Advanced machine learning schemes for prediction CO2 flux based experimental approach in underground coal fire areas

INTRODUCTION: flux in underground coal fire areas. OBJECTIVES: flux prediction in coal fire areas and inform environmental monitoring and management strategies. METHODS: flux. Different machine learning algorithms, including Natural gradient boosting regression (NGRB), Extreme gradient boosting (XGboost), Light gradient boosting (LGRB), and random forest (RF), are evaluated and compared for their predictive capabilities. The models are trained, optimized, and assessed using appropriate performance metrics. RESULTS: emissions from underground coal fires, contributing to global efforts in combating climate change.