Rise of precision: a review of emergency risk analysis methodology

doi:10.16265/j.cnki.issn1003-3033.2022.09.2742

Abstract

Abstract:

In order to improve the timeliness and accuracy of emergency risk analysis, this paper reviewed the development of emergency risk analysis methods through systematic literature insights. The development of emergency risk analysis methods, the research status of technologies, methods, models, and theories supporting quantitative and precise risk analysis, and the difficulties in quantifying complex emergencies and catastrophes were discussed. The results show that the future development trends of emergency risk analysis methods and theories include: studying and proposing methods to optimize qualitative, semi-quantitative, and quantitative models by using information technology such as big data of sensing monitoring and computing, developing emergency risk calculation methods based on data & model & calculation, and developing the ability of accurate risk calculation.

Key words: emergency, disaster, risk analysis, simulation and modeling, risk computing

LIU Yi, QIAN Jing, FAN Weicheng. Rise of precision: a review of emergency risk analysis methodology[J]. China Safety Science Journal, 2022, 32(9): 1-10.

Figures/Tables 4

Tab.1

Fig.1

Tab.2

Data sources

数据采集机构	网址
国际地震中心(International Seismological Center,ISC)	http://www.isc.ac.uk/standards/ datacollection/
全球地震网(Global Seismographic Network,IRIS)	http://www.iris.edu/
全球火山计划(The Smithsonian Institution's Global Volcanism Program,GVP)	https://volcano.si.edu/gvp_about.cfm
火山数据库(World Organization of Volcano Observatories,WOVOdat)	http://www.wovodat.org/
CRED	https://www.emdat.be
商业遥感空间(Commercial Remote Sensing Space Policy,CRSSP)	http://crssp.usgs.gov/
欧洲安全、可靠性与数据协会(European Safety,Reliability and Data Association,ESReDA)	https://www.esreda.org/
社交媒体分析(Social Media Analytic,SMA)	http://www.datalabs.com.au/
美国地质调查局(United State Geological Survey,USGS)	http://www.usgs.gov/
美国联邦政府数据资源库(A repository of federal enterprise data resource,DATAGOV)	https://catalog.data.gov/ dataset/global
美国国家环境信息中心(National Centers for Environmental Information,NCEI)	https://www.ncei.noaa.gov
NTSB	https://www.ntsb.gov/Pages/ home.aspx/
美国航空航天局(National Aeronautics and Space Administration,NASA)	https://www.nasa.gov
日本国家地球科学和抗灾研究所(the National Research Institute for Earth Science and Disaster Resilience,NIED)	https://www.bosai.go.jp/e/ research/database/
日本气象厅(Japan Meteorological Agency,JMA)	http://www.jma.go.jp/jma/en/ Activities/earthquake.html
新西兰地质灾害信息网(Geological hazard information for New Zealand,GeoNet)	https://www.geonet.org.nz/volcano
印度国家遥感中心(National Remote Sensing Center,NRSC)	http://www.nrsc.gov.in
中国国家气象科学数据中心(China Meteorological Data Service Centre,CMDSC)	http://data.cma.cn/share/subject.html
中国国家地震科学数据中心(National Earthquake Data Center,NEDC)	https://data.earthquake.cn/index.html

Tab.2

Tab.3

Data types and sources of data related to disaster

灾害类型	数据分类	数据格式	数据说明	机构
地震、海啸	地震数据	里氏震级和EXCEL数据表	1∶5 000 000至1∶50 000尺度的小尺度(区域)地震带区划,以及1∶50—25 000 ~1∶10 000尺度的大尺度(局部)地震区划	ISC, IRIS, NIED, USGS, CRED, NEDC
海啸、洪水、干旱	水文数据	谷歌地球图像	水位、水压和水密度等	CRED,NIED,NCEI
滑坡	地质数据	不同设备包含不同数据存储格式	降雨、湿度、孔隙压力、倾斜、振动等	USGS, NIED, GeoNet
洪水、海啸	无线传感网络数据	通过无线发射器和接收器发送和接收数据	模拟警告信号(需要模数转换器)	NIED
火山爆发	地质和地震数据	里氏震级和MySQL和XML格式	空间、时间和主题数据(如角度、电火花、全球定位系统、雷达图像图像、重力、磁场等)	WOVOdat, JMA, DATAGOV, USGS.
全类型灾害	遥感数据	图像格式数据	空间、时间和专题数据(卫星数据)	CRSSP, NASA, NRSC,CMDSC
全类型灾害	谷歌地图数据	表单和文本格式	空间、时间和专题数据(洪水和洪水前的雷达图像)	NASA,CMDSC
航空、高速公路、海洋、管道和铁路	事故数据	文本格式	时间、地点、伤亡、损失、事故原因、环境、设备、人员情况	NTSB
全类型灾害	气象数据、遥感数据、地图数据、海洋数据	表单、图像、文本、脚本等多种数据格式	气象、海洋、卫星、雷达、自然灾害、地质、海岸线等专题数据	NCEI,CMDSC
台风	地图数据	图像和文本格式数据	时间、空间、气象专题数据	NIED,CMDSC
全类型灾害	地质、水文、事故数据等	EXCEL数据表	时间、地点、经度、纬度、受灾面积、地震区划、震级、风速等	CRED
全类型灾害	可靠性数据	文本、表单多种数据格式	多个行业的产品和过程的安全和可靠性研究信息、数据和成果	ESReDA

