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A Machine Learning-Based Approach to Estimate Energy Flows of the Mangrove Forest: The Case of Panama Bay

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  • Jefferson Brooks

    (Research Group in Energy and Comfort in Bioclimatic Buildings (ECEB), Faculty of Mechanical Engineering, Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama)

  • Ana Rivera

    (Research Group in Energy and Comfort in Bioclimatic Buildings (ECEB), Faculty of Mechanical Engineering, Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama)

  • Miguel Chen Austin

    (Research Group in Energy and Comfort in Bioclimatic Buildings (ECEB), Faculty of Mechanical Engineering, Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama
    Sistema Nacional de Investigación (SNI), Panama City 0816-02852, Panama
    Centro de Estudios Multidisciplinarios en Ciencias, Ingeniería y Tecnología (CEMCIT-AIP), Panama City 0819-07289, Panama)

  • Nathalia Tejedor-Flores

    (Research Group in Energy and Comfort in Bioclimatic Buildings (ECEB), Faculty of Mechanical Engineering, Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama
    Sistema Nacional de Investigación (SNI), Panama City 0816-02852, Panama
    Centro de Estudios Multidisciplinarios en Ciencias, Ingeniería y Tecnología (CEMCIT-AIP), Panama City 0819-07289, Panama
    Centro de Investigaciones Hidráulicas e Hidrotécnicas (CIHH), Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama)

Abstract

Two models were developed to simulate energy flows in a mangrove area of A. germinans and A. bicolor in the Bay of Panama, considering the importance of these areas in CO 2 fixation. The first model (black box) consisted of the use of artificial neural networks for estimation, using meteorological data and energy flows calculated by the Eddy Covariance method for model training. The second model (grey box) used the RC circuit theory, considering a non-steady state model for the flow of water from the ground to the atmosphere. A methodology was developed to reduce the uncertainty of the data collected by the sensors in the field. The black box model managed to predict the fluxes of latent heat (R 2 > 0.91), sensible heat (R 2 > 0.86), CO 2 (R 2 > 0.88), and the potential of water in the air (R 2 > 0.88) satisfactorily, while the grey box model generated R 2 values of 0.43 and 0.37, indicating that it requires further analysis regarding the structuring of the equations and parameters used. The application of the methodology to filter the data improved the effectiveness of the model during the predictions, reducing the computational capacity necessary for the resolution of the iterations.

Suggested Citation

  • Jefferson Brooks & Ana Rivera & Miguel Chen Austin & Nathalia Tejedor-Flores, 2022. "A Machine Learning-Based Approach to Estimate Energy Flows of the Mangrove Forest: The Case of Panama Bay," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:664-:d:1020415
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    References listed on IDEAS

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    1. Anh Nguyen & Bao V.Q Le & Otto Richter, 2020. "The Role of Mangroves in the Retention of Heavy Metal (Chromium): A Simulation Study in the Thi Vai River Catchment, Vietnam," IJERPH, MDPI, vol. 17(16), pages 1-22, August.
    2. Er-Raki, S. & Rodriguez, J.C. & Garatuza-Payan, J. & Watts, C.J. & Chehbouni, A., 2013. "Determination of crop evapotranspiration of table grapes in a semi-arid region of Northwest Mexico using multi-spectral vegetation index," Agricultural Water Management, Elsevier, vol. 122(C), pages 12-19.
    3. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    4. Jefferson Brooks & Miguel Chen Austin & Dafni Mora & Nathalia Tejedor-Flores, 2021. "A Critical Review on Mathematical Descriptions to Study Flux Processes and Environmental-Related Interactions of Mangroves," Sustainability, MDPI, vol. 13(12), pages 1-13, June.
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