A case study on the susceptibility assessment of debris flows disasters based on prototype network in Nujiang Prefecture, Yunnan Province
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摘要:
针对基于泥石流因子评价方法中选取因子不一及训练样本少的问题，提出了一种基于原型网络的沟谷泥石流灾害易发性评价方法。首先，通过元学习方式组织训练数据，计算每一类沟谷的原型中心。其次，计算未知样本与每一类原型中心的距离，得到其从属类别的概率。最后，根据类别概率计算沟谷的泥石流易发性指数，得到泥石流易发性评价等级。运用模型对怒江州的沟谷进行评价，并与历史灾害数据进行比对，分类正确率达到67.39%，历史事件中泥石流灾害严重程度与模型的评价等级吻合度较好。相比传统实地勘测和因子评价等方法，文章方法能够通过遥感影像进行泥石流灾害区域的快速识别与评价，为泥石流灾害的预警预测研究带来新的思路。
Abstract:In response to the issues of inconsistent factor selection and limited training samples in debris flow factor-based evaluation methods, this study proposed a prototypical network-based approach for assessing the susceptibility of valley debris flow disasters. The method involves organizing the training data through meta-learning and calculating the prototype center for each valley type, serving as a representative of that category. Subsequently, the distance between the features of unknown samples and the prototype center of each class is computed to determine the probability of their classification. Based on the category probabilities, the debris flow susceptibility index of the valley is calculated to obtain the evaluation grade for debris flow susceptibility. The model was applied to evaluate the valleys in Nujiang Prefecture, and its results were compared with historical disaster data, yielding a classification accuracy rate of 67.39%. The evaluation levels provided by the model align well with the severity of debris flow disasters in historical events. Compared to traditional methods such as field surveys and factor evaluation, the method proposed in this paper allows for the rapid identification and evaluation of debris flow disaster areas using remote sensing imagery, presenting new insights for research on early warning and prediction of debris flow disasters.
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Key words:
- debris flow /
- remote sensing image /
- few-shot learning /
- prototype network /
- susceptibility assessment
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表 1 Google Earth遥感和DEM数据信息
Table 1. Information on Google Earth remote sensing and DEM data
数据信息 Google Earth DEM 数据来源 Google Earth软件 地理空间数据云 http://www.gscloud.cn/home 数据类型 遥感图像 数字高程模型 空间分辨率 2～15 m
按照DEM采样为30 mASTER GDEM V2
垂直精度20m，水平精度30m通道数 3 1 时相 泥石流沟谷/与发生时间后最近时间分辨率影像图像
负样本及评测沟谷/最新遥感图像2015年1月6日发布的ASTER GDEM V2版本 
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表 2 多个特征提取器的分类性能
Table 2. Classification performance of multiple feature extractors
特征提取器 6分类/% 2分类/% VGG 55.43 59.78 GoogleNet 51.09 56.52 ShuffleNetV2 47.82 51.09 MobileNetV2 53.26 55.43 ResNet12 40.22 44.57 ResNet18 44.57 48.91 DenseNet 50.00 64.13 Rir 51.08 57.61 Conv4 58.70 67.39
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表 3 6分类测试混淆矩阵
Table 3. Confusion matrix of six-classification
6分类混淆矩阵 预测值 0 1 2 3 4 5 真实值 0 9 2 0 2 1 0 1 0 10 1 0 2 6 2 0 2 12 0 0 3 3 2 2 0 9 0 0 4 0 8 0 0 5 2 5 0 0 4 0 1 9
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表 4 正负2分类测试混淆矩阵
Table 4. Confusion matrix for positive and negative binary-classification test
2分类混淆矩阵 预测值 正样本 负样本 真实值 正样本 36 14 负样本 16 26
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表 5 2分类和6分类指标表
Table 5. Binary-classification and six-classification indicator table
Accuracy/% Precision/% Recall/% F1 Score Kappa 67.39 69.23 72.00 0.71 0.50
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表 6 4条泥石流沟谷原型网络计算的所属概率和易发性指数
Table 6. Probability and susceptibility index of four debris flow valleys with prototype networks
沟谷流域 编号 所属概率 
易发性指数 
0 1 2 3 4 5 普拉底乡东月谷 A 0.000 4 0.966 3 0.002 7 0 0.014 5 0.016 2 0.966 3 普拉底乡咪谷河 B 0.000 7 0.999 3 0 0 0 0 0.999 3 独龙江乡巴坡村沟谷 C 0.863 4 0.105 7 0.006 2 0.001 9 0.009 4 0.013 3 0.863 4 金顶镇七联村练登大沟 D 0.315 3 0.504 2 0.002 4 0.060 3 0.113 9 0.003 9 0.504 2
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表 7 4条泥石流沟谷因子分析方法计算的所属概率和易发性指数
Table 7. Probability and susceptibility index of four debris flow valleys with factor analysis methods
沟谷流域 编号 所属概率 
易发性指数 
0 1 2 3 4 5 普拉底乡东月谷 A 0 0.08 0.79 0 0.01 0.12 0.79 普拉底乡咪谷河 B 0.53 0 0.04 0.41 0.02 0 0.53 独龙江乡巴坡村沟谷 C 0.31 0 0 0.69 0 0 −0.69 金顶镇七联村练登大沟 D 0.68 0.02 0.04 0.11 0.13 0.02 0.68
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表 8 4条泥石流沟谷的地貌条件和物质条件因子分析
Table 8. Factors analysis of geomorphological and material conditions of four debris flow valleys
地貌条件和
物源条件泥石流沟谷 东月谷 咪谷河 巴坡村沟谷 练登大沟 主沟长度/km 16.52 14.70 2.45 10.30 面积/km2 45.90 47.22 1.10 16.26 高程差/m 2 854 2 548 1 244 1 386 坡降比 0.17 0.17 0.51 0.13 平均坡度/（°） 15.46 26.49 33.64 16.17 Melton指数 0.42 0.37 1.18 0.34 植被覆盖[35] 有林地41.66%
灌木林10.42%
疏林地31.25%
中覆盖度草地16.67%有林地55.55%
灌木林8.88%
疏林地15.52%
中覆盖度草地5.03%
低覆盖度草地15.02%有林地50.00%
灌木林50.00%疏林地47.06%
高覆盖度草地47.06%
其它建设用地5.88%土壤性质[36] 松软薄层土
简育高活性淋溶土松软薄层土
简育高活性淋溶土简育高活性强酸土
简育高活性淋溶土不饱和雏形土
简育高活性淋溶土地层岩性[36] 花岗岩、板岩、千枚岩、片岩 花岗岩、板岩、千枚岩、片岩 花岗岩、石灰岩和其他碳酸盐岩 板岩、千枚岩、杂砂岩、长石砂岩、砂岩
石灰岩和其他碳酸盐岩
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