Candidate: Ursula Das
Date: January 13, 2025
Time: 2:30pm
Location: Online (via Teams)
Supervisor: Dr. Sagar Naik
All are welcome!
Abstract:
Wildfires annually cause substantial economic and environmental losses, as well as a detrimental impact on human lives and health due to the release of their harmful byproducts. Moreover, wildfire incidents have exhibited an alarming surge in frequency as well as severity in recent years due to increased urbanization near forested areas coupled with climate change, highlighting the need for advanced technologies to predict wildfire behavior in advance and mitigate its impact. In recent years, the enormous strides in machine learning research coupled with the increased availability of wildfire data through various sources such as remote sensing and the increased availability of computational resources have fueled the rise of data-driven approaches across all stages of wildfire management. Despite the growing adoption of machine learning-driven approaches in wildfire mitigation, the primary focus has been on analyzing historical patterns and identifying the causes leading to wildfire patterns rather than predicting wildfire behavior. The prediction of wildfire behavior over time, such as the burned area has been largely underexplored. This study aims to address this gap by advancing data-driven methods for predicting wildfire behavior during the active fire stage and aiding in resource allocation efforts.
This study adopts a Graph Neural Network based framework for predicting the burned area resulting from a wildfire ignition. While CNN-based architectures have been widely employed to model wildfire behavior, including spread prediction, as a semantic segmentation task, these architectures impose specific limitations on geospatial data due to their reliance on fixed-size inputs and local receptive fields. Graph Neural Network (GNNs), have shown success in capturing the long-range dependencies and irregular-sized inputs inherent in geospatial data, such as wildfires, making them a viable alternative to CNNs. To this end, a GNN-based approach is adopted to model wildfire burned area prediction. A framework is developed to represent spatial wildfire data and its influencing factors as homogeneous graphs followed by the development of three distinct GNN models based on different message-passing mechanisms to process the graph-structured data. The results obtained through various experiments illustrate the efficacy of Graph Neural Networks in modeling wildfire behavior by effectively capturing spatial dependencies, improving prediction accuracy, and providing insights into the key factors influencing the extent of the burned area resulting from wildfires. These findings underscore the potential of Graph Neural Networks (GNNs) as a powerful tool for wildfire behavior modeling and supporting resource allocation initiatives.