Liu C, Wu C (2007) Mapping susceptibility of rainfall-triggered shallow landslides using a probabilistic approach. Environ Geol (Berlin) 55(4):907–915. https://doi.org/10.1007/s00254-007-1042-x
Malone B, Searle R (2020) Improvements to the Australian national soil thickness map using an integrated data mining approach. Geoderma 377:114579. https://doi.org/10.1016/j.geoderma.2020.114579
Marin RJ (2020) Physically based and distributed rainfall intensity and duration thresholds for shallow landslides. Landslides 17(12):2907–2917. https://doi.org/10.1007/s10346-020-01481-9
Martinovic K, Gavin K, Reale C, Mangan C (2018) Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network. Geomorphology (Amsterdam, Netherlands) 306:40–50. https://doi.org/10.1016/j.geomorph.2018.01.006
Medina V, Hürlimann M, Guo Z, Lloret A, Vaunat J (2021) Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale. CATENA 201:105213. https://doi.org/10.1016/j.catena.2021.105213
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Montrasio L, Valentino R, Losi GL (2011) Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale. Nat Hazards Earth Sys Sci 11(7):1927–1947. https://doi.org/10.5194/nhess-11-1927-201
Nie Y, Li X, Zhou W, Xu R (2021) Dynamic hazard assessment of group-occurring debris flows based on a coupled model. Nat Hazards (Dordrecht) 106(3):2635–2661. https://doi.org/10.1007/s11069-021-04558-3
Osanai N, Shimizu T, Kuramoto K, Kojima S, Noro T (2010) Japanese early-warning for debris flows and slope failures using rainfall indices with Radial Basis Function Network. Landslides 7(3):325–338. https://doi.org/10.1007/s10346-010-0229-5
Park DW, Nikhil NV, Lee SR (2013) Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event. Nat Hazard 13(11):2833–2849. https://doi.org/10.5194/nhess-13-2833-2013
Peng L, Peng L, Xu S, Xu S, Hou J, Hou J, Peng J, Peng J (2015) Quantitative risk analysis for landslides: the case of the Three Gorges area, China. Landslides 12(5):943–960. https://doi.org/10.1007/s10346-014-0518-5
Piciullo L, Calvello M, Cepeda JM (2018) Territorial early warning systems for rainfall-induced landslides. Earth Sci Rev 179:228–247. https://doi.org/10.1016/j.earscirev.2018.02.013
Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Roccati A, Paliaga G, Luino F, Faccini F, Turconi L (2020) Rainfall threshold for shallow landslides initiation and analysis of long-term rainfall trends in a Mediterranean area. Atmosphere 11(1367):1367. https://doi.org/10.3390/atmos11121367
Rosi A, Segoni S, Canavesi V, Monni A, Gallucci A, Casagli N (2021) Definition of 3D rainfall thresholds to increase operative landslide early warning system performances. Landslides 18(3):1045–1057. https://doi.org/10.1007/s10346-020-01523-2
Salciarini D, Fanelli G, Tamagnini C (2017) A probabilistic model for rainfall-induced shallow landslide prediction at the regional scale. Landslides 14(5):1731–1746. https://doi.org/10.1007/s10346-017-0812-0
Savage WZ, Godt JW, Baum RL, Rickenmann D, Chen C (2003) A model for spatially and temporally distributed shallow landslide initiation by rainfall infiltration. Publisher varies, pp 179–187
Scarpone C, Schmidt MG, Bulmer CE, Knudby A (2016) Modelling soil thickness in the critical zone for Southern British Columbia. Geoderma 282:59–69. https://doi.org/10.1016/j.geoderma.2016.07.012
Schilirò L, Cevasco A, Esposito C, Mugnozza GS (2018) Shallow landslide initiation on terraced slopes: inferences from a physically based approach. Geomat Nat Hazards Risk 9(1):295–324. https://doi.org/10.1080/19475705.2018.1430066
Seefelder CDLN, Koide S, Mergili M (2017) Does parameterization influence the performance of slope stability model results? A case study in Rio de Janeiro, Brazil. Landslides 14(4):1389–1401. https://doi.org/10.1007/s10346-016-0783-6
Segoni S, Piciullo L, Gariano SL (2018) A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides 15(8):1483–1501. https://doi.org/10.1007/s10346-018-0966-4
Srivastava R, Yeh T (1991) Analytical solutions for one-dimensional, transient infiltration toward the water table in homogeneous and layered soils. Water Resour Res 27:753–762
Thomas MA, Collins BD, Mirus BB (2019) Assessing the feasibility of satellite-based thresholds for hydrologically driven landsliding. Water Resour Res 55(11):9006–9023. https://doi.org/10.1029/2019WR025577
Thomas MA, Mirus BB, Collins BD (2018) Identifying physics-based thresholds for rainfallinduced landsliding. Geophys Res Lett 45(18):9651–9661
Tran TV, Alvioli M, Lee G, An HU (2017) Three-dimensional, time-dependent modeling of rainfall-induced landslides over a digital landscape: a case study. Landslides 15(6):1071–1084. https://doi.org/10.1007/s10346-017-0931-7
