Landslide Hazard Assessment in Northeast Afghanistan Plateau Based on Optimized Neural Network
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摘要: 阿富汗东北部是典型的高原寒旱地区，滑坡灾害发育，除受地形地貌、地质构造、人类活动等因素影响外，还由积雪覆盖、冰雪消融等方面控制；为研究高原寒旱地区滑坡危险性，在遥感解译基础数据上，考虑高原寒旱地区积雪覆盖和冰川活动对滑坡发育的影响，引入积雪覆盖度和消融水当量两个评价指标，基于证据权‒全连接神经网络模型建立滑坡易发性评价模型，以度日模型、SCS-CN模型建立滑坡危险性评价体系，并根据混淆矩阵对评价模型进行检验；危险性评价结果表明极高危险性区域占全区10.46%，分布灾害面积占比82.71%，主要分布在努尔斯坦省东部库纳尔‒奇特拉尔河段、巴达赫尚省除瓦罕走廊段的中东部高山区和帕尔万省赫尔曼德河段；高危险性区域占全区14.83%，分布灾害面积占比12.11%，主要分布在巴达赫尚省东部区域、努尔斯坦省和帕尔万省西部.检验结果及统计结果均表明结合证据权法取负样本对神经网络精度提升显著；研究成果为阿富汗滑坡灾害早期预警与工程防治提供科学依据.Abstract: The northeastern part of Afghanistan is a typical cold and arid region where landslide geological hazards are developed. The landslide development is not only affected by topography, geological structure, human activities, and other factors, but also is controlled by snow cover, snow, and ice melt. In this paper, based on the primary data of remote sensing interpretation, considering the influence of snow cover and glacier activity on landslide development, two evaluation indexes of snow cover and ablation water equivalent were introduced to study the landslide risk in the cold and dry areas of the plateau. The landslide susceptibility evaluation system was established based on the weight of evidence and a fully connected neural network model. Degree-day model and SCS-CN model established the landslide risk evaluation system, and the evaluation model was tested according to the confusion matrix. The hazard assessment results show that the extremely high-risk area accounts for 10.46% of the total area, and the disaster area accounts for 82.71%, mainly distributed in the Kunar-Chitral reach in the east of Nuristan Province, the middle and eastern high mountains of Badakhshan Province except for Wakhan corridor section, and the Helmand Reach in Parwan Province. The high-risk area accounts for 14.83% of the total area, and the disaster area accounts for 12.11%, mainly distributed in the eastern region of Badakhshan Province, the western region of Nuristan Province, and Parwan Province. The test results and statistical results all show that the accuracy of the neural network is significantly improved by taking negative samples with a weight of evidence method. The research results can provide the scientific basis for Afghanistan's early warning and prevention of landslide geological disasters.
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图 1 阿富汗东北部高原区地理位置
Fig. 1. Geographical location of the plateau region in northeast Afghanistan
图 2 研究区滑坡及道路河流遥感解译图
Fig. 2. Remote sensing interpretation map of landslide, road and river
图 6 不同比例训练集、验证集数据成果
Fig. 6. Data results of training set and validation set in different proportions
图 7 不同比例训练集验证集评价结果
Fig. 7. Different proportions of training sets verify the evaluation results of training sets
表 1 研究区基础数据来源
Table 1. Basic data sources
分类 数据源 分辨率（m） 底图 Google Earth Engine / DEM ASTER GDEM V3 30 遥感影像 Planet 3 水系 道路 地质构造 文献资料 / 地震 地层岩性 1:500万亚洲构造图 / 降雨 美国国家航空航天局 600 温度 积雪覆盖度 Science Data Bank 600 植被覆盖度（NDVI） Landsat8 30 
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表 2 不同土地利用下的CN值
Table 2. CN values under different land uses
土壤湿度 干燥 适中 湿润 裸地 69 70 84 灌木 72 81 86 草地 51 63 70 森林 60 73 79 冰川 80 88 95 耕地 71 78 81 人造地表 74 82 86 湿地 40 60 78 水体 100 100 100
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表 3 研究区滑坡易发性评价指标相关性系数
Table 3. Correlation coefficients of landslide susceptibility evaluation index
因子 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X1 1 X2 0.112 1 X3 0.148 0.038 1 X4 0.014 ‒0.08 ‒0.38 1 X5 ‒0.66 ‒0.25 ‒0.34 ‒0.03 1 X6 ‒0.34 ‒0.33 0.104 0.256 ‒0.52 1 X7 ‒0.17 ‒0.21 0.276 0.625 ‒0.06 0.323 1 X8 0.124 0.107 0.128 0.031 0.013 ‒0.01 ‒0.12 1 X9 ‒0.12 0.05 ‒0.29 ‒0.08 ‒0.04 ‒0.1 0.076 ‒0.15 1 X10 0.213 0.193 0.346 ‒0.01 0.41 ‒0.01 0.036 0.021 0.009 1 X11 0.186 0.139 0.141 ‒0.22 ‒0.03 ‒0.05 ‒0.05 ‒0.15 ‒0.08 0.236 1 X12 ‒0.21 ‒0.25 ‒0.02 ‒0.11 0.049 ‒0.33 ‒0.03 0.024 ‒0.01 ‒0.26 0.084 1 X13 ‒0.15 0.019 ‒0.01 ‒0.01 ‒0.09 0.07 0.306 0.021 ‒0.03 ‒0.13 ‒0.02 0.219 1 X14 ‒0.02 0.001 0.001 ‒0.14 0.001 0.002 0.541 0.005 0.001 0.003 0.008 ‒0.01 0.002 1
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表 4 研究区滑坡易发性评价统计
Table 4. Susceptibility statistics of landslide
分级 分级面积(km²) 分级占比(%) 灾害面积(km²) 灾害占比(%) 灾害密度 非 52 439 54.46 1.41 0.49 0.027×10‒3 低 19 744 20.51 8.58 2.97 0.434×10‒3 中 13 252 13.76 30.74 10.63 2.319×10‒3 高 10 846 11.27 248.2 85.91 22.89×10‒3
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表 5 研究区危险性权重判别矩阵
Table 5. Discriminant matrix of hazard weight
因子名称 易发性 消融水当量 地表径流 权重 易发性 1 4 5 0.665 消融水当量 1/4 1 4 0.245 地表径流 1/5 1/4 1 0.090
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