Available at: https://digitalcommons.calpoly.edu/theses/2721
Date of Award
12-2022
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Maria Pantoja
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, employing ensemble modeling reduces bias and variation, leading to more consistent and accurate predictions. When inferencing on wildfire propagation at 30-minute intervals, the architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1%.
Included in
Artificial Intelligence and Robotics Commons, Computational Engineering Commons, Software Engineering Commons