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优化神经网络下阿富汗东北高原寒旱区滑坡危险性评价
余波, 常鸣, 倪章, 孙文静, 徐恒志
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PDF(5898 KB)
优化神经网络下阿富汗东北高原寒旱区滑坡危险性评价
Landslide Hazard Assessment in Northeast Afghanistan Plateau Based on Optimized Neural Network
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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.
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.
阿富汗 / 滑坡 / 高原寒旱区 / 证据权‒全连接神经网络 / 危险性评价 / 灾害地质
Afghanistan / landslide / plateau cold and arid region / weight of evidence-fully connected neural network / hazard assessment / hazard geology
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