Wildfires Classification in Canadian Boreal Forest: A Comparative Study of Logistic Regression and XGBoost Models

Authors

• Brandon Tran, Chapman University
• Elijah James Duran, Chapman University
• Mike Luu, Chapman University
• Hesham Morgan, Chapman University
• Surendra Maharjan, Chapman UniversityFollow
• Wenzhao Li, Chapman UniversityFollow
• Hesham el-Askary, Chapman UniversityFollow

Document Type

Conference Proceeding

Publication Date

8-2025

Abstract

In recent years, Canada has faced a growing number of wildfires. These events have devastated ecosystems, displaced communities, and posed severe health risks. To minimize the damage caused by such disasters, this study aims to develop an early warning system that predicts wildfire occurrences. Two machine learning models for binary classification of wildfire occurrence in Canadian wild forests, Logistic regression and XGBoost, will be compared and evaluated. The models are used to predict the likelihood of wildfire events based on various environmental and climatic factors. The models are evaluated using a 70-30 split validation approach and their performance is assessed through confusion matrices. Our research contributes to the understanding and prediction of wildfire events.

Comments

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium in 2025. This article may not exactly replicate the final published version. The definitive publisher-authenticated version is available online at https://doi.org/10.1109/IGARSS55030.2025.11243375.

Recommended Citation

B. Tran et al., "Wildfires Classification in Canadian Boreal Forest: A Comparative Study of Logistic Regression and XGBoost Models," IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium, Brisbane, Australia, 2025, pp. 3116-3121, https://doi.org/10.1109/IGARSS55030.2025.11243375.

Copyright

© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.