Machine Learning and Data Assimilation for Accurate Wildfire Predictions and Estimations.

Twitter is increasingly being used as a real-time human-sensor network during natural disasters, detecting, tracking and documenting these events. Social media data represents a large amount of publicly available, unprocessed social data which is both opinionated, informative, and emotional. During disaster scenarios, users post a geographically and emotionally subjective account of events unfolding locally. There is scope for this information to be collated and analysed in real-time, and incorporated into wildfire models to improve their accuracy. 

This PhD project aims to help make disaster management teams make more informed, data driven decisions by including social media analysis as a data source in real-time wildfire models. In doing this, we aim to create more socially conscious wildfire models, which consider the impacts of wildfire spread.

Project duration: 2020-2024