Mitigating Class Imbalance in Forest Fire Prediction with GAN-Augmented Data Fusion

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 %.