Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping

Rui-Xuan Tang, E-Chuan Yan, Tao Wen, Xiao-Meng Yin and Wei Tang
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Rui-Xuan Tang: School of Geosciences, Yangtze University, Wuhan 430100, China
E-Chuan Yan: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Tao Wen: School of Geosciences, Yangtze University, Wuhan 430100, China
Xiao-Meng Yin: College of Architecture and Civil Engineering, Xinyang Normal University, Xinyang 464000, China
Wei Tang: School of Urban and Rural Planning and Architectural Engineering, Guiyang University, Guiyang 550005, China

Sustainability, 2021, vol. 13, issue 7, 1-25

Abstract: This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.

Keywords: landslide susceptibility; logistic regression; frequency ratio; information value; artificial neural networks; analytic hierarchy process (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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