With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development ...With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers.The Bayesian network(BN)was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies.To capture the interacting relationships among different factors,a scenario representation model of disaster chains was developed,followed by the determination of the BN structure.In deriving the conditional probability tables of the BN model,we found that,due to the lack of data and the significant uncertainty of disaster chains,parameter learning methodologies based on data or expert knowledge alone are insufficient.By integrating both sample data and expert knowledge with the maximum entropy principle,we proposed a parameter estimation algorithm under expert prior knowledge(PEUK).Taking the rainstorm disaster chain as an example,we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP)algorithm and the direct expert opinion elicitation method.The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.展开更多
With the rapid development of China's economy,external dependence on petroleum resources continues to increase,and their security has become an important part of national security.To evaluate the security of China...With the rapid development of China's economy,external dependence on petroleum resources continues to increase,and their security has become an important part of national security.To evaluate the security of China's petroleum resource supply in a scientific and objective manner,this study establishes a corresponding petroleum life-cycle evaluation index system,based on the theory and method of the whole life-cycle security evaluation of mineral resources,and conducts further independence and grey correlation analysis on the indexes for the purpose of evaluating the petroleum risk situation in China,based on relevant public data from the past 10 years.The results show that the overall trend of China's oil risk has a“U”-shaped characteristic of first decreasing and then increasing.Furthermore,the analysis finds that China's mineral resources have been greatly influenced by the domestic production situation and international trade.These results suggest that the security of petroleum supply can be improved by safeguarding international trade in petroleum resources,strengthening the strategic reserves of domestic petroleum resources,and developing new alternative clean energy sources to improve the resilience of petroleum supply security.This study's research methodology is more logical and systematic than traditional methods,and the analysis of the factors is comprehensive and of high application value,providing implications for the establishment of a big data analysis and evaluation index system for oil resource security.展开更多
An emergency platform is an informatization support platform for disaster information perception,disaster situa-tional awareness,and emergency decision command.This is a key tool for ensuring the efficiency and effect...An emergency platform is an informatization support platform for disaster information perception,disaster situa-tional awareness,and emergency decision command.This is a key tool for ensuring the efficiency and effectiveness of emergency responses.Numerous large countries have conducted in-depth research on the key technologies of emergency platforms.Over the past 20 years,with the reform of emergency management mechanisms,China has developed two generations of emergency platform systems.This paper reviews the two generations of emergency platforms and summarizes their key technologies including multi-source-based monitoring and early warning,multi-hazard risk assessment,“scenario-response”-based decision support,synthetical forecasting based on in-cident chain,and emergency common operational picture(COP)for command and dispatch.Future research directions for the next generation emergency platforms are also proposed.展开更多
Emergency events need early detection,quick response,and accuracy recover.In the era of big data,the use of social media platforms is being popularized.Social media users can be seen as social sensors to monitor real ...Emergency events need early detection,quick response,and accuracy recover.In the era of big data,the use of social media platforms is being popularized.Social media users can be seen as social sensors to monitor real time emergency events.In this paper,a similarity-based method is proposed to early detect all kinds of emergency events in social media,including natural disasters,accidents,public health events and social security events.The method focuses on clustering social media texts based on the 3 W attribute information(What,When,and Where)of events.First,with the two-step classification,emergency related messages are detected and divided into different types from the massive and irrelevant data.Second,the time and location information are respectively extracted with the regular expression matching and the BiLSTM model.Finally,the text similarity is calculated using the type,time and location information,based on which social media texts are clustered into different events.The experiments on Sina Weibo data demonstrate the superiority of the proposed framework.Case studies on some real emergency events show the proposed framework has good performance and high timeliness.As the attribute information of events is extracted during the algorithm flow,it can be described what emergency,and when and where it happened.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2021YFF0600400)the National Natural Science Foundation of China(Grant Nos.72104123,72004113)。
文摘With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers.The Bayesian network(BN)was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies.To capture the interacting relationships among different factors,a scenario representation model of disaster chains was developed,followed by the determination of the BN structure.In deriving the conditional probability tables of the BN model,we found that,due to the lack of data and the significant uncertainty of disaster chains,parameter learning methodologies based on data or expert knowledge alone are insufficient.By integrating both sample data and expert knowledge with the maximum entropy principle,we proposed a parameter estimation algorithm under expert prior knowledge(PEUK).Taking the rainstorm disaster chain as an example,we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP)algorithm and the direct expert opinion elicitation method.The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.
基金This work was financially supported by the Fundamental Research Funds for Central Universities(Grant No.2021NTSS10)the National Natural Science Foundation of China(Grant No.72004141).
文摘With the rapid development of China's economy,external dependence on petroleum resources continues to increase,and their security has become an important part of national security.To evaluate the security of China's petroleum resource supply in a scientific and objective manner,this study establishes a corresponding petroleum life-cycle evaluation index system,based on the theory and method of the whole life-cycle security evaluation of mineral resources,and conducts further independence and grey correlation analysis on the indexes for the purpose of evaluating the petroleum risk situation in China,based on relevant public data from the past 10 years.The results show that the overall trend of China's oil risk has a“U”-shaped characteristic of first decreasing and then increasing.Furthermore,the analysis finds that China's mineral resources have been greatly influenced by the domestic production situation and international trade.These results suggest that the security of petroleum supply can be improved by safeguarding international trade in petroleum resources,strengthening the strategic reserves of domestic petroleum resources,and developing new alternative clean energy sources to improve the resilience of petroleum supply security.This study's research methodology is more logical and systematic than traditional methods,and the analysis of the factors is comprehensive and of high application value,providing implications for the establishment of a big data analysis and evaluation index system for oil resource security.
基金the China National Natural Science Foundation(Grant Nos.72104123,72004113,and 71790613).
文摘An emergency platform is an informatization support platform for disaster information perception,disaster situa-tional awareness,and emergency decision command.This is a key tool for ensuring the efficiency and effectiveness of emergency responses.Numerous large countries have conducted in-depth research on the key technologies of emergency platforms.Over the past 20 years,with the reform of emergency management mechanisms,China has developed two generations of emergency platform systems.This paper reviews the two generations of emergency platforms and summarizes their key technologies including multi-source-based monitoring and early warning,multi-hazard risk assessment,“scenario-response”-based decision support,synthetical forecasting based on in-cident chain,and emergency common operational picture(COP)for command and dispatch.Future research directions for the next generation emergency platforms are also proposed.
基金This research has been supported by the China National Key R&D Program during the 13th Five-year Plan Period(Grant No.2018YFC0807000)the China National Science Foundation for Post-doctoral Scientists(Grant No.2019M660663).
文摘Emergency events need early detection,quick response,and accuracy recover.In the era of big data,the use of social media platforms is being popularized.Social media users can be seen as social sensors to monitor real time emergency events.In this paper,a similarity-based method is proposed to early detect all kinds of emergency events in social media,including natural disasters,accidents,public health events and social security events.The method focuses on clustering social media texts based on the 3 W attribute information(What,When,and Where)of events.First,with the two-step classification,emergency related messages are detected and divided into different types from the massive and irrelevant data.Second,the time and location information are respectively extracted with the regular expression matching and the BiLSTM model.Finally,the text similarity is calculated using the type,time and location information,based on which social media texts are clustered into different events.The experiments on Sina Weibo data demonstrate the superiority of the proposed framework.Case studies on some real emergency events show the proposed framework has good performance and high timeliness.As the attribute information of events is extracted during the algorithm flow,it can be described what emergency,and when and where it happened.