基于贝叶斯网络和GIS的热带气旋灾害风险评估

doi:10.11978/2022092

Abstract

Abstract:

To address the complexity and uncertainty of tropical cyclone disaster, this study proposes a new tropical cyclone disaster risk assessment model based on the Bayesian network and geographic information system (GIS). The model can automatically explore the causal relationships among disaster influencing factors from objective historical data, and express them in the form of probabilities to assess and predict uncertain disaster risks. Based on the historical data of tropical cyclone disaster in three southeastern coastal provinces (Guangdong, Fujian, and Zhejiang) of China from 1980 to 2016 for risk assessment experiments, a total of 12 assessment indicators in three aspects, i.e., hazard of disaster-causing factors, sensitivity of disaster-inducing environment, and vulnerability of disaster-bearing bodies, were selected as model inputs, and direct economic losses were quantified as disaster risk levels as model outputs to construct a Bayesian network-based risk assessment model. The model was then tested against cyclone disaster data from 2017 to 2021, and the accuracy of the assessment prediction was 80.75%. The relative errors of very low, low, medium, high and very high risks predicted by the model were 27.72%, 8.45%, 18.58%, 16.52%and 19.12%, respectively, and the zonal results of risk prediction values were highly consistent with the actual disaster loss distribution in terms of spatial patterns. In addition, the assessment construction method was applied to the risk assessment of individual cases of Typhoon “Meranti”. The results showed that the high and very high risk areas assessed by the model were basically consistent with the actual disaster reports. Thus, the tropical cyclone disaster risk assessment model established in this study has high accuracy and credibility, and provides a new methodological approach and technical support for tropical cyclone disaster risk assessment.

Key words: tropical cyclone disaster, risk assessment, three southeastern coastal provinces, Bayesian network, GIS

TIAN Cheng, LI Xin, DU Yang, LI Ming, XIE Yong, XIA Jilu. Assessment of tropical cyclone disaster risk based on the Bayesian network and GIS[J].Journal of Tropical Oceanography, 2023, 42(5): 17-29.

Figures/Tables 13

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风险组成成分	风险指标
致灾因子危险性	最小中心压强
	频率
	影响持续时间
	降水
	风速
孕灾环境敏感性	高程
	坡度
	土里利用覆盖率
	归一化植被指数
	海岸线接近距离
承灾体脆弱性	地方生产总值(GDP)
承灾体脆弱性	人口密度

划分的区域单元格	1	2	3	4	……	4039	4040	4041
最小中心压强	1	1	1	2	……	4	4	4
频率	1	1	1	0	……	4	4	4
影响持续时间	0	0	0	0	……	2	4	4
降水	4	4	4	4	……	0	0	0
风速	2	2	2	2	……	2	1	2
高程	3	4	3	4	……	3	3	3
坡度	4	4	3	4	……	3	3	3
土地利用覆盖率	1	3	4	4	……	3	4	0
归一化植被指数	2	0	2	3	……	1	1	1
海岸线接近距离	2	2	2	3	……	4	4	4
GDP	4	4	4	4	……	4	4	4
人口密度	0	0	0	0	……	0	0	0
热带气旋灾害风险	3	3	3	3	……	2	2	2

降水等级	灾害风险等级
降水等级	0	1	2	3	4
0	0.0206	0.0995	0.0443	0.0198	0.0000
1	0.1777	0.0357	0.0521	0.0555	0.0201
2	0.0985	0.0636	0.0404	0.0047	0.0573
3	0.0073	0.0675	0.0010	0.0177	0.0417
4	0.0000	0.0000	0.0000	0.0443	0.0307

测试集样本	0	1	2	3	4	评估风险等级	实际风险等级
1	0	0	0	0.811148	0.188852	3	4
2	0.844427	0.155573	0	0	0	0	0
3	0.862136	0.137864	0	0	0	0	0
4	0	0	0	0	1	4	4
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
201	0	0	0.029417	0.970583	0	3	3
202	1	0	0	0	0	0	0
203	1	0	0	0	0	0	0

风险等级	0	1	2	3	4
模型预测风险等级	1055	1424	951	556	55
实际风险等级	826	1313	1168	666	68
相对误差/%	27.72	8.45	18.58	16.52	19.12

Assessment of tropical cyclone disaster risk based on the Bayesian network and GIS

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