Predicting Forest Fire Hotspots with Carbon Emission Insights Using Random Forest and Gradient Boosting Regression - palupi, irma; wahyudi, bambang ari; AL Mamuda, Naila; Shabrina, Ayu (International Journal on Information and Communication Technology (IJoICT), 2023)
This research paper focuses on predicting the dispersion of carbon emissions, a crucial indicator for identifying potential forest fire hotspots in the wooded regions of Sumatra Island, Indonesia. Forest fires, often triggered by extended periods of dry weather, result in significant environmental degradation, impacting both the ecosystem and the economy. Furthermore, health concerns arise from smoke inhalation, leading to respiratory problems. To achieve this predictive capability, we harnessed valuable datasets, including GFED4.1s for carbon emissions and ERA5 for historical climate indicators, spanning from 1998 to 2022. Employing supervised learning ensemble methods, specifically Random Forest Regression (RFR) and Gradient Boosting Regression (GBR), we sought to forecast carbon emissions. It is noteworthy that our predictions encompassed carbon emission values from 1998 to 2023, providing insights into recent trends. Our analysis showed that GBR did better than RFR in terms of evaluation metrics, with a root mean square error (RMSE) of 10.87 and a mean absolute error (MAE) of 2.91. This was done by carefully tuning the hyperparameters. Additionally, our study highlighted that precipitation, temperature, and humidity were the primary climate factors influencing carbon emission values.