Canada Wildfire Next-Day Spread Prediction Tools Using Deep Learning

Abstract

Wildfires have become a pressing issue globally, with their increasing frequency and intensity causing significant environmental, economic, and human impacts. Traditional wildfire prediction methods, while useful, often fall short in time complexity or simulation on heterogeneous landscapes. This thesis explores the application of deep learning models, especially convolutional networks, to improve the accuracy and reliability of wildfire spread predictions. By leveraging advanced machine learning techniques, this research aims to enhance the current prediction capabilities and provide better tools for Canadian wildfire management and mitigation.

Utilizing a comprehensive dataset from various sources, this thesis integrates multiple features such as weather data, vegetation types, and topographical information. The research introduces a novel module for fusing multi-modal data, which enhances the performance of U-shape deep learning models like U-Net. Additionally, an innovative U-shape network structure with atrous(dilated) convolution and new attention implementation was developed to further improve prediction accuracy. The thesis also proposes an enhancement method that amplifies grouped error pixels for element-wise error computation for model training. The novel data fusion module proposed in this thesis has been proven to improve the baseline model on the F1 score, while the new model I suggest outperformed the baseline model and its two variants on the same metric. In the final part of the thesis I proposed various additional enhancement methods to improve performance further, it has shown its statistical significance under certain conditions when applied to BCELoss.

By enhancing the predictive capabilities of wildfire spread models, this thesis offers valuable insights for emergency responders and policymakers, aiding in better resource allocation and risk mitigation strategies. The deep learning methodologies developed in this study are versatile and have potential applications in other fields requiring spatial data predictions, such as intelligent healthcare, flood forecasting, and disease spread modelling.