Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

Remote Sensing

卷:14

Rui Liu¹  Xin Yang¹  Chong Xu²  Liangshuai Wei³  Xiangqiang Zeng⁴ 

[1] College of Geophysics, Chengdu University of Technology, Chengdu 610059, China;

[2] National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China;

[3] The Institute of Exploration Technology of Chinese Academy of Geological Science, Chengdu 611734, China;

[4] University of Chinese Academy of Sciences, Beijing 100049, China;

关键词: landslide susceptibility mapping;    convolutional neural network;    machine learning;    GIS;    Jiuzhaigou region;    Lantau Island;   

DOI  :  10.3390/rs14020321

来源: DOAJ

【 摘 要 】

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM.

【 授权许可】

Unknown