Forest Fire Prediction Framework Based on Machine Learning Models

Abstract

: Forest fires pose a recurring and severe threat to forest ecosystems, biodiversity, and human communities across India, particularly during the dry season. Traditional methods for predicting and monitoring forest fires are often constrained by their reliance on manual observations and limited historical datasets, leading to sub optimal accuracy and untimely interventions. Leveraging recent advances in remote sensing and data science, this study develops a comprehensive machine learning framework for the prediction of forest fires in India using state-wise fire detection data from the SNPP-VIIRS satellite, supplemented with environmental and meteorological features. Multiple regression, classification, and time series forecasting models were implemented to estimate fire counts, categorize states by risk, extract key predictors, and anticipate future fire trends. Experimental results indicate that ensemble machine learning methods, specifically Gradient Boosting and Random Forest, significantly outperform traditional linear approaches, achieving high predictive accuracy (R² = 0.91 for regression; 91% accuracy for classification). Seasonal analysis revealed that meteorological variables, notably temperature and rainfall, are dominant drivers of fire risk, with fire incidents peaking during the pre-monsoon period. The study's findings provide interpretable, actionable insights for policymakers and resource managers, enabling targeted early warning systems and improved allocation of fire prevention resources. This framework demonstrates the potential of integrating satellite data and machine learning to advance forest fire prediction and supports sustainable risk mitigation strategies across the Indian landscape.