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Prediction of Water Carbon Fluxes and Emission Causes in Rice Paddies Using Two Tree-Based Ensemble Algorithms

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  • Xinqin Gu

    (School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin, Shangrao 334100, China)

  • Li Yao

    (School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin, Shangrao 334100, China)

  • Lifeng Wu

    (School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin, Shangrao 334100, China
    State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

Abstract

Quantification of water carbon fluxes in rice paddies and analysis of their causes are essential for agricultural water management and carbon budgets. In this regard, two tree-based machine learning models, which are extreme gradient boosting (XGBoost) and random forest (RF), were constructed to predict evapotranspiration (ET), net ecosystem carbon exchange (NEE), and methane flux (FCH 4 ) in seven rice paddy sites. During the training process, the k-fold cross-validation algorithm by splitting the available data into multiple subsets or folds to avoid overfitting, and the XGBoost model was used to assess the importance of input factors. When predicting ET, the XGBoost model outperformed the RF model at all sites. Solar radiation was the most important input to ET predictions. Except for the KR-CRK site, the prediction for NEE was that the XGBoost models also performed better in the other six sites, and the root mean square error decreased by 0.90–11.21% compared to the RF models. Among all sites (except for the absence of net radiation (NETRAD) data at the JP-Mse site), NETRAD and normalized difference vegetation index (NDVI) performed well for predicting NEE. Air temperature, soil water content (SWC), and longwave radiation were particularly important at individual sites. Similarly, the XGBoost model was more capable of predicting FCH 4 than the RF model, except for the IT-Cas site. FCH 4 sensitivity to input factors varied from site to site. SWC, ecosystem respiration, NDVI, and soil temperature were important for FCH 4 prediction. It is proposed to use the XGBoost model to model water carbon fluxes in rice paddies.

Suggested Citation

  • Xinqin Gu & Li Yao & Lifeng Wu, 2023. "Prediction of Water Carbon Fluxes and Emission Causes in Rice Paddies Using Two Tree-Based Ensemble Algorithms," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12333-:d:1216507
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    References listed on IDEAS

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    1. Filgueiras, Roberto & Almeida, Thomé Simpliciano & Mantovani, Everardo Chartuni & Dias, Santos Henrique Brant & Fernandes-Filho, Elpídio Inácio & da Cunha, Fernando França & Venancio, Luan Peroni, 2020. "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
    2. Yash Agrawal & Manoranjan Kumar & Supriya Ananthakrishnan & Gopalakrishnan Kumarapuram, 2022. "Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1025-1042, February.
    3. Helge Bormann, 2011. "Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations," Climatic Change, Springer, vol. 104(3), pages 729-753, February.
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