Mitigating class imbalance in forest fire prediction with GAN-Augmented data fusion

• This work presents a novel idea in the field of forest fire detection and addresses the critical limitations of existing bias mitigation approaches. • The proposed approach is able to handle the complex interaction of environmental factors and adapts quickly to quickly changing forest fire scenarios. • The proposed approach uses the complex relationships seen between meteorological variables, generative adversarial networks and data fusion to mitigate bias. • The proposed approach addresses comprehensive bias mitigation through the analysis of both high-level and low-level image features, which in turn significantly improve the specificity and accuracy in forest fire detection. Imbalanced data sets exacerbate recognition biases in forest fire prediction models, as disproportionate representation of class instances leads to skewed results. Existing work on bias mitigation has limited ability to generalize and extract features specific to forest fires. Internet of Things (IoT)-based sensor networks can provide real-time, granular data on environmental factors such as temperature, humidity, and soil moisture, helping to capture the dynamic nature of forest conditions and alleviate data imbalance. To address these challenges, this work introduces a novel hybrid approach that explores complex probabilistic relationships among environmental factors, incorporating IoT-driven data, and using a generative adversarial network (GAN) to synthetically augment minority classes. The proposed model is validated on publicly available datasets, and the performance is reported on evaluation metrics such as accuracy, precision, recall, F1-score, computational efficiency and training cost. The results show that the proposed hybrid model is able to achieve a significant improvement over the exiting methods achieving classification accuracy of 95.08%, a precision of 93.03%, a recall of 92.80%, and an F1-score of 92.91%.