Impact of Jet Stream Features on Forecast of Burned Areas in India Using Machine Learning During The South Asian Heatwave of 2022

Jet streams’ persistent tropospheric ridging plays a crucial role in temperature extremes, heatwaves, and consequent wildfires in subtropical and polar regions. To address this, the research presented in this study incorporates jet stream variables into atmospheric data to enhance the forecast of burned areas using machine learning (ML). Focusing on the anomalous early and intense summer weather in South Asia during April and May 2022, this research employed ML algorithms such as Random Forest, Support Vector Regression (SVR), Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), and Neural Network (NN). Notably, the XGBoost model outperformed others, and its accuracy improved by approximately 11.5% (with R
²
scores rising from 0.61 to 0.68) when jet stream features were included, which emphasized their importance. These findings highlight the importance of both natural and anthropogenic factors, including upper tropospheric patterns, in predicting burned areas.