Enhanced Wildfire Detection Using a Mixture of Experts Approach

With increasing global wildfire severity, effective fire detection methods are essential to mitigate widespread environmental and health impacts. A recent solution to this phenomena is the application of ensemble machine learning methods, which combine several models to create a more effective one. However, this raises several questions, notably whether an ensemble method is more effective than an individual model or if increasing the number of constituent models leads to overfitting. This paper conducts an ablation study on the Mixture of Experts (MoE) approach for forest fire detection via satellite imagery across a Canadian dataset. The model (MoE6) constitutes all six state-of-the-art architectures, including InceptionNet, ResNet, Vision Transformer (ViT), AlexNet, VGG-Net, and a baseline CNN. Experts of the MoE6 will be systematically removed to form MoE4 and MoE2, which constitute only the top four and top two performing constituent models respectively. We hypothesize that the MoE ensemble approach will outperform any constituent model (two heads are better than one). Furthermore, among the MoE architectures, we hypothesize MoE2 as the top model as it comprehensively integrates characteristics from top model architectures while mitigating overfitting. However, the results show that the original MoE6 was the top performer, achieving a peak accuracy of 93.13\% and ROC-AUC of 0.9303. This work provides a promising solution for improving wildfire detection accuracy and response times, potentially reducing the devastation caused by wildfires globally.