FireDetXplainer: Decoding Wildfire Detection With Transparency and Explainable AI Insights
Syeda Fiza Rubab, Arslan Abdul Ghaffar, Gyu Sang Choi
Abstract
Recent analyses by leading national wildfire and emergency monitoring agencies have highlighted an alarming trend: the impact of wildfire devastation has escalated to nearly three times that of a decade ago. To address this challenge, we propose FireDetXplainer (FDX), a robust deep-learning model that enhances the interpretability often lacking in current solutions. FDX employs an innovative approach, combining transfer learning and fine-tuning methodologies with the Learning without Forgetting (LwF) framework. A key aspect of our methodology is the utilization of the pre-trained MobileNetV3 model, renowned for its efficiency in image classification tasks. Through strategic adaptation and augmentation, we have achieved an exceptional classification accuracy of 99.91%. The model is further refined with convolutional blocks and advanced image pre-processing techniques, contributing to this high level of precision. Leveraging diverse datasets from Kaggle and Mendeley, FireDetXplainer incorporates Explainable AI (XAI) tools such as Gradient Weighted Class Activation Map (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME) for comprehensive result interpretation. Our extensive experimental results demonstrate that FireDetXplainer not only outperforms existing state-of-the-art models but does so with remarkable accuracy, making it a highly effective solution for interpretable image classification in wildfire management.