Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model
This work addresses forecasting challenges for decision-makers dealing with climate and public health issues in the Amazon, but it is incremental as it builds on existing decomposition and forecasting methods.
The authors tackled short-term forecasting of Amazon rainforest fires by developing a novel heterogeneous decomposition-ensemble model, which demonstrated more accurate forecasting compared to other models, though it was statistically equal to one of them.
Accurate forecasting is important for decision-makers. Recently, the Amazon rainforest is reaching record levels of the number of fires, a situation that concerns both climate and public health problems. Obtaining the desired forecasting accuracy becomes difficult and challenging. In this paper were developed a novel heterogeneous decomposition-ensemble model by using Seasonal and Trend decomposition based on Loess in combination with algorithms for short-term load forecasting multi-month-ahead, to explore temporal patterns of Amazon rainforest fires in Brazil. The results demonstrate the proposed decomposition-ensemble models can provide more accurate forecasting evaluated by performance measures. Diebold-Mariano statistical test showed the proposed models are better than other compared models, but it is statistically equal to one of them.