Tab.3

References 46

[1]	陈玉和, 姜秀娟. 风险评价[M]. 北京: 中国标准出版社, 2009:67-72.
[2]	AMES D P, HORSBURGH J S, CAO Yang, et al. HydroDesktop: web services-based software for hydrologic data discovery, download, visualization, and analysis[J]. Environmental Modelling & Software, 2012, 37:146-156.
[3]	BARNES B, DUNN S, WILKINSON S. Natural hazards, disaster management and simulation: a bibliometric analysis of keyword searches[J]. Natural Hazards, 2019, 97(2):813-840. doi: 10.1007/s11069-019-03677-2
[4]	SERMET Y, DEMIR I. An intelligent system on knowledge generation and communication about flooding[J]. Environmental Modelling & Software, 2018, 108:51-60.
[5]	巴鲁克·费斯科霍夫, 莎拉·利希藤斯坦, 保罗·斯诺维克, 等 [英]. 人类可接受风险[M].王红漫,译. 北京: 北京大学出版社, 2009:45-48.
	FISCHHOFF B, LICHTENSTEIN S, SLOVIC P, et al. Acceptable risk[M]. WANG Hongman,Translated. Beijing: Peking University Press, 2009:45-48.
[6]	KAPLAN S, GARRICK B J. On the quantitative definition of risk[J]. Risk Analysis, 1981, 1(1):11-27. doi: 10.1111/j.1539-6924.1981.tb01350.x
[7]	郝红岩. 风险思维: 理论及应用[M]. 北京: 电子工业出版社, 2019:67-71.
[8]	GOSWAMI S, CHAKRABORTY S, GHOSH S, et al. A review on application of data mining techniques to combat natural disasters[J]. Ain Shams Engineering Journal, 2016, 9(3):365-378. doi: 10.1016/j.asej.2016.01.012
[9]	SAJJAD M. Disaster resilience in Pakistan: a comprehensive multi-dimensional spatial profiling[J]. Applied Geography, 2021, 126:DOI: 10.1016/j.apgeog.2020.102367. doi: 10.1016/j.apgeog.2020.102367
[10]	AHMED S, HASAN M Z, PONGSIRI M J, et al. Effect of extreme weather events on injury, disability, and death in Bangladesh[J]. Climate and Development, 2021, 13(4): 306-317. doi: 10.1080/17565529.2020.1772705
[11]	SHRESTHA B B, PERERA E D P, KUDO S, et al. Assessing flood disaster impacts in agriculture under climate change in the river basins of Southeast Asia[J]. Natural Hazards, 2019, 97(1):157-192. doi: 10.1007/s11069-019-03632-1
[12]	LI Xiangnan, YAN Denghua, WANG Kun, et al. Flood risk assessment of global watersheds based on multiple machine learning models[J]. Water, 2019, 11(8):DOI: 10.3390/w11081654.
[13]	BINH Thai P, CHINH L, TRAN VAN P, et al. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam[J]. Journal of Hydrology, 2021, 592:DOI: 10.1016/j.jhydrol.2020.125815. doi: 10.1016/j.jhydrol.2020.125815
[14]	SOULARD C E, WALKER J J, PETRAKIS R E. Implementation of a surface water extent model in cambodia using cloud-based remote sensing[J]. Remote Sensing, 2020, 12(6):DOI: 10.3390/rs12060984.
[15]	SANTOS P P, REIS E. Assessment of stream flood susceptibility: a cross-analysis between model results and flood losses[J]. Journal of Flood Risk Management, 2018, 11:1038-1050.
[16]	LI Linyi, CHEN Yun, XU Tingbao, et al. Spatial attraction models coupled with Elman neural networks for enhancing sub-pixel urban inundation mapping[J]. Remote Sensing, 2020, 12(13):DOI: 10.3390/rs12132068. doi: 10.3390/rs12132068
[17]	COSTABILE P, COSTANZO C, DE LORENZO G, et al. Is local flood hazard assessment in urban areas significantly influenced by the physical complexity of the hydrodynamic inundation model?[J]. Journal of Hydrology, 2020, 580:DOI: 10.1016/j.jhydrol.2019.124231. doi: 10.1016/j.jhydrol.2019.124231
