Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest

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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Gross primary productivity
carbon flux
hybrid models
STL decomposition
LSTM
statistical models
Online Access:https://ieeexplore.ieee.org/document/10965627/
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