Information Retrieval and Classification of Real-Time Multi-Source Hurricane Evacuation Notices
This provides real-time data for government agencies and news media during hurricanes, with potential applications to other disasters, though it is incremental as it applies existing NLP techniques to a new domain.
The study tackled the challenge of tracking rapidly issued and updated hurricane evacuation notices from multiple local authorities by developing a method to detect and classify them using web scraping and deep learning, achieving 96% recall for mandatory notices.
For an approaching disaster, the tracking of time-sensitive critical information such as hurricane evacuation notices is challenging in the United States. These notices are issued and distributed rapidly by numerous local authorities that may spread across multiple states. They often undergo frequent updates and are distributed through diverse online portals lacking standard formats. In this study, we developed an approach to timely detect and track the locally issued hurricane evacuation notices. The text data were collected mainly with a spatially targeted web scraping method. They were manually labeled and then classified using natural language processing techniques with deep learning models. The classification of mandatory evacuation notices achieved a high accuracy (recall = 96%). We used Hurricane Ian (2022) to illustrate how real-time evacuation notices extracted from local government sources could be redistributed with a Web GIS system. Our method applied to future hurricanes provides live data for situation awareness to higher-level government agencies and news media. The archived data helps scholars to study government responses toward weather warnings and individual behaviors influenced by evacuation history. The framework may be applied to other types of disasters for rapid and targeted retrieval, classification, redistribution, and archiving of real-time government orders and notifications.