Spatial prediction of forest fires in India: a machine learning approach for improved risk assessment and early warning systems

Forest fires pose a significant ecological and environmental threat globally, and India has seen a marked increase in both the frequency and severity of these events in recent years. This has led to extensive damage to natural resources, including forests and wildlife habitats. Effective management strategies are essential to mitigate these impacts, and this study aims to contribute to that effort through spatial prediction of forest fires in India using machine learning techniques. The research begins by analyzing spatial patterns and trends of forest fires and identifying key factors contributing to their occurrence. It then explores the relationships between forest fires and these factors. Using data from 2001 to 2020, the study develops a probability map of forest fire occurrences in India, leveraging data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on fire points and burned areas and the European Space Agency Climate Change Initiative (ESA-CCI) land use land cover. The analysis incorporates various fire occurrence conditioning factors, including climate variables (precipitation, air temperature, specific humidity, near-surface wind speed, net longwave radiation, and land surface temperature for both daytime and nighttime), biophysical factors (aspect, slope, elevation, soil moisture, and Normalized Difference Vegetation Index), and other factors such as proximity to railways, roads, and waterways. The maximum entropy model (MaxEnt) was employed to identify these factors and generate the probability map. The results indicate that forest fires are predominantly concentrated in three major regions: Northeast India, Uttarakhand-Himachal, and the Deccan Plateau. The study also highlights the link between forest fires and Sustainable Development Goals (SDGs 1, 2, 3, 5, 6, 9, 12, 13, and 15). The findings can inform the identification of vulnerable areas, enabling the implementation of preventative measures. This research is crucial as it not only pinpoints regions at risk of forest fires but also elucidates their impacts on the environment and local livelihoods, thereby enhancing risk assessment and early warning systems.