[18]	BHUYIAN M N M, KALYANAPU A. Accounting digital elevation uncertainty for flood consequence assessment[J]. Journal of Flood Risk Management, 2018, 11(S2):1051-1062.
[19]	MIHU-PINTILIE A, CIMPIANU C I, STOLERIU C C, et al. Using high-density LiDAR data and 2D streamflow hydraulic modeling to improve urban flood hazard maps: a HEC-RAS multi-scenario approach[J]. Water, 2019, 11(9):DOI: 10.3390/w11091832.
[20]	MISHRA D, KUMAR S, HASSINI E. Current trends in disaster management simulation modelling research[J]. Annals of Operations Research, 2018, 283(3):1387-1411. doi: 10.1007/s10479-018-2985-x
[21]	UTOMO D S, ONGGO B S, ELDRIDGE S. Applications of agent-based modelling and simulation in the agri-food supply chains[J]. European Journal of Operational Research, 2018, 269(3):794-805. doi: 10.1016/j.ejor.2017.10.041
[22]	LIU Qingrong, RUAN Chengqing, GUO Jingtian, et al. Storm surge hazard assessment of the levee of a rapidly developing city-based on LiDAR and numerical models[J]. Remote Sensing, 2020, 12(22):DOI: 10.3390/rs12223723.
[23]	JIMENEZ-PERALVAREZ J D, EI HAMDOUNI R, PALENZUELA J A, et al. Landslide-hazard mapping through multi-technique activity assessment: an example from the Betic Cordillera (southern Spain)[J]. Landslides, 2017, 14(6):1975-1991. doi: 10.1007/s10346-017-0851-6
[24]	NATALIE B, LOUIS A, RICHARD G, et al. Window-based morphometric indices as predictive variables for landslide susceptibility models[J]. Remote Sensing, 2021, 13(3):DOI: 10.3390/RS13030451.
[25]	MOHAJANE M, COSTACHE R, KARIMI F, et al. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area[J]. Ecological Indicators, 2021, 129:DOI: 10.1016/J.ECOLIND.2021.107869. doi: 10.1016/J.ECOLIND.2021.107869
[26]	YAO Xiaochuang, ZHU Dehai, YUN Wenju, et al. A WebGIS-based decision support system for locust prevention and control in China[J]. Computers and Electronics in Agriculture, 2017, 140:148-158. doi: 10.1016/j.compag.2017.06.001
[27]	RANA I A, ROUTRAY J K. Integrated methodology for flood risk assessment and application in urban communities of Pakistan[J]. Natural Hazards, 2018, 91(1):239-266. doi: 10.1007/s11069-017-3124-8
[28]	ARRIGHI C, ROSSI L, TRASFORINI E, et al. Quantification of flood risk mitigation benefits: a building-scale damage assessment through the RASOR platform[J]. Journal of Environmental Management, 2018, 207:92-104. doi: S0301-4797(17)31092-7 pmid: 29154012
[29]	ARRIGHI C, MASI M, IANNELLI R. Flood risk assessment of environmental pollution hotspots[J]. Environmental Modelling & Software, 2018, 100:1-10.
[30]	JOHNSON S, YU Dapeng. From flooding to finance: NHS ambulance-assisted evacuations of care home residents in Norfolk and Suffolk, UK[J]. Journal of Flood Risk Management, 2020, 13(1):DOI:10.1111/jfr3.12592.
[31]	FU Sheng, CHEN Lixia, WOLDAI T, et al. Landslide hazard probability and risk assessment at the community level: a case of western Hubei, China[J]. Natural Hazards and Earth System Sciences, 2020, 20(2):581-601.
