RESEARCH ARTICLE


Application of GIS-Based Back Propagation Artificial Neural Networks and Logistic Regression for shallow Landslide Susceptibility Mapping in South China-Take Meijiang River Basin as an Example



Qing-hua Gong1, 2, *, Jun-xiang Zhang3, Jun Wang1, 2
1 Guangzhou Institute of Geography, Guangzhou 510070, China
2 Guangdong Open Laboratory of Geo-spatial Information Technology and Application, Guangzhou 510070, China
3 School of Tourism, Huangshan University, Huangshan 245021, China


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© 2018 Gong et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Guangzhou Institute of Geography 100#,XianLie Road, Yuexiu District, Guangdong Province, Guangzhou City, China, Fax: Tel: +86-13560347467, E-mail: gqh100608@163.com


Abstract

Introduction:

In this study, artificial neural network (ANN) model and logistic regression were applied to analyze susceptibility and identify the main controlling factors of landslide in Meijiang River Basin of Southern China.

Methods:

Methods: Eleven variables such as altitude, slope angle, slope aspect, topographic relief, distance to fault, rock-type, soil-type, land-use type, NDVI, maximum rainfall intensity, distance to river were employed as landslide conditioning factors in landslide susceptibility mapping.

Both landsliding and non-landsliding samples were needed as training data for ANN model. 384 landslides and 380 non-landsliding point with no recorded landslides according to field investigation and survey data were chosen as sample data of ANN model. And ROC curve was applied to calculate the prediction accuracy.

Results:

The validation results showed that prediction accuracy rate of 82.6% exists between the susceptibility map and the location of the initial 384 landsliding samples. However, logistic regression analysis showed that the average correct classification percentage was 75.4%. The prediction results of ANN model in high sensitive zone is more accurate than the logistic regression model.

Conclusion:

Therefore, the ANN model is valid when assessing the susceptibility. The main controlling factors were identified from the eleven factors by ANN model. The slope, rock and land use type appeared to be the main controlling factors in landslide formation process in Southern China.

Keywords: Shallow landslide, Susceptibility, Artificial neural network, Logistic regression, GIS, South China.