Urban cities heatwaves vulnerability and societal responses towards hazard zoning: Social media real-time based heatwave detection using deep learning

Abstract

Study region

Pakistan.

Study focus

Enhanced social media data accessibility facilitates the effective detection of disaster-associated information, exemplified by urban heatwaves. Social media elucidates human behavior and geographical trends in Sindh Province, Pakistan, amidst intensifying extreme heat events. The utilization of this data facilitates a comprehensive examination of heatwaves, thereby enhancing the real-time management and mitigation strategies within urban environments.

Innovative insights

This study introduces an innovative framework for the real-time extraction and analysis of heatwave-related data from Facebook in areas impacted by disasters. This framework employs topic modeling and transfer learning for examining heatwave disaster dynamics. It extracts temperature and humidity information from both textual content and visual representation using state-of-the-art deep learning techniques and fuses these insights via decision-making processes. A fine-grained location corpus specific to urban heatwaves was developed using a named entity recognition (NER) model. The BERT-BiLSTM-CRF model was then utilized to extract heatwave data accurately. In the case of southeastern Pakistan, the extracted heatwave points aligned with 86 % of officially documented incidents, predominantly occurring near urban areas. This framework improves situational awareness and supports real-time spatiotemporal analyses of the urban heatwaves, thus providing a new approach for effective disaster management and response.