Since the 1953 truce, the Republic of Korea Army (ROKA) has regularly conducted artillery training, posing a risk of wildfires - a threat to both the environment and the public perception of national defense. To assess this risk and aid decision-making within the ROKA, we built a predictive model of wildfires triggered by artillery training. To this end, we combined the ROKA dataset with meteorological database. Given the infrequent occurrence of wildfires (imbalance ratio 1:24 in our dataset), achieving balanced detection of wildfire occurrences and non-occurrences is challenging. Our approach combines a weighted support vector machine with a Gaussian mixture-based oversampling, effectively penalizing misclassification of the wildfires. Applied to our dataset, our method outperforms traditional algorithms (G-mean=0.864, sensitivity=0.956, specificity= 0.781), indicating balanced detection. This study not only helps reduce wildfires during artillery trainings but also provides a practical wildfire prediction method for similar climates worldwide.