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arXiv · 2007.07979

Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model

Ramon Gomes da Silva·Matheus Henrique Dal Molin Ribeiro·Viviana Cocco Mariani·Leandro dos Santos Coelho
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Abstract

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.

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Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Viviana Cocco Mariani, Leandro dos Santos Coelho. 2020-07-15. Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model. https://arxiv.org/abs/2007.07979

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