Scenarios Construction and Spatial-temporal Deduction of Typhoon Storm Surge

Wenli RAO; Nianxue LUO

doi:10.12082/dqxxkx.2020.190604

Journals >Journal of Geo-information Science >Volume 22 >Issue 2 >Page 187 > Article

Journal of Geo-information Science
Vol. 22, Issue 2, 187 (2020)

Scenarios Construction and Spatial-temporal Deduction of Typhoon Storm Surge

Wenli RAO^1、1 and Nianxue LUO^2、2、*

Author Affiliations

¹Beijing Global Safety Technology Company Limited, Wuhan 430000, China

¹北京辰安科技股份有限公司,武汉 430000

²School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

²武汉大学测绘学院,武汉 430079

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DOI: 10.12082/dqxxkx.2020.190604 Cite this Article

Wenli RAO, Nianxue LUO. Scenarios Construction and Spatial-temporal Deduction of Typhoon Storm Surge[J]. Journal of Geo-information Science, 2020, 22(2): 187 Copy Citation Text

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Fig. 1. Structure of scenario in storm surge

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Fig. 2. UML(Unified Modeling Language) diagram of storm surge

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Fig. 3. Evolution of storm surge scenario

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Fig. 4. Scenario elements of Mangkhut

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Fig. 5. Forecast path of at 11o'clock of Mangkhut on September 16, 2018

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Fig. 6. Damaged body within the influence range of Mangkhut at 11 o'clock on on September 16, 2018

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Fig. 7. Results of predicting the increase and decrease of water of Mangkhut by different agencies at 17 o'clock on September 16, 2018

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Fig. 8. Scenario deduction of Mangkhut based on dynamic Bayesian network

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数据	数据来源	描述
承灾体	课题组制定	Shapefile格式;包含沿海城市的堤防工程、重点保护目标、生态敏感目标、沿岸社区人口与房屋、沿岸内陆区域、电力设施、地质灾害高发区等多种类型承灾体的名称、类别、编码、位置、脆弱性等级等
台风数据	气象局	GeoJSON、CSV格式;包含时间、位置、台风等级、台风强度、风速、中心气压、风圈半径等
台风预测数据	中国、中国香港、中国台湾、美国、韩国、日本6个台风预测机构提供	GeoJSON格式
底图	http://www.tianditu.gov.cn/	天地图
应急管理数据	课题组制定	Excel格式;包含名称、类别、编码、数量等
增减水数据	课题组模拟设计	Tiff格式;中国、中国香港、日本3个机构预测的山竹16日17时增减水模拟数据

Table 1. Datas and sources using in instance analysis

情景名称	承灾体	应急管理
山竹台风 $(S_{0})$	广东沿海城市 $(A_{0})$	建立应急指挥救援小组,启动预案 $(D_{0})$
溃堤 $(S_{1})$	广东沿海城市 $(A_{1})$	加紧抢修溃堤口,加固堤坝 $(D_{1})$
事件消失 $(S_{2})$	广东沿海城市
海水倒灌 $(S_{3})$	广东沿海城市 $(A_{3})$	紧急疏散、转移可能倒灌区域人员 $(D_{3})$
洪水 $(S_{4})$	广东沿海城市 $(A_{4})$	蓄洪滞洪分洪,人员转移安置 $(D_{4})$
事件消失 $(S_{5})$	广东沿海城市
滑坡 $(S_{6})$	广东沿海城市 $(A_{6})$	撤离危险区人员,疏通排水渠道,引导泥石流顺畅流动 $(D_{6})$
事件消失 $(S_{7})$	广东沿海城市

Table 2. Scenario chain of Mangkhut (partial deduction)

节点名称	类型	取值集合
情景状态 $(S)$	布尔变量	{真 $(T)$ ,假 $(F)$ }
承灾体 $(A)$	二值顺序变量	{积极 $(P)$ ,消极 $(N)$ }
应对措施 $(D)$	布尔变量	{真 $(T)$ ,假 $(F)$ }

Table 3. Variable type and value set of network node

情景	节点变量	先验概率		条件概率
山竹台风	$S_{0}$	$P (D_{0} = T)$	0.95	$P (S_{0} = T \| D_{0} = T, A_{0} = P)$	0.95
		$P (D_{0} = F)$	0.05	$P (S_{0} = T \| D_{0} = T, A_{0} = N)$	0.80
		$P (A_{0} = P)$	0.70	$P (S_{0} = T \| D_{0} = F, A_{0} = P)$	0.70
		$P (A_{0} = N)$	0.30	$P (S_{0} = T \| D_{0} = F, A_{0} = N)$	0.40
溃堤	$S_{1}$	$P (D_{1} = T)$	0.96	$P (S_{1} = T \| D_{1} = T, A_{1} = P, S_{0} = T)$	0.90
		$P (D_{1} = F)$	0.04	$P (S_{1} = T \| D_{1} = T, A_{1} = P, S_{0} = F)$	0.70
		$P (A_{1} = P)$	0.75	$P (S_{1} = T \| D_{1} = T, A_{1} = N, S_{0} = T)$	0.85
		$P (A_{1} = N)$	0.25	$P (S_{1} = T \| D_{1} = T, A_{1} = N, S_{0} = F)$	0.60
		—	—	$P (S_{1} = T \| D_{1} = F, A_{1} = P, S_{0} = T)$	0.55
		—	—	$P (S_{1} = T \| D_{1} = F, A_{1} = P, S_{0} = F)$	0.40
		—	—	$P (S_{1} = T \| D_{1} = F, A_{1} = N, S_{0} = T)$	0.30
		—	—	$P (S_{1} = T \| D_{1} = F, A_{1} = N, S_{0} = F)$	0.25

Table 4. Prior probability and conditional probability of network nodes (partial data)

Wenli RAO, Nianxue LUO. Scenarios Construction and Spatial-temporal Deduction of Typhoon Storm Surge[J]. Journal of Geo-information Science, 2020, 22(2): 187

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