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Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

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  • Xianming Dou

    (Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process of Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China
    School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Yongguo Yang

    (Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process of Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China
    School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Jinhui Luo

    (Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process of Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China
    School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination ( R 2 ), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R 2 = 0.93, NSE = 0.91, Bias = 0.11 g C m −2 day −1 and RMSE = 1.04 g C m −2 day −1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m −2 day −1 and 0.72 g C m −2 day −1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m −2 day −1 and 0.89 g C m −2 day −1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.

Suggested Citation

  • Xianming Dou & Yongguo Yang & Jinhui Luo, 2018. "Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements," Sustainability, MDPI, vol. 10(1), pages 1-26, January.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:203-:d:127011
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    2. Cai, Jianchao & Xu, Kai & Zhu, Yanhui & Hu, Fang & Li, Liuhuan, 2020. "Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest," Applied Energy, Elsevier, vol. 262(C).
    3. Nanyan Zhu & Chen Liu & Andrew F. Laine & Jia Guo, 2020. "Understanding and Modeling Climate Impacts on Photosynthetic Dynamics with FLUXNET Data and Neural Networks," Energies, MDPI, vol. 13(6), pages 1-11, March.
    4. Jasna Petković & Nataša Petrović & Ivana Dragović & Kristina Stanojević & Jelena Andreja Radaković & Tatjana Borojević & Mirjana Kljajić Borštnar, 2019. "Youth and forecasting of sustainable development pillars: An adaptive neuro-fuzzy inference system approach," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-25, June.
    5. Guo, Hui & Zhou, Xiao & Dong, Yi & Wang, Yahui & Li, Sien, 2023. "On the use of machine learning methods to improve the estimation of gross primary productivity of maize field with drip irrigation," Ecological Modelling, Elsevier, vol. 476(C).
    6. Abbasian, Hassan & Solgi, Eisa & Mohsen Hosseini, Seyed & Hossein Kia, Seyed, 2022. "Modeling terrestrial net ecosystem exchange using machine learning techniques based on flux tower measurements," Ecological Modelling, Elsevier, vol. 466(C).

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