Natural language processing (NLP) is a promising tool for collecting data that are usually hard to obtain during extreme weather, like community response and infrastructure performance. Patterns and trends in abundant data sources such as weather reports, news articles, and social media may provide insights into potential impacts and early warnings of impending disasters. This paper reviews the peer-reviewed studies (journals and conference proceedings) that used NLP to assess extreme weather events, focusing on heavy rainfall events. The methodology searches four databases (ScienceDirect, Web of Science, Scopus, and IEEE Xplore) for articles published in English before June 2022. The preferred reporting items for systematic reviews and meta-analysis reviews and meta-analysis guidelines were followed to select and refine the search. The method led to the identification of thirty-five studies. In this study, hurricanes, typhoons, and flooding were considered. NLP models were implemented in information extraction, topic modeling, clustering, and classification. The findings show that NLP remains underutilized in studying extreme weather events. The review demonstrated that NLP could potentially improve the usefulness of social media platforms, newspapers, and other data sources that could improve weather event assessment. In addition, NLP could generate new information that should complement data from ground-based sensors, reducing monitoring costs. Key outcomes of NLP use include improved accuracy, increased public safety, improved data collection, and enhanced decision-making are identified in the study. On the other hand, researchers must overcome data inadequacy, inaccessibility, nonrepresentative and immature NLP approaches, and computing skill requirements to use NLP properly.
Keywords: Disaster management; Extreme weather events; Natural language processing; Text mining.
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