Forest Fire Detection Using Ensemble Deep Learning Model With XAi

The incidence of forest fires seems to be rising and causing damages to the environment and properties, as well as loss of lives. The high rate of forest fires therefore requires effective early detection systems to address the catastrophic challenges the disasters pose. Traditional methods of fire detection, which rely heavily on direct visual observation systems and conventional sensor devices, are often slow and inadequate. This study introduces an intelligent detection system that integrates Deep learning with Explainable AI (XAI) to improve the early identification of forest fires. We developed an ensemble model integrating ResNet50V2 and InceptionV3 architectures. This study uses explainer techniques from XAI like LIME and Grad-CAM to ensure that the results meet compliance and performance standards as well as to reveal possible biases and flaws in the model’s decision-making process. The findings indicate that incorporating an ensemble model significantly improves detection by achieving a high accuracy of 98%. It also showed better results regarding distinguishing between fire and no-fire conditions. Based on the approaches outlined in this paper, including XAI for model interpretability means that advanced deep learning models can be reliable and more effective even for real-world applications in forest fire detection.