Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest
This study presents a hybrid model, STL-ARIMA-ES-LSTM, developed to improve the accuracy of Gross Primary Productivity (GPP) forecasts in the Amazon region. The model integrates seasonal and trend decomposition using Loess (STL) with statistical methods (ARIMA and Exponential Smoothing-ES) and a mac...
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| Format: | Article |
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10965627/ |
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| author | Jean A. C. Dias Pedro H. Do V. Guimaraes Williane G. S. Pereira Leonardo De O. Tamasauskas Marivan S. Gomes Alan B. S. Correa Karla Figueiredo Gilson Costa Gabriel Brito Costa Fernando A. R. Costa Marcos C. Da R. Seruffo |
| author_facet | Jean A. C. Dias Pedro H. Do V. Guimaraes Williane G. S. Pereira Leonardo De O. Tamasauskas Marivan S. Gomes Alan B. S. Correa Karla Figueiredo Gilson Costa Gabriel Brito Costa Fernando A. R. Costa Marcos C. Da R. Seruffo |
| author_sort | Jean A. C. Dias |
| collection | DOAJ |
| description | This study presents a hybrid model, STL-ARIMA-ES-LSTM, developed to improve the accuracy of Gross Primary Productivity (GPP) forecasts in the Amazon region. The model integrates seasonal and trend decomposition using Loess (STL) with statistical methods (ARIMA and Exponential Smoothing-ES) and a machine learning technique (Long Short-Term Memory - LSTM). Applied to GPP data from the PE-QFR site, the hybrid model achieved significantly better error metrics, with RMSE of 1.69 gC/m2/day, MAE of 1.35 gC/m2/day, and MAPE of 0.20%, compared to the standalone LSTM (RMSE of 2.16 gC/m2/day, MAE of 1.78 gC/m2/day, and MAPE of 0.27%). Furthermore, the hybrid model showed stronger agreement with the observed data, with correlation coefficient r =0.62 and <inline-formula> <tex-math notation="LaTeX">${R}^{2} =0.39$ </tex-math></inline-formula>, whereas the LSTM alone yielded r =0.26 and <inline-formula> <tex-math notation="LaTeX">${R}^{2} =$ </tex-math></inline-formula> –0.002. The STL decomposition allowed effective separation of trend, seasonality, and residual components, enabling tailored modeling of each, which contributed to the improved predictive performance. These results demonstrate the advantage of hybrid approaches in capturing the nonlinear and seasonal patterns of GPP, supporting enhanced environmental monitoring and more informed climate change mitigation strategies in the Amazon. |
| format | Article |
| id | doaj-art-c40cdd06bf814b44804b0dfc4219fe3b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c40cdd06bf814b44804b0dfc4219fe3b2025-08-20T01:53:26ZengIEEEIEEE Access2169-35362025-01-0113847138472610.1109/ACCESS.2025.356116610965627Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon ForestJean A. C. Dias0https://orcid.org/0009-0005-7663-8072Pedro H. Do V. Guimaraes1https://orcid.org/0009-0006-2350-2451Williane G. S. Pereira2https://orcid.org/0009-0005-0611-2462Leonardo De O. Tamasauskas3Marivan S. Gomes4Alan B. S. Correa5https://orcid.org/0009-0002-9590-8347Karla Figueiredo6https://orcid.org/0000-0001-8420-3937Gilson Costa7https://orcid.org/0000-0001-7341-9118Gabriel Brito Costa8https://orcid.org/0000-0002-5254-489XFernando A. R. Costa9https://orcid.org/0000-0002-0226-7505Marcos C. Da R. Seruffo10https://orcid.org/0000-0002-8106-0560Operational Research Laboratory, Federal University of Pará (UFPA), Belém, BrazilOperational Research Laboratory, Federal University of Pará (UFPA), Belém, BrazilOperational Research Laboratory, Federal University of Pará (UFPA), Belém, BrazilOperational Research Laboratory, Federal University of Pará (UFPA), Belém, BrazilSchool of Technology, State University of Amazonas (UEA), Amazonas, BrazilOperational Research Laboratory, Federal University of Pará (UFPA), Belém, BrazilDepartment of Informatics and Computer Science, Institute of Mathematics and Statistics, Rio de Janeiro State University (UERJ), Rio de Janeiro, BrazilDepartment of Genetics, Institute of Biological and Health Sciences II, Rio de Janeiro State University (UERJ), Rio de Janeiro, BrazilPost-Graduate Program in Natural Resources of the Amazon (PPGRNA), Federal University of Western Pará (UFOPA), Santarém, BrazilOperational Research Laboratory, Federal University of Pará (UFPA), Belém, BrazilOperational Research Laboratory, Federal University of Pará (UFPA), Belém, BrazilThis study presents a hybrid model, STL-ARIMA-ES-LSTM, developed to improve the accuracy of Gross Primary Productivity (GPP) forecasts in the Amazon region. The model integrates seasonal and trend decomposition using Loess (STL) with statistical methods (ARIMA and Exponential Smoothing-ES) and a machine learning technique (Long Short-Term Memory - LSTM). Applied to GPP data from the PE-QFR site, the hybrid model achieved significantly better error metrics, with RMSE of 1.69 gC/m2/day, MAE of 1.35 gC/m2/day, and MAPE of 0.20%, compared to the standalone LSTM (RMSE of 2.16 gC/m2/day, MAE of 1.78 gC/m2/day, and MAPE of 0.27%). Furthermore, the hybrid model showed stronger agreement with the observed data, with correlation coefficient r =0.62 and <inline-formula> <tex-math notation="LaTeX">${R}^{2} =0.39$ </tex-math></inline-formula>, whereas the LSTM alone yielded r =0.26 and <inline-formula> <tex-math notation="LaTeX">${R}^{2} =$ </tex-math></inline-formula> –0.002. The STL decomposition allowed effective separation of trend, seasonality, and residual components, enabling tailored modeling of each, which contributed to the improved predictive performance. These results demonstrate the advantage of hybrid approaches in capturing the nonlinear and seasonal patterns of GPP, supporting enhanced environmental monitoring and more informed climate change mitigation strategies in the Amazon.https://ieeexplore.ieee.org/document/10965627/Gross primary productivitycarbon fluxhybrid modelsSTL decompositionLSTMstatistical models |
| spellingShingle | Jean A. C. Dias Pedro H. Do V. Guimaraes Williane G. S. Pereira Leonardo De O. Tamasauskas Marivan S. Gomes Alan B. S. Correa Karla Figueiredo Gilson Costa Gabriel Brito Costa Fernando A. R. Costa Marcos C. Da R. Seruffo Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest IEEE Access Gross primary productivity carbon flux hybrid models STL decomposition LSTM statistical models |
| title | Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest |
| title_full | Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest |
| title_fullStr | Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest |
| title_full_unstemmed | Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest |
| title_short | Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest |
| title_sort | enhanced carbon flux forecasting via stl decomposition and hybrid arima es lstm model in amazon forest |
| topic | Gross primary productivity carbon flux hybrid models STL decomposition LSTM statistical models |
| url | https://ieeexplore.ieee.org/document/10965627/ |
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