Application of Explainable Artificial Intelligence in Predicting Wildfire Spread: An ASPP-Enabled CNN Approach

Abstract

Forest ecosystems have been persistently affected by wildfires, leading to significant damage worldwide. The severity and frequency of wildfires have escalated in recent years, necessitating more effective prediction models. This study presents an application of convolutional neural networks (CNNs) for wildfire spread prediction, focusing on the use of atrous spatial pyramid pooling (ASPP) mechanisms in these networks. However, the black-box nature of these algorithms has not been fully explored. To bridge this gap, we proposed an explainable CNN model with an ASPP mechanism (CNN-ASPP) in this study. More specifically, we utilize the Next Day Wildfire Spread dataset, which includes environmental variables, to evaluate the performance of our model. The proposed model is compared with state-of-the-art machine learning (ML) methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and another CNN model. Our results showed that CNN-ASPP achieved an F1-score of 97%, outperforming the ML methods with an F1-score of 90% for a neighborhood size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\times 7$ </tex-math></inline-formula>. We also opened the black box and tried to explanation different convolutional layers based on the gradient-weighted class activation mapping (Grad-CAM) algorithm. Our findings indicate that larger dilation rates (DRs) can extract more meaningful features from the input data. This study contributes to the development of more transparent and accurate models for wildfire spread prediction, which could have significant implications for forest management and wildfire prevention strategies.

Topics & Concepts