Journal of Tropical Oceanography >
Disaster loss assessment of storm surge based on Dempster-Shafer theory of evidence
Copy editor: YAO Yantao
Received date: 2021-03-29
Revised date: 2021-05-13
Online published: 2021-05-24
A disaster loss assessment method based on the Dempster-Shafer theory of evidence for storm surge is proposed in this paper. Because of uncertainty of storm surge in the disaster process, we select representative indicators (maximum height of storm surge, significant wave height, disaster prevention and reduction ability) to produce several pieces of evidence. The weight of evidence is calculated by using correlation between indicator and direct economic loss of storm surge disaster. A modified Murphy method is used to fuse evidence from different sources to judge the disaster loss level. An example is used to show that the classification accuracy of the method used in this paper can reach 93.1%, which is better than some existing methods, such as the Naive Bayes, Support Vector Machine, Neural Network, and Decision Tree methods. In addition, the method is simple in computation, and the results of disaster loss assessment can be more detailed with increasing training samples.
SUN Fenglin . Disaster loss assessment of storm surge based on Dempster-Shafer theory of evidence[J]. Journal of Tropical Oceanography, 2022 , 41(1) : 75 -81 . DOI: 10.11978/2021037
表1 样本数据及留一法样本测试结果Tab. 1 Sample data and results of the leave-one-out method |
风暴潮编号 | 防灾减灾能力 | 最大增水高度/m | 最大有效波高/m | 实际灾级 | 证据1 | 证据2 | 证据3 | 本文方法预测灾级 |
---|---|---|---|---|---|---|---|---|
201909 | 1.00 | 0.63 | 0.81 | 1 | 1 | 2 | 1 | 1 |
201822 | 0.97 | 0.44 | 0.55 | 1 | 1 | 2 | 1 | 1 |
201911 | 1.00 | 0.89 | 0.80 | 1 | 1 | 2 | 1 | 1 |
201509 | 0.85 | 0.63 | 0.57 | 1 | 1 | 2 | 1 | 1 |
201205 | 0.72 | 1.69 | 0.62 | 1 | 1 | 3 | 1 | 1 |
201601 | 0.88 | 1.50 | 0.89 | 1 | 1 | 3 | 1 | 1 |
201407 | 0.80 | 1.89 | 1.06 | 1 | 1 | 3 | 2 | 1 |
201006 | 0.60 | 0.91 | 0.69 | 1 | 2 | 2 | 1 | 1 |
201011 | 0.60 | 1.84 | 0.94 | 1 | 2 | 3 | 1 | 1 |
201111 | 0.66 | 0.56 | 0.76 | 1 | 1 | 2 | 1 | 1 |
200108 | 0.37 | 1.96 | 1.00 | 1 | 3 | 3 | 2 | 3 |
201617 | 0.88 | 1.59 | 0.99 | 1 | 1 | 3 | 2 | 1 |
201308 | 0.77 | 0.58 | 0.63 | 1 | 1 | 2 | 1 | 1 |
201323 | 0.77 | 0.76 | 1.23 | 2 | 1 | 2 | 2 | 2 |
201312 | 0.77 | 1.64 | 1.41 | 2 | 1 | 3 | 2 | 2 |
199504 | 0.30 | 0.69 | 1.00 | 2 | 3 | 2 | 2 | 2 |
200808 | 0.52 | 1.34 | 1.05 | 2 | 2 | 2 | 2 | 2 |
200010 | 0.35 | 1.82 | 0.90 | 2 | 3 | 3 | 1 | 3 |
199417 | 0.29 | 0.42 | 1.05 | 2 | 3 | 2 | 2 | 2 |
200908 | 0.55 | 0.90 | 1.32 | 2 | 2 | 2 | 2 | 2 |
201013 | 0.60 | 1.33 | 1.02 | 2 | 2 | 2 | 2 | 2 |
200216 | 0.38 | 1.30 | 1.20 | 3 | 3 | 2 | 2 | 3 |
199012 | 0.26 | 2.92 | 0.18 | 3 | 3 | 3 | 1 | 3 |
200513 | 0.43 | 1.74 | 0.83 | 3 | 3 | 3 | 1 | 3 |
199914 | 0.34 | 1.40 | 0.97 | 3 | 3 | 2 | 2 | 3 |
200102 | 0.37 | 2.76 | 1.22 | 3 | 3 | 3 | 2 | 3 |
199607 | 0.31 | 1.49 | 0.89 | 3 | 3 | 3 | 1 | 3 |
200604 | 0.45 | 1.78 | 1.14 | 3 | 3 | 3 | 2 | 3 |
200608 | 0.45 | 2.36 | 1.26 | 3 | 3 | 3 | 2 | 3 |
注: 阴影代表实际灾级与本文方法预测灾级不符的样本 |
表2 风暴潮灾害灾级识别结果Tab. 2 Results of disaster loss level for storm surge |
灾级 | 本文方法 | DS融合 | Murphy方法 | |||
---|---|---|---|---|---|---|
正确数 | 错误数 | 总数 | 正确率 | |||
一级 | 12 | 1 | 13 | 92.3% | 77% | 77% |
二级 | 7 | 1 | 8 | 87.5% | 88% | 88% |
三级 | 8 | 0 | 8 | 100.0% | 88% | 88% |
总体 | 27 | 2 | 29 | 93.1% | 83% | 83% |
表3 多种方法的结果比较Tab. 3 Comparison of results of five methods |
灾级 | 本文方法 | 朴素贝叶斯 | 支持向量机 | 神经网络 | 决策树 |
---|---|---|---|---|---|
一级 | 92.3% | 92.3% | 92.3% | 84.6% | 92.3% |
二级 | 87.5% | 87.5% | 50.0% | 62.5% | 0.0% |
三级 | 100.0% | 62.5% | 75.0% | 62.5% | 100.0% |
总体 | 93.1% | 82.8% | 75.9% | 72.4% | 69.0% |
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