A Study on the Development of Wind Disaster Impact Model Based on Weather GIS Big Data

Le résumé fourni par la source

The frequency and intensity of natural disasters, including high winds, floods, and droughts, are being significantly altered by climate change. This increase in activity not only causes immediate damage but also increases the long-term socioeconomic risks associated with these extreme events. As a result, the field of impact-based forecasting has emerged as an important area of research. In contrast to traditional weather forecasting, which is primarily concerned with meteorological elements such as temperature, precipitation, and wind speed, impact-based forecasting aims to predict the potential socioeconomic impacts of weather events in order to better prepare for and mitigate risks. Currently, the majority of research on impact forecasting is focused on major natural disasters such as floods and typhoons, which can cause significant damage to infrastructure and communities. However, there is still a significant gap in research on the impact of high winds on vehicles, particularly in the transportation sector. Although damage in this sector is generally low compared to more catastrophic events, the increasing frequency of high winds due to climate change requires a deeper understanding and assessment of the risks to vehicle safety. In response to this need, we developed three specific indices to assess the risk to vehicles in high wind conditions. These indices were carefully calculated and analysed to identify areas vulnerable to wind-related vehicle accidents. The results effectively recreated areas where these accidents have occurred in the past, confirming the usefulness of the indices in real-world scenarios. Through this research, we hope to lay the groundwork for an objective, data-driven assessment of the risks that high winds pose to drivers, which can better inform policy decisions, improve driver safety measures, and ultimately reduce the number of road accidents and fatalities caused by severe weather. AcknowledgementThis research was support by a (2022-MOIS63-002(RS-2022-ND641012)) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea).

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