On September 30, Li Nan’s research group held the 11th session of its series of invited academic lectures. The activity was hosted by Dr. LI Nan, and Dr. CHEN Zhenhua from the Ohio State University, the United States, was invited to present an academic report on the Evaluation of Disaster Economic Impact, Management of Disaster Risks and Assessment of Resilience Measures.
Dr. CHEN delivered an indepth report on the research methods for the Evaluation of Disaster Economic Impact, Management of Disaster Risks and Assessment of Resilience Measures. The report was divided into three parts. In the first part, the reduced-form CGE approach was introduced using a case study of a civil aviation transportation system. In the second part, a new disaster emergency management and assessment approach based on a combination of disaster risks management and resilience assessment measures was introduced. In the third part, the application of big data and machine learning in predicting the service level of a traffic and transportation system was introduced, which can provide auxiliary support for emergency response of disasters and other emergencies.
In recent years, all kinds of emergencies caused by natural and man-made factors have had a great impact on air transport. Under different disaster scenarios, efficient allocation of resources and rapid recovery of the economic system is a major challenge faced by policymakers and researchers. In the existing literature, considerable progress has been done in this area, however, most of the studies only focus on a single event/disaster type and research method, which leaves a gap in knowledge concerning the dynamic response to multi-hazards. Moereover, a large amount of literature often only considers the impact of the disaster itself in the process of analysis and attach little importance to the resilience of the economic system and its impact. Therefore, Dr. Chen’s research aims to address the above limitations and achieve rapid cross-hazard assessment.
Biulding upon the above background, Dr. CHEN designed a process to analyze the direct damage of disasters based on reliable data and a series of important indicators and parameters. Using uncertainty analysis methods, he took into account the different features of each scenario and proposed a simplified analysis method based on the computable general equilibrium model to realize the rapid assessment of disaster economic loss. Based on the research content above, Dr. CHEN developed a rapid economic impact assessment tool (E-CAT) for multiple disasters, whose interface is shown in Picture 1. In this study, the differences between disasters and the impact of human response factors are taken into full consideration, the complex assessment process is simplified and a rapid econimc impact assessment tool is provided for decision-makers.
Picture 1 A Rapid Disaster Economic Impact Assessment Tool Proposed by Dr. CHEN (E-CAT)
In the second part, the management of disaster risks and assessment of resilience measures were combined and tested in a case study of the sea ice disaster in Bohai Sea, China. Sea ice disaster can cause sea water surface freezing and and seriously affect sea surface operation and normal port operation. Historical data show that sea ice has a significant negative impact on port throughput. In the existing studies, there is a lack of connection between disaster risks and economic system resilience. Therefore, Dr. CHEN led his team to establish joint functions based on Copula theory, select disaster factors, conduct risk analysis of sea ice disasters, and define resilience measures. Then, based on the responses at differnet levels of risks (resilience measures), he simulated and compared the results of the economic losses with and without consideration of resilience measures. The results indicated that when the Gumbel Copula function was combined with two disaster factors (ice thickness and ice quantity), the predictive results were the most accurate. In addition, resilience measures can significantly reduce the impact of disasters. In this study, disaster risk management and resilience measures evaluation are innovatively combined, which provides a new idea for related research.
Picture 2 Sea Ice Disaster Risk Analysis and Resilience Assessment
In the third part, a predictive study was carried out on the service levels of transport systems under extreme weather conditions. In recent years, various extreme weather events occurred frequently, which posed great challenges to the normal operation of transportation system. However, the existing studies usually only focus on a specific mode of transportation and the impact of extreme weather on transportation is often only considered in two cases: yes/no. As a result, Dr. CHEN led his team to apply big data and machine learning methods to predict the service level of civil aviation and high-speed rail transportation systems based on a large number of data samples. By comparing the two modes of transportation, the research team found that the punctuality rate of high-speed rail was more accurate than that of civil aviation. In addition to weather factors, some factors of the system itself would also have a significant impact on the overall system function level. For civil aviation transportation, departure/arrival time was the key factor of punctuality prediction, while for high-speed rail, efficient scheduling was the key factor to predict punctuality. This research improves the understanding of the impact of disasters on transportation systems and provides a theoretical support for dynamic management and control of transportation systems.
Picture 3 Comparison of Simulation Effects of Different Machine Learning Algorithms
Dr. Chen pointed out that the integration of resilience measures to achieve more efficient disaster response and disaster prevention and mitigation will be an important part of future research. Researchers and disaster risk management personnel should be fully aware of the importance of resilience measures.
In the Q&A session, Dr. CHEN briefly explained how inconsistencies between multi-source data in case studies were treated, and pointed out the necessity of multi-party cooperative research. In addition, with regard to the uncertainty in the evaluation of disaster economic impact and resilience measures, Dr. Chen Zhenhua believes that there is still room for improvement. In terms of the impact of weather factors on punctuality, Dr. CHEN pointed out that the impact of weather factors was not very big and some human factors, such as China's traffic controls, can also had a huge impact on civil aviation. The accuracy of the assessment would be further improved if more potential influencing factors can be included in the assessment.
At the end of the seminar, Dr. CHEN shared some of his experiences in scientific research. Dr. Chen believes that it takes stamina to do academic work, and it is very important to have firm convictions. Graduate students should learn to expand their resources, flexibly adjust research ideas, and strive to achieve the unity of long-term macro research goals and current research goals. In addition, young scholars should pay attention to maintaining coherence between research topics, and establish connections between different parts of their research to form individual research systems while broadening their own research work.
