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Open AccessArticle

An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method

Division of Earth Environmental System Science, Pukyong National University, 45 Yongso–ro, Nam-gu, Busan 48513, Korea
Department of Geography, University of Bergen, Fosswinckelsgt. 6, 5020 Bergen, Norway
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(11), 365;
Received: 5 July 2017 / Revised: 4 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
PDF [23225 KB, uploaded 16 November 2017]


The Mw 7.8 Gorkha earthquake of 25 April 2015 triggered thousands of landslides in the central part of the Nepal Himalayas. The main goal of this study was to generate an ensemble-based map of co-seismic landslide susceptibility in Sindhupalchowk District using model comparison and combination strands. A total of 2194 co-seismic landslides were identified and were randomly split into 1536 (~70%), to train data for establishing the model, and the remaining 658 (~30%) for the validation of the model. Frequency ratio, evidential belief function, and weight of evidence methods were applied and compared using 11 different causative factors (peak ground acceleration, epicenter proximity, fault proximity, geology, elevation, slope, plan curvature, internal relief, drainage proximity, stream power index, and topographic wetness index) to prepare the landslide susceptibility map. An ensemble of random forest was then used to overcome the various prediction limitations of the individual models. The success rates and prediction capabilities were critically compared using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). By synthesizing the results of the various models into a single score, the ensemble model improved accuracy and provided considerably more realistic prediction capacities (91%) than the frequency ratio (81.2%), evidential belief function (83.5%) methods, and weight of evidence (80.1%). View Full-Text
Keywords: co-seismic landslide susceptibility; ensemble model; GIS; random forest method co-seismic landslide susceptibility; ensemble model; GIS; random forest method

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Shrestha, S.; Kang, T.-S.; Suwal, M.K. An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method. ISPRS Int. J. Geo-Inf. 2017, 6, 365.

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