Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China

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.

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Landslide susceptibility mapping in the region of eastern Himalayan syntaxis, Tibetan Plateau, China: a comparison between analytical hierarchy process information value and logistic regression-information value methods

Bulletin of Engineering Geology and the Environment ◽

10.1007/s10064-018-1393-4 ◽

2018 ◽

Vol 78 (6) ◽

pp. 4201-4215 ◽

Cited By ~ 5

Author(s):

Guoliang Du ◽

Yongshuang Zhang ◽

Zhihua Yang ◽

Changbao Guo ◽

Xin Yao ◽

...

Keyword(s):

Logistic Regression ◽

Tibetan Plateau ◽

Analytical Hierarchy Process ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Information Value ◽

Landslide Susceptibility Mapping ◽

Process Information ◽

Hierarchy Process ◽

Eastern Himalayan Syntaxis

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Application of an Integrated Model Based on Bivariate and Multivariate Method in Landslide Susceptibility Mapping

10.21203/rs.3.rs-107923/v1 ◽

2020 ◽

Author(s):

Han Hu ◽

Changming Wang ◽

Zhu Liang

Keyword(s):

Land Use Planning ◽

Landslide Susceptibility ◽

Area Under The Curve ◽

Susceptibility Mapping ◽

Information Value ◽

Landslide Susceptibility Mapping ◽

Information Value Method ◽

Model Combining ◽

Major Factors ◽

Improved Model

Abstract Landslides usually result in human losses and economic damages in mountainous areas especially for Himalayan areas. Landslide susceptibility mapping (LSM) is a key approach for avoiding hazard and risk. This study aims to explore an improved model combining multivariate and bivariate statistical methods for LSM. Four models were established as logistic regression (LR), LR integrated with certain factor (CF), LR integrated with frequency ratio (FR) and LR integrated with information value method (IV) and their performance was compared in LSM. Firstly, a landslide inventory map with 313 determined landslide events was prepared and 12 predisposing factors were selected. Secondly, the dataset was randomly divided into two parts, 75% of which was used for modeling and 25% for validation. Finally, area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. Results show that the performance of IVLR model is the best (AUC 0.792 and accuracy=78.8%). Besides, the LSM constructed by IVLR model did a reasonable job at predicting the distribution of susceptible areas. It identified the major factors and intervals of high susceptibility that profile curvature greater than 0.1, less than 2 km from the stream, maximum elevation difference greater than 1200 m and rainfall between 440 and 450 mm were prone to landslide. The conclusion reveals that the quality of LSM can be improved by comparing and combining the bivariate and multivariate methods, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.

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