Proposal of a Classification Method of Near-Miss Incidents from Disaster Response Verification Reports Using Deep Language Models

Abstract

In recent years, municipalities have increasingly utilized disaster response verification reports to enhance their disaster preparedness. These reports document near-miss incidents encountered by administrative personnel, and the collection and classification of such cases are essential for improving disaster management measures. Currently, case extraction is performed manually, followed by classification into 12 categories based on the context. However, even when limited to flood-related verification reports since 2017, there are 85 reports spanning a total of 7,053 pages, making manual extraction and classification increasingly unfeasible. Thus, a tool to support the extraction and classification of near-miss cases is in high demand. Our previous research proposed a BERT-based method for extracting near-miss incident sentences with high accuracy. This study extends that approach by classifying extracted sentences into 12 categories using BERT. The model is trained with manually labeled data. Evaluation using Top-3 accuracy shows that while performance for low-data categories remains a challenge, accuracy for high-data categories reaches a practically usable level.