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

Scenarios Construction and Spatial-temporal Deduction of Typhoon Storm Surge

Wenli RAO1...1 and Nianxue LUO22,* |Show fewer author(s)
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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    Figures & Tables(12)
    Structure of scenario in storm surge
    UML(Unified Modeling Language) diagram of storm surge
    Evolution of storm surge scenario
    Scenario elements of Mangkhut
    Forecast path of at 11o'clock of Mangkhut on September 16, 2018
    Damaged body within the influence range of Mangkhut at 11 o'clock on on September 16, 2018
    Results of predicting the increase and decrease of water of Mangkhut by different agencies at 17 o'clock on September 16, 2018
    Scenario deduction of Mangkhut based on dynamic Bayesian network
    • Table 1. Datas and sources using in instance analysis

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      Table 1. Datas and sources using in instance analysis

      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 2. Scenario chain of Mangkhut (partial deduction)

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      Table 2. Scenario chain of Mangkhut (partial deduction)

      情景名称承灾体应急管理
      山竹台风(S0)广东沿海城市(A0)建立应急指挥救援小组,启动预案(D0)
      溃堤(S1)广东沿海城市(A1)加紧抢修溃堤口,加固堤坝(D1)
      事件消失(S2)广东沿海城市
      海水倒灌(S3)广东沿海城市(A3)紧急疏散、转移可能倒灌区域人员(D3)
      洪水(S4)广东沿海城市(A4)蓄洪滞洪分洪,人员转移安置(D4)
      事件消失(S5)广东沿海城市
      滑坡(S6)广东沿海城市(A6)撤离危险区人员,疏通排水渠道,引导泥石流顺畅流动(D6)
      事件消失(S7)广东沿海城市
    • Table 3. Variable type and value set of network node

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      Table 3. Variable type and value set of network node

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

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

      情景节点变量先验概率条件概率
      山竹台风S0P(D0=T)0.95P(S0=T|D0=T,A0=P)0.95
      P(D0=F)0.05P(S0=T|D0=T,A0=N)0.80
      P(A0=P)0.70P(S0=T|D0=F,A0=P)0.70
      P(A0=N)0.30P(S0=T|D0=F,A0=N)0.40
      溃堤S1P(D1=T)0.96P(S1=T|D1=T,A1=P,S0=T)0.90
      P(D1=F)0.04P(S1=T|D1=T,A1=P,S0=F)0.70
      P(A1=P)0.75P(S1=T|D1=T,A1=N,S0=T)0.85
      P(A1=N)0.25P(S1=T|D1=T,A1=N,S0=F)0.60
      P(S1=T|D1=F,A1=P,S0=T)0.55
      P(S1=T|D1=F,A1=P,S0=F)0.40
      P(S1=T|D1=F,A1=N,S0=T)0.30
      P(S1=T|D1=F,A1=N,S0=F)0.25
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    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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    Paper Information

    Received: Oct. 15, 2019

    Accepted: --

    Published Online: Sep. 16, 2020

    The Author Email: LUO Nianxue (nxluo@sgg.whu.edu.cn)

    DOI:10.12082/dqxxkx.2020.190604

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