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Assessment of a Dynamic Physically Based Slope Stability Model to Evaluate Timing and Distribution of Rainfall-Induced Shallow Landslides

Bibliographic Data

ID22033717
AuthorsJuby Thomas (0009-0008-4716-7736, University of Delhi), Manika Gupta (University of Delhi, corresponding author), Prashant K Srivastava (0000-0002-4155-630X, Banaras Hindu University), George P Petropoulos (0000-0003-1442-1423, Harokopio University of Athens)
Year2023
Volume12
Issue3
Pages105
Publication date2023-03-02
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi12030105
OpenAlexW4323046622
LanguageEN
References cited94

Shallow landslides due to hydro-meteorological factors are one of the most common destructive geological processes, which have become more frequent in recent years due to changes in rainfall frequency and intensity. The present study assessed a dynamic, physically based slope stability model, Transient Rainfall Infiltration and Grid-Based Slope Stability Model (TRIGRS), in Idukki district, Kerala, Western Ghats. The study compared the impact of hydrogeomechanical parameters derived from two different data sets, FAO soil texture and regionally available soil texture, on the simulation of the distribution and timing of shallow landslides. For assessing the landslide distribution, 1913 landslides were compared and true positive rates (TPRs) of 68% and 60% were obtained with a nine-day rainfall period for the FAO- and regional-based data sets, respectively. However, a false positive rate (FPR) of 36% and 31% was also seen, respectively. The timing of occurrence of nine landslide events was assessed, which were triggered in the second week of June 2018. Even though the distribution of eight landslides was accurately simulated, the timing of only three events was found to be accurate. The study concludes that the model simulations using parameters derived from either of the soil texture data sets are able to identify the location of the event. However, there is a need for including a high-spatial-resolution hydrogeomechanical parameter data set to improve the timing of landslide event modeling

Geography · Geotechnical engineering · Landslide · Meteorology · Physical geography · Remote sensing · Soil texture · Soil water · Spatial distribution · Environmental Science · Landslides and related hazards · Soil and Unsaturated Flow · Tree Root and Stability Studies · Geology · Soil Science

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