[32]	LIN Chunjin, ZHANG Meng, ZHOU Zongqing, et al. A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model[J]. Tunnelling and Underground Space Technology, 2020, 95:DOI: 10.1016/j.tust.2019.103136. doi: 10.1016/j.tust.2019.103136
[33]	YEKEEN S T, BALOGUN A L. Advances in remote sensing technology, machine learning and deep learning for marine oil spill detection, prediction and vulnerability assessment[J]. Remote Sensing, 2020, 12(20):DOI:10.3390/rs12203416.
[34]	KECHIDI S, CASTRO J M, MONTEIRO R, et al. Development of exposure datasets for earthquake damage and risk modelling: the case study of northern Algeria[J]. Bulletin of Earthquake Engineering, 2021, 19(12):5253-5283. doi: 10.1007/s10518-021-01161-6
[35]	BODDUPALLI C, SADHU A, AZAR E R, et al. Improved visualization of infrastructure monitoring data using building information modeling[J]. Structure and Infrastructure Engineering, 2019, 15(9):1247-1263. doi: 10.1080/15732479.2019.1602150
[36]	LIU Ying, WANG Zhixiao, REN Jinging, et al. A COVID-19 risk assessment decision support system for general practitioners: design and development study[J]. Journal of Medical Internet Research, 2020, 22(6):DOI:10.2196/19786.
[37]	QIE Zijun, RONG Lili. An integrated relative risk assessment model for urban disaster loss in view of disaster system theory[J]. Natural Hazards, 2017, 88(1):165-190. doi: 10.1007/s11069-017-2861-z
[38]	NATEGHI R, SUTTON J, MURRAY-TUITE P. The frontiers of uncertainty estimation in interdisciplinary disaster research and practice[J]. Risk Analysis, 2021, 41(7):1129-1135. doi: 10.1111/risa.13337
[39]	PINELLI J P, ESTEVA M, RATHJE E M, et al. Disaster risk management through the design Safe cyberinfrastructure[J]. International Journal of Disaster Risk Science, 2020, 11(6):719-734. doi: 10.1007/s13753-020-00320-8
[40]	KAPLAN R M, CHAMBERS D A, GLASGOW R E. Big data and large sample size: a cautionary note on the potential for bias[J]. Clinical and Translational Science, 2014, 7(4):342-346. doi: 10.1111/cts.12178 pmid: 25043853
[41]	CORNWALL W. Doomsday machines[J]. Nature, 2016, 353(6296):238-241.
[42]	LI Guoqing, ZHAO Jing, MURRAY V, et al. Gap analysis on open data interconnectivity for disaster risk research[J]. Geo-Spatial Information Science, 2019, 22(1):45-58. doi: 10.1080/10095020.2018.1560056
[43]	MEHROTRA S, QIU Xiaogang, CAO Zhidong, et al. Technological challenges in emergency response[J]. IEEE Intelligent Systems, 2013, 28(4):5-8.
[44]	XIAO Yang, LI Beiqun, GONG Zaiwu. Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data[J]. Natural Hazards, 2018, 94(2):833-842. doi: 10.1007/s11069-018-3427-4
[45]	AMAILEF K, LU Jie. Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services[J]. Decision Support Systems, 2013, 55(1):79-97. doi: 10.1016/j.dss.2012.12.034
[46]	ALAMDAR F, KALANTARI M, RAJABIFARD A. Towards multi-agency sensor information integration for disaster management[J]. Computers Environment & Urban Systems, 2016, 56:68-85.

研究类型	研究主题	文献数量
自然灾害	洪水	37
	山体滑坡	17
	地震	3
	风暴潮	3
	台风	2
	暴雨内涝	1
	蝗灾	1
	风蚀	1
	干旱	1
	海底滑坡	1
	落石	1
	泥石流	1
	森林火灾	1
	土壤流失	1
	土壤迁移	1
	土壤侵蚀	1
	土壤液化	1
	雪崩	1
事故灾难	建筑物风险暴露	3
	海洋石油泄漏	1
	化工厂事故	1
	煤矿灾害	1
	隧道突水	1
	尾矿溃坝	1
公共卫生	传染病	1
受灾人群	个体风险	2
多灾种混合	综合风险	15
灾害管理	灾难恢复	2
灾害管理	风险决策	1
研究方法	数据的不确定性	2
研究方法	评估方法的不确定性	1