Role of landslide sampling strategies in susceptibility modelling: types, comparison and mechanism

• Published in: Bulletin of engineering geology and the environment 2024-09, Vol.83 (9), p.357, Article 357
• Main Authors: Thanveer, Jiyadh, Singh, Ajay, Shirke, Amit V., Umrikar, Bhavana, Yunus, Ali P.
• Format: Article
• Language: English
• Subjects: Accumulation; Accuracy; Centroids; Climate prediction; Earth and Environmental Science; Earth Sciences; Earthquakes; Emergency preparedness; Escarpments; Extreme weather; Foundations; Geoecology/Natural Processes; Geoengineering; Geotechnical Engineering & Applied Earth Sciences; Hydraulics; Landslides; Landslides & mudslides; Machine learning; Modelling; Nature Conservation; Original Paper; Rainfall; Sampling; Satellite data; Wind
• ISSN: 1435-9529; 1435-9537
• DOI: 10.1007/s10064-024-03851-2

This work explores the role of landslide sampling strategies in landslide susceptibility modelling (LSM) viz. (a) samples from the landslide scarp, (b) centroid of landslide body, and (c) samples from the debris accumulation zone, and discuss the mechanism and predictive capacity of each type in the LSM output. The evaluation took place near the surroundings of Koyna reservoir region, a highly vulnerable zone in Western Ghats, India, that had not undergone a comparable assessment previously. For this, an inventory dataset, featuring over 3000 landslide polygons were mapped following the July 2021 extreme rainfall event, including details on source-accumulation zone separation using high-resolution satellite data. Fourteen landslide conditioning factors (LCF) (topographic, hydrologic, and climate) are then identified as predictors and utilized to train and test with four widely-used machine learning (ML) models. Our findings reveal substantial differences in the areal percentage of landslides within identical classes of LCF when employing distinct sampling strategies, implying potential differences in predictive accuracies. Results show that LSM prepared from scarp zones demonstrated higher predictive power (AUC = 0.95), and random forest outperforms all other ML models. The outcomes of our study aid landslide investigators in evaluating the suitability of landslide data types and models, as they can significantly impact the accuracy of the resulting LSMs.