Landslide susceptibility modeling based on GIS and novel bagging-based Kernel logistic regression

• Main Authors: Chen, W., Shahabi, H., Zhang, S., Khosravi, K., Shirzadi, A., Chapi, K., Pham, B. T., Zhang, T., Zhang, L., Chai, H., Ma, J., Chen, Y., Wang, X., Li, R., Ahmad, B. B.
• Format: Article
• Language: English
• Published: MDPI AG 2018
• Subjects: G70.212-70.215 Geographic information system
• Online Access: http://eprints.utm.my/id/eprint/79632/1/BaharinAhmad2018_LandslideSusceptibilityModelingbasedonGIS.pdf; http://eprints.utm.my/id/eprint/79632/; http://dx.doi.org/10.3390/app8122540

Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel...