Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable opera...Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.展开更多
Taking the rainstorm flood disaster of Huaihe River basin as the research object,according to the principles of risk assessment for natural disasters,starting from the fatalness of inducing factors and the vulnerabili...Taking the rainstorm flood disaster of Huaihe River basin as the research object,according to the principles of risk assessment for natural disasters,starting from the fatalness of inducing factors and the vulnerability of hazard bearing body,the weight of each impact factor was calculated by using AHP. By using spatial analysis and statistical function of GIS,the integrated risk chart of rainstorm flood disaster in Huaihe River basin was obtained. The results showed that the high risk areas of rainstorm flood disaster in Huaihe River basin mainly distributed in the southern part of Henan,the central northern part of Anhui and eastern part of Jiangsu Province. Due to higher fatalness of inducing factors in southern Henan,there was high risk in the region. Central Anhui and east Jiangsu were not only high-fatalness zones but also high vulnerability zones of population and economy.展开更多
Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The re...Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The result is that knowledge gained can only benefit future responses rather than the response under investigation. This article argues that there is an opportunity to conduct analysis while the response is operational due to the increasing availability of information within hours and days of a disaster event. Hence, this article introduces a methodology for analyzing publicly communicated disaster response information in near-real-time. A classification scheme for the disaster information needs of the public has been developed to facilitate analysis and has led to the establishment of best observed practice standards for content and timeliness. By comparing the information shared with the public within days of a disaster to these standards,information gaps are revealed that can be investigated further. The result is identification of potential deficiencies in communicating critical disaster response information to the public at a time when they can still be corrected.展开更多
Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep uncertainty.Due to deep uncertainty,decision-makers have severe i...Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep uncertainty.Due to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of interest.In this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep uncertainty.In the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future scenarios.Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system.This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated.Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions.The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.展开更多
文摘Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.
文摘Taking the rainstorm flood disaster of Huaihe River basin as the research object,according to the principles of risk assessment for natural disasters,starting from the fatalness of inducing factors and the vulnerability of hazard bearing body,the weight of each impact factor was calculated by using AHP. By using spatial analysis and statistical function of GIS,the integrated risk chart of rainstorm flood disaster in Huaihe River basin was obtained. The results showed that the high risk areas of rainstorm flood disaster in Huaihe River basin mainly distributed in the southern part of Henan,the central northern part of Anhui and eastern part of Jiangsu Province. Due to higher fatalness of inducing factors in southern Henan,there was high risk in the region. Central Anhui and east Jiangsu were not only high-fatalness zones but also high vulnerability zones of population and economy.
文摘Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The result is that knowledge gained can only benefit future responses rather than the response under investigation. This article argues that there is an opportunity to conduct analysis while the response is operational due to the increasing availability of information within hours and days of a disaster event. Hence, this article introduces a methodology for analyzing publicly communicated disaster response information in near-real-time. A classification scheme for the disaster information needs of the public has been developed to facilitate analysis and has led to the establishment of best observed practice standards for content and timeliness. By comparing the information shared with the public within days of a disaster to these standards,information gaps are revealed that can be investigated further. The result is identification of potential deficiencies in communicating critical disaster response information to the public at a time when they can still be corrected.
基金This research was supported by the National Natural Science Foundation of China[72004141,72174102,72334003]the Guangdong Office of Philosophy and Social Science[GD23XGL115].
文摘Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep uncertainty.Due to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of interest.In this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep uncertainty.In the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future scenarios.Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system.This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated.Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions.The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.