Journal
GEOMORPHOLOGY
Volume 303, Issue -, Pages 284-298Publisher
ELSEVIER
DOI: 10.1016/j.geomorph.2017.12.007
Keywords
Data-scarce environment; Landslide susceptibility; Random forest; Support vector machine; GIS
Funding
- National Research Foundation of Korea (NRF) grant - Korea government (MSIP) [NRF-2016R1A2B4008963]
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This study evaluated the generalizability of five models to select a suitable approach for landslide susceptibility modeling in data-scarce environments. In total, 418 landslide inventories and 18 landslide conditioning factors were analyzed. Multicollinearity and factor optimization were investigated before data modeling, and two experiments were then conducted. In each experiment, five susceptibility maps were produced based on support vector machine (SVM), random forest (RF), weight-of-evidence (WoE), ridge regression (Rid_R), and robust regression (RR) models. The highest accuracy (AUC = 0.85) was achieved with the SVM model when either the full or limited landslide inventories were used. Furthermore, the RF and WoE models were severely affected when less landslide samples were used for training. The other models were affected slightly when the training samples were limited. (C) 2017 Elsevier B.V. All rights reserved.
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