Vieira BC, Fernandes NF, Augusto Filho O, Martins TD, Montgomery DR (2018) Assessing shallow landslide hazards using the TRIGRS and SHALSTAB models, Serra do Mar, Brazil. Environ Earth Sci 77(6):1–15. https://doi.org/10.1007/s12665-018-7436-0
Weidner L, Oommen T, Escobar-Wolf R, Sajinkumar KS, Samuel RA (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
Xie J, Liu L, Yin K, Du H, Niu X (2003) Study on the threshold values of rainfall of landslide hazards for early-warning and prediction in Zhejiang province. Geol Sci Technol Inf 04:101–105
Yan L, Quanbing G, Fei W, Lixia C, Deying L, Yin K (2023) Integrated methodology for potential landslide identification in highly vegetation-covered areas. Remote Sens 15
Ye X, Zhu H, Cheng G, Pei H, Shi B, Schenato L, Pasuto A (2023) Thermo-hydro-poro-mechanical responses of a reservoir-induced landslide tracked by high-resolution fiber optic sensing nerves. J Rock Mech Geotech Eng. https://doi.org/10.1016/j.jrmge.2023.04.004
Ye X, Zhu HH, Wang J, Zhang Q, Shi B, Schenato L, Pasuto A (2022) Subsurface multi-physical monitoring of a reservoir landslide with the fiber-optic nerve system. Geophysical Res Lett. https://doi.org/10.1029/2022GL098211
Yin G, Luo J, Niu F, Lin Z, Liu M (2021) Machine learning-based thermokarst landslide susceptibility modeling across the permafrost region on the Qinghai-Tibet Plateau. Landslides 18(7):2639–2649. https://doi.org/10.1007/s10346-021-01669-7
Yin K, Wang Y, Tang Z (2002) Mechanism and dynamic simulation of landslide by precipitation. Geol Sci Technol Inf 21(01):75–78
Zeng T, Glade T, Xie Y, Kunlong Y, Peduto D (2023a) Deep learning powered long-term warning systems for reservoir landslides. Int J Disaster Risk Reduct. https://doi.org/10.1016/j.ijdrr.2023.103820
Zeng T, Guo Z, Wang L, Jin B, Wu F, Guo R (2023b) Tempo-spatial landslide susceptibility assessment from the perspective of human engineering activity. Remote Sensing 15(16):4111. https://doi.org/10.3390/rs15164111
Zeng T, Jiang H, Liu Q, Yin K (2022a) Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model. Stoch Env Res Risk Assess. https://doi.org/10.1007/s00477-021-02145-3
Zeng T, Jin B, Glade T, Xie Y, Li Y, Zhu Y, Yin K (2024) Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling: A critical inquiry. CATENA 236:107732. https://doi.org/10.1016/j.catena.2023.107732
Zeng T, Wu L, Peduto D, Glade T, Hayakawa YS, Yin K (2023c) Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy. Geosci Front 14(6):101645. https://doi.org/10.1016/j.gsf.2023.101645
Zeng T, Yin K, Gui L, Peduto D, Wu L, Guo Z, Li Y (2023d) Quantitative risk assessment of the Shilongmen reservoir landslide in the Three Gorges area of China. Bull Eng Geol Env 82:214
Zeng T, Yin K, Jiang H, Liu X, Guo Z, Peduto D (2022b) Groundwater level prediction based on a combined intelligence method for the Sifangbei landslide in the Three Gorges Reservoir Area. Sci Rep. https://doi.org/10.1038/s41598-022-14037-9
Zhang S, Liu G, Chen S, Rasmussen C, Liu B (2021) Assessing soil thickness in a black soil watershed in northeast China using random forest and field observations. Int Soil Water Conserv Res 9(1):49–57. https://doi.org/10.1016/j.iswcr.2020.09.004
Zhang Y, Xu X, Li Z, Yi R, Xu C, Luo W (2022) Modelling soil thickness using environmental attributes in karst watersheds. CATENA 212:106053. https://doi.org/10.1016/j.catena.2022.106053
Zhou C, Cao Y, Hu X, Yin K, Wang Y, Catani F (2022) Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area. Landslides. https://doi.org/10.1007/s10346-021-01796-1
Acknowledgements
We wish to thank the Quzhou Kecheng District Natural Resources and Planning Bureau for their assistance. The results of the research activity that Taorui Zeng carried out under the joint supervision of Prof. Yin and Prof. Peduto are reported in the present work.
Funding
This research was funded by the Comprehensive risk warning and control project of geological disasters in small watershed of Kecheng District (ZZCG2021058); National Natural Science Foundation of China (Grant No. 4187752); the National Natural Science Foundation of China (Grant No. 41601563) and National Key R&D Program of China (2023YFC3007201).
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Writing—original draft preparation: [Taorui Zeng]; Writing—review and editing: [Kunlong Yin], [Dario Peduto]; Methodology: [Quanbing Gong], [Liyang Wu]; Investigation: [Yuhang Zhu]. All authors have read and agreed to the published version of the manuscript.
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Zeng, T., Gong, Q., Wu, L. et al. Double-index rainfall warning and probabilistic physically based model for fast-moving landslide hazard analysis in subtropical-typhoon area. Landslides 21, 753–773 (2024). https://doi.org/10.1007/s10346-023-02187-4
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DOI: https://doi.org/10.1007/s10346-023-02187-4