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Prediction of the CO2 emission across grassland and cropland using tower-based eddy covariance flux measurements: a machine learning approach

Author

Listed:
  • Simin Kheradmand

    (Kharazmi University)

  • Nima Heidarzadeh

    (Kharazmi University)

  • Seyed Hossein Kia

    (University of Southampton)

Abstract

In this research, the magnitude of the net ecosystem exchange (NEE) for both grasslands (GRA) and croplands (CRO) is estimated by different machine learning approaches (MLAs). There are two main goals including prediction/data gap filling of the NEE and developing a new MLA model. The variation of CO2 is affected by soil temperature and meteorological factors, including air temperature, latent heat, and sensible heat considered as inputs. Hourly data of three AmeriFlux sites have been collected for seven years. The normalized smoothed data are applied for modeling. Both artificial neural network (ANN) and genetic algorithm (GA) are the computational MLAs working by deep learning methods. In this study, a new GA-based model named integration of optimization with genetic algorithm and Fourier series (IOGAFS) was proposed for estimation of the NEE. The results show the IOGAFS and ANN methods have acceptable performance with 0.86 and 0.88 determination coefficient for CRO and 0.75 and 0.81 for GRA, respectively. Due to high performance of both methods, they can be used estimation of the NEE in similar ecosystems, mainly where there are no flux towers.

Suggested Citation

  • Simin Kheradmand & Nima Heidarzadeh & Seyed Hossein Kia, 2023. "Prediction of the CO2 emission across grassland and cropland using tower-based eddy covariance flux measurements: a machine learning approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(6), pages 5495-5509, June.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:6:d:10.1007_s10668-022-02276-9
    DOI: 10.1007/s10668-022-02276-9
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    References listed on IDEAS

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    1. Suvajit Banerjee, 2019. "Addressing the Drivers of Carbon Emissions Embodied in Indian Exports: An Index Decomposition Analysis," Foreign Trade Review, , vol. 54(4), pages 300-333, November.
    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).
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