A Comparative Exploration of Time Series Models for Wild Fire Prediction

Wildfires pose a great threat to human safety and property arising from both natural and human causes. According to a technical assessment by the Forest Survey of India more than 95% of fires are of anthropogenic origin. Wildfires erupt due to burning of fossils by local communities for crop rotation, camp fires without proper supervision etc. Climate change further elevates the risk by fostering dry conditions. Traditionally, wildfire prediction relied on statistical models and expert judgment. However, the emergence of Machine Learning (ML) and Deep Learning (DL) techniques has significantly improved the accuracy of forest fire prediction. The objective of this work is to prevent wildfires and save the ecosystem. In this work, LightGBM(Light Gradient Boosting Machine) and LSTM(Long Short-Term Memory) machine learning models are utilized to predict the forest fire. Both models exhibit high F1 scores of 97% and 95% accuracy in forest fire prediction, enabling the development of reliable prediction systems. The results of these ML-based models may aid in identifying highrisk areas, optimizing prevention measures, refining evacuation plans, and guiding firefighting efforts.