IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2022i1p664-d1020415.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/664/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/1/664/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. César Benavente-Peces & Nisrine Ibadah, 2020. "Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers," Energies, MDPI, vol. 13(13), pages 1-24, July.
    2. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    3. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
    4. Awada, Hassan & Di Prima, Simone & Sirca, Costantino & Giadrossich, Filippo & Marras, Serena & Spano, Donatella & Pirastru, Mario, 2022. "A remote sensing and modeling integrated approach for constructing continuous time series of daily actual evapotranspiration," Agricultural Water Management, Elsevier, vol. 260(C).
    5. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    6. Amazirh, Abdelhakim & Merlin, Olivier & Er-Raki, Salah & Bouras, Elhoussaine & Chehbouni, Abdelghani, 2021. "Implementing a new texture-based soil evaporation reduction coefficient in the FAO dual crop coefficient method," Agricultural Water Management, Elsevier, vol. 250(C).
    7. Mohammed Olama & Jin Dong & Isha Sharma & Yaosuo Xue & Teja Kuruganti, 2020. "Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
    8. Evangelos Anastasiou & Athanasios Balafoutis & Nikoleta Darra & Vasileios Psiroukis & Aikaterini Biniari & George Xanthopoulos & Spyros Fountas, 2018. "Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes," Agriculture, MDPI, vol. 8(7), pages 1-17, June.
    9. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
    10. Ouaadi, Nadia & Jarlan, Lionel & Khabba, Saïd & Le Page, Michel & Chakir, Adnane & Er-Raki, Salah & Frison, Pierre-Louis, 2023. "Are the C-band backscattering coefficient and interferometric coherence suitable substitutes of NDVI for the monitoring of the FAO-56 crop coefficient?," Agricultural Water Management, Elsevier, vol. 282(C).
    11. Huang, Sen & Lin, Yashen & Chinde, Venkatesh & Ma, Xu & Lian, Jianming, 2021. "Simulation-based performance evaluation of model predictive control for building energy systems," Applied Energy, Elsevier, vol. 281(C).
    12. Zhao, Peng & Kang, Shaozhong & Li, Sien & Ding, Risheng & Tong, Ling & Du, Taisheng, 2018. "Seasonal variations in vineyard ET partitioning and dual crop coefficients correlate with canopy development and surface soil moisture," Agricultural Water Management, Elsevier, vol. 197(C), pages 19-33.
    13. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    14. Yang, Xiaolin & Jin, Xinnan & Chu, Qingquan & Pacenka, Steven & Steenhuis, Tammo S., 2021. "Impact of climate variation from 1965 to 2016 on cotton water requirements in North China Plain," Agricultural Water Management, Elsevier, vol. 243(C).
    15. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    16. Er-Raki, S. & Bouras, E. & Rodriguez, J.C. & Watts, C.J. & Lizarraga-Celaya, C. & Chehbouni, A., 2021. "Parameterization of the AquaCrop model for simulating table grapes growth and water productivity in an arid region of Mexico," Agricultural Water Management, Elsevier, vol. 245(C).
    17. Pal, Monalisa & Alyafi, Amr Alzouhri & Ploix, Stéphane & Reignier, Patrick & Bandyopadhyay, Sanghamitra, 2019. "Unmasking the causal relationships latent in the interplay between occupant’s actions and indoor ambience: A building energy management outlook," Applied Energy, Elsevier, vol. 238(C), pages 1452-1470.
    18. Anh Nguyen & Otto Richter & Bao V.Q. Le & Nguyen Thi Kim Phuong & Kim Chi Dinh, 2020. "Long-Term Heavy Metal Retention by Mangroves and Effect on Its Growth: A Field Inventory and Scenario Simulation," IJERPH, MDPI, vol. 17(23), pages 1-24, December.
    19. Campos, Isidro & Balbontín, Claudio & González-Piqueras, Jose & González-Dugo, Maria P. & Neale, Christopher M.U. & Calera, Alfonso, 2016. "Combining a water balance model with evapotranspiration measurements to estimate total available soil water in irrigated and rainfed vineyards," Agricultural Water Management, Elsevier, vol. 165(C), pages 141-152.
    20. Mahmoud, Shereif H. & Gan, Thian Yew, 2019. "Irrigation water management in arid regions of Middle East: Assessing spatio-temporal variation of actual evapotranspiration through remote sensing techniques and meteorological data," Agricultural Water Management, Elsevier, vol. 212(C), pages 35-47.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:664-:d:1020415. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.