基于层数自适应加权卷积神经网络的川藏交通廊道沿线滑坡易发性评价

黄武彪; 丁明涛; 王栋; 蒋良文; 李振洪

doi:10.3799/dqkx.2021.243

Volume 47 Issue 6

Jun. 2022

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Article Navigation > Earth Science > 2022 > 47(6): 2015-2030

Huang Wubiao, Ding Mingtao, Wang Dong, Jiang Liangwen, Li Zhenhong, 2022. Evaluation of Landslide Susceptibility Based on Layer Adaptive Weighted Convolutional Neural Network Model along Sichuan-Tibet Traffic Corridor. Earth Science, 47(6): 2015-2030. doi: 10.3799/dqkx.2021.243

Citation:

Huang Wubiao, Ding Mingtao, Wang Dong, Jiang Liangwen, Li Zhenhong, 2022. Evaluation of Landslide Susceptibility Based on Layer Adaptive Weighted Convolutional Neural Network Model along Sichuan-Tibet Traffic Corridor. Earth Science, 47(6): 2015-2030. doi: 10.3799/dqkx.2021.243

Citation:

Huang Wubiao, Ding Mingtao, Wang Dong, Jiang Liangwen, Li Zhenhong, 2022. Evaluation of Landslide Susceptibility Based on Layer Adaptive Weighted Convolutional Neural Network Model along Sichuan-Tibet Traffic Corridor. Earth Science, 47(6): 2015-2030. doi: 10.3799/dqkx.2021.243

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Evaluation of Landslide Susceptibility Based on Layer Adaptive Weighted Convolutional Neural Network Model along Sichuan-Tibet Traffic Corridor

doi: 10.3799/dqkx.2021.243

Huang Wubiao^{1, 2
,
,},
Ding Mingtao^{1, 2, 3
,
,
,},
Wang Dong⁴,
Jiang Liangwen⁴,
Li Zhenhong^{1, 2, 3}

1.
College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
2.
Big Data Center for Geosciences and Satellites, Chang'an University, Xi'an 710054, China
3.
Key Laboratory of Western China's Mineral Resource and Geological Engineering, Ministry of Education, Xi'an 710054, China
4.
China Railway Eryuan Engineering Group Co. Ltd., Chengdu 610031, China

Received Date: 2021-09-30
Publish Date: 2022-06-25

Abstract

Abstract

It is of great significance for disaster risk management in the process of railway engineering construction, operation and maintenance to carry out precise landslide susceptibility assessment along the Sichuan-Tibet traffic corridor. In this paper, a layer adaptive weighted convolutional neural network (LAW-CNN) is proposed to evaluate the landslide susceptibility along the Sichuan-Tibet traffic corridor. According to the field investigation and influencing factor analysis, the influencing factors are selected, the landslide catalogue and the spatial database is constructed.To optimize the initial weight and the layer number of the CNN network, the channel weighted method and the network layer optimization strategy based on the influence factor information entropy are proposed, and the LAW-CNN architecture is constructed by multi-channel weighted convolution and multi-layer classification convolution. The optimal LAW-CNN structure is searched and the network parameters are trained to obtain the landslide occurrence probability in the study area, followed by a susceptibility classification evaluation.The proposed LAW-CNN model can fully represent the deep characteristics of the factor layers with different weights and depths.The experimental results show that the area under curve value of the proposed model is 0.852 8 and the landslide density in the very high susceptibility area is 1.251 9, which are better than the SVM and CNN models.The very high and high susceptibility areas are mainly concentrated on both sides of large rivers and the Hengduan Mountain Range.The LAW-CNN model can precisely assess landslide susceptibility, and then provide a scientific basis for the construction of the Sichuan-Tibet traffic corridor and disaster prevention.
- landslide susceptibility mapping,
- Sichuan-Tibet traffic corridor,
- layer adaptive,
- multi-channel weighted,
- convolutional neural network,
- landslide

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References(66)

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