Landslide susceptibility assessment in Shenzhen based on multi-scale convolutional neural networks model
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摘要:
卷积神经网络（convolutional neural networks，CNN）模型因其强大的特征提取能力被广泛应用于滑坡易发性评估，但传统CNN已难以满足要求。文章提出一种能够顾及深层与浅层特征的多尺度卷积神经网络（multi-scale convolutional neural networks，MSCNN）模型，通过增加模型深度和样本的感受野，挖掘更深层和更稳定的特征，提高复杂场景下的滑坡易发性评估可靠性。文章以深圳市为研究区，根据系统性原则和代表性原则选取了12个深圳市滑坡影响因子，构建多尺度卷积神经网络滑坡易发性评估模型，并与多层感知器（multilayer perceptron，MLP）、支持向量机（support vector machine，SVM）以及随机森林（random forest，RF）等方法进行对比。结果表明，文章构建的MSCNN模型的AUC值（0.99）较高，优于MLP（0.97）、SVM（0.91）和RF（0.85），证明提出的MSCNN模型具有优异的预测能力；深圳市极高易发性区域面积约为105.3 km2，占研究区总面积的4.98%，主要分布在坡体较陡、植被覆盖稀疏和人类工程活动频繁的龙岗区，坡度、地表粗糙度和地表起伏度成为影响深圳市滑坡的主控因子。文章实现的滑坡易发性图反映了深圳市滑坡灾害的分布现状，可为深圳市未来滑坡灾害防治提供数据支持和关键技术支撑。
Abstract:Convolutional neural network (CNN) models are widely used in landslide susceptibility assessment due to their powerful feature extraction capabilities, and traditional CNN is no longer able to meet the requirements. Therefore, this paper proposes a multi-scale convolutional neural networks (MSCNN) model that can take into account deep and shallow features. By increasing the depth of the model and expanding the receptive field of samples, the MSCNN can tap deeper and more stable features to improve the reliability of landslide susceptibility assessment in complex scenarios. In this study, Shenzhen City is selected as the research area, and 12 landslide conditioning factors of landslides in Shenzhen City were selected based on systematic and representative principles. A multi-scale convolutional neural network landslide susceptibility assessment model is constructed and compared with methods such as multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF). The results show that the AUC value (0.99) of the MSCNN model constructed in this paper is higher than that of MLP (0.97), SVM (0.91), and RF (0.85), which proves that the proposed MSCNN model has excellent prediction ability. The area of extremely high susceptibility in Shenzhen City is approximately 105.3 km², accounting for 4.98% of the total area of the study area, mainly distributed in Longgang District with steep slopes, sparse vegetation cover, and frequent human engineering activities. Slope, surface roughness, and surface relief are identified as the main conditioning factors affecting landslides in Shenzhen City. The landslide susceptibility mapping implemented in this paper reflects the current distribution of landslide disasters in Shenzhen City, providing data support and key technical support for future landslide disaster prevention and control in Shenzhen City.
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Key words:
- MSCNN /
- landslide susceptibility assessment /
- machine learning model /
- Shenzhen
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表 1 滑坡影响因子数据来源
Table 1. Data sources for landslide conditioning factors
数据源 分辨率 滑坡影响因子 数据来源 数字高程模型 30 m 高程 https://www.gscloud.cn/ 坡度 坡向 曲率 地表粗糙度 地表起伏度 断层 1∶ 2500000 到断层距离 https://www.cgs.gov.cn/ 道路和河流 1∶ 1000000 到河流距离 OpenStreetMap 到道路距离 土地利用类型 30 m 土地利用类型 http://data.ess.tsinghua/ 土壤类型 1000 m土壤类型 https://www.fao.org/ 沙含量 1000 m沙含量 http://www.geodata.cn/ 
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表 2 影响因子共线性评价表
Table 2. Evaluation of factor collinearity among conditioning factors
序号 影响因子 VIF 容差 1 高程 1.514 0.660 2 坡度 6.666 0.150 3 坡向 1.007 0.993 4 曲率 1.003 0.997 5 地表粗糙度 5.012 0.200 6 地表起伏度 2.148 0.466 7 到断层距离 1.082 0.924 8 土壤类型 1.071 0.993 9 沙含量 1.029 0.972 10 到河流距离 1.110 0.901 11 土地利用类型 1.285 0.778 12 到道路距离 1.070 0.935
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表 3 影响因子地理探测器结果
Table 3. Results of geodetector analysis for conditioning factors
序号 影响因子 q值 1 高程 0.322 2 坡度 0.185 3 坡向 0.058 4 曲率 0.073 5 地表粗糙度 0.118 6 地表起伏度 0.193 7 到断层距离 0.107 8 土壤类型 0.144 9 沙含量 0.179 10 到河流距离 0.172 11 土地利用类型 0.128 12 到道路距离 0.093
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