Landslide susceptibility modelling in a part of Himachal Pradesh, India: An integrated method based on machine learning and geospatial techniques

Keywords: extremely randomised trees model, geo-informatics, landslide susceptibility, machine learning, rainfall variability, Western Himalaya


Landslides are one of the most destructive natural hazards in the mountainous regions across the globe including the western Himalayas of India. Hence, it is essential to implement mitigation plans, evacuation measures, and an infrastructure plan based on precise, efficient landslide susceptibility models. Current methods of landslide susceptibility mapping are improving constantly, using geospatial techniques to incorporate visual representation of the environment. However, these current methods are often opinion driven, due to lack of consensus on which factors take precedence over others. This study aims to provide a different approach namely a machine learning based approach towards Landslide Susceptibility Mapping, integrating GIS to give an accurate visual representation of the surrounding areas ranked by order of susceptibility in/and around Kullu Valley of western Himalaya, India. The landslide conditioning factors used in the study involve both static and dynamic data such as slope, land use, land cover, and rainfall variables. The research found that although the Extremely Randomised Trees provide a considerably more accurate assessment of the study area’s vulnerability, the Random Forest Regressor has greater overall accuracy. There is a significant relationship between the model’s outputs and past landslides. According to the study, there would be significantly more regions with high susceptibility to the effects of climate change on landslides by 2030. The application can identify the geographical distribution of landscape risk and is significantly less time-consuming than current methods of susceptibility analysis. Machine learning models could be crucial in evacuation efforts and in preventing damage to life and property.



Download data is not yet available.


Aleotti P, Chowdhury R (1999). Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment 58(1):21-44.

Breiman L (2001). Machine Learning 45(1):5-32.

Camilo DC, Lombardo L, Mai PM, Dou J, Huser R (2017). Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model. Environmental Modelling & Software 97:145-156.

Chae B-G, Park H-J, Catani F, Simoni A, Berti M (2017). Landslide prediction, monitoring and early warning: a concise review of state-of-the-art. Geosciences Journal 21(6):1033-1070.

Chang K-T, Merghadi A, Yunus AP, Pham BT, Dou J (2019). Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Scientific Reports 9(1):1-21.

Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, 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.

Du J, Glade T, Woldai T, Chai B, Zeng B (2020). Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas. Engineering Geology 270:105572.

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. Engineering Geology 102(3-4):85-98.

Formetta G, Rago V, Capparelli G, Rigon R, Muto F, Versace P (2014). Integrated physically based system for modeling landslide susceptibility. Procedia Earth and Planetary Science 9:74-82.

Geurts P, Ernst D, Wehenkel L (2006). Extremely randomized trees. Machine Learning 63(1):3-42.

Jung BC (2021). Disaster by choice. How our actions turn natural hazards into catastrophes Ilan Kelman Oxford, UK: Oxford University Press, 2020. ISBN: 9780198841340. World Medical & Health Policy 14(2): 445-446.

Kalsnes B, Nadim F (2012). SafeLand: Changing pattern of landslides risk and strategies for its management. In: Sassa K, Rouhban B, Briceño S, McSaveney M, He B (Eds). Landslides: Global Risk Preparedness. Springer, Berlin, Heidelberg pp 95-114.

Korup O, Stolle A (2014). Landslide prediction from machine learning. Geology Today 30(1):26-33.

Kuniyal JC, Jamwal A, Kanwar N, Chand B, Kumar K, Dhyani PP (2019). Vulnerability assessment of the Satluj catchment for sustainable development of hydroelectric projects in the northwestern Himalaya. Journal of Mountain Science 16(12):2714-2738.

Lee S, Hong S-M, Jung H-S (2017). A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability 9(1):48.

Liu C, Fan X, Zhu C, Shi B (2019). Discrete element modeling and simulation of 3-Dimensional large-scale landslide-Taking Xinmocun landslide as an example. Journal of Engineering Geology 27(6):1362-1370.

Lu P, Rosenbaum MS (2003). Artificial neural networks and grey systems for the prediction of slope stability. Natural Hazards 30(3):383-398.

Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews 207:103225.

Nakileza BR, Nedala S (2020). Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda. Geoenvironmental Disasters 7(1):27.

NLRMS (2019). National Landslide Risk Management Strategy (2019). National Landslide Risk Management Strategy. A publication of the National Disaster Management Authority, Government of India, New Delhi.

Pham BT, Pradhan B, Tien Bui D, Prakash I, Dholakia MB (2016). A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environmental Modelling & Software 84:240-250.

Sufri S, Dwirahmadi F, Phung D, Rutherford S (2020). Enhancing community engagement in disaster early warning system in Aceh, Indonesia: opportunities and challenges. Natural Hazards 103(3):2691-2709.

Sun D, Gu Q, Wen H, Shi S, Mi C, Zhang F (2022). A hybrid landslide warning model coupling susceptibility zoning and precipitation. Forests 13(6):827.

Tien Bui D, Shirzadi A, Shahabi H, Geertsema M, Omidvar E, Clague J, Thai Pham B, Dou J, Talebpour Asl D, Bin Ahmad B, Lee S (2019). New ensemble models for shallow landslide susceptibility modeling in a semi-arid watershed. Forests 10(9):743.

Turner AK (2018). Social and environmental impacts of landslides. Innovative Infrastructure Solutions 3:70.

Uniyal A (2017). National Landslide Risk Management Strategy.

Van Westen CJ, Seijmonsbergen AC, Mantovani F. (1999). Comparing landslide hazard maps. Natural Hazards 20:137-158.

Watson DF, Philip GM (1985). A refinement of inverse distance weighted interpolation. Geoprocessing 2(4):315-327.

Wickramasinghe D (2021). Ecosystem-based disaster risk reduction. Oxford Research Encyclopedia of Natural Hazard Science.

Wilkinson PL, Anderson MG, Lloyd DM (2002). An integrated hydrological model for rain-induced landslide prediction. Earth Surface Processes and Landforms 27(12):1285-1297.

Xie M, Esaki T, Cai M (2004). A time-space based approach for mapping rainfall-induced shallow landslide hazard. Environmental Geology 46(6-7):840-850.

Yunus AP, Dou J, Song X, Avtar R (2019). Improved bathymetric mapping of coastal and lake environments using Sentinel-2 and Landsat-8 Images. Sensors 19(12):2788.

How to Cite
MOHAPATRA, R. (2023). Landslide susceptibility modelling in a part of Himachal Pradesh, India: An integrated method based on machine learning and geospatial techniques. Nova Geodesia, 3(1), 63.
Research articles
DOI: 10.55779/ng3163