Universiti Teknologi Malaysia Institutional Repository

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

Chen, W. and Shahabi, H. and Zhang, S. and Khosravi, K. and Shirzadi, A. and Chapi, K. and Pham, B. T. and Zhang, T. and Zhang, L. and Chai, H. and Ma, J. and Chen, Y. and Wang, X. and Li, R. and Ahmad, B. B. (2018) Landslide susceptibility modeling based on GIS and novel bagging-based Kernel logistic regression. Applied Sciences (Switzerland), 8 (12). ISSN 2076-3417

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Official URL: http://dx.doi.org/10.3390/app8122540

Abstract

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 hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the overfitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas.

Item Type:Article
Uncontrolled Keywords:China, Landslide, Meta classifier, Prediction power
Subjects:G Geography. Anthropology. Recreation > G Geography (General) > G70.212-70.215 Geographic information system
Divisions:Geoinformation and Real Estate
ID Code:79632
Deposited By: Fazli Masari
Deposited On:28 Jan 2019 12:58
Last Modified:28 Jan 2019 12:58

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