FF-BERT: A BERT-based ensemble for automated classification of web-based text on flash flood events

Abstract

The web is a rich information repository that can be mined to uncover additional data about past flash flood (FF) events, currently missing from existing structured databases. However, this information originates from multiple sources (news articles, government records, and weather records among others) and may cover several topics. Furthermore, these topics may be disproportionately covered on the web. The large size and heterogenous nature of web information render manual review difficult. To address this challenge, we have developed a multi-label text classification model, FF-BERT. FF-BERT is designed to classify FF-related web paragraphs into one or more of seven categories: (1) Damage and Economic Impact (DI), (2) Fatalities, Injuries, and Rescue (FIR), (3) Hydrometeorology (HM), (4) Warning and Emergency (WE), (5) Response and Recovery (RR), (6) Public Health (PH), and (7) Mitigation (MG). To develop FF-BERT, we labeled 21,180 paragraphs from FF-related webpages and performed experiments with multiple model architectures based on the widely used language model Bidirectional Encoder Representation from Transformers (BERT). Our final model outperforms the baseline by 11.83%, as measured by the micro-F1 score. In addition, FF-BERT significantly improves the prediction of minority labels (RR-32.1%, PH-260.4%, and MG-138.6%). We demonstrate using real world examples that FF-BERT can be used to uncover new information about flash flood events. This information can be used to enhance existing databases, such as NOAA’s Storm Events Database.