Automated extraction and summarization of wind disaster data using deep learning models, with extended applications to seismic events

The United States experiences more extreme wind events than any other country due to its diverse climate and geographical features. While these events pose significant threats to society, they generate substantial data that can support researchers and disaster managers in resilience planning. This research leverages such data to develop a framework that automates the extraction and summarization of structural and community damage information from reconnaissance reports. The framework utilizes the large Bidirectional and Auto-Regressive Transformers model (BART-large), a deep learning model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) and Cable News Network (CNN) Daily Mail datasets for these tasks. Specifically, the BART-large MNLI model employs zero-shot text classification to identify sentences containing relevant impact information based on user-defined keywords, minimizing the need for fine-tuning the model on wind damage-related datasets. Subsequently, the BART-large CNN model generates comprehensive summaries from these sentences, detailing structural and community damage. The performance of the framework is assessed using reconnaissance reports published by the Structural Extreme Events Reconnaissance (StEER), part of the Natural Hazards Engineering Research Infrastructure (NHERI) network. Particularly, the initial evaluation is conducted with the 2022 Hurricane Ian report. This is followed by a verification of the BART-large MNLI model’s capability to extract impact sentences, utilizing the 2023 Hurricane Otis report. Finally, the versatility of the framework is illustrated through an extended application to the 2023 Türkiye earthquake sequences report, highlighting its adaptability across diverse disaster contexts.