IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i6p1322-d331642.html
   My bibliography  Save this article

Understanding and Modeling Climate Impacts on Photosynthetic Dynamics with FLUXNET Data and Neural Networks

Author

Listed:
  • Nanyan Zhu

    (Biological Sciences, Columbia University, New York City, NY 10027, USA)

  • Chen Liu

    (Electrical Engineering, Columbia University, New York City, NY 10027, USA)

  • Andrew F. Laine

    (Biomedical Engineering, and Radiology, Columbia University, New York City, NY 10027, USA)

  • Jia Guo

    (Psychiatry, and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, NY 10027, USA)

Abstract

Global warming, which largely results from excessive carbon emission, has become an increasingly heated international issue due to its ever-detereorating trend and the profound consequences. Plants sequester a large amount of atmospheric CO 2 via photosynthesis, thus greatly mediating global warming. In this study, we aim to model the temporal dynamics of photosynthesis for two different vegetation types to further understand the controlling factors of photosynthesis machinery. We experimented with a feedforward neural network that does not utilize past histories, as well as two networks that integrate past and present information, long short-term memory and transformer. Our results showed that one single climate driver, shortwave radiation, carries the most information with respect to prediction of upcoming photosynthetic activities. We also demonstrated that photosynthesis and its interactions with climate drivers, such as temperature, precipitation, radiation, and vapor pressure deficit, has an internal system memory of about two weeks. Thus, the predictive model could be best trained with historical data over the past two weeks and could best predict temporal evolution of photosynthesis two weeks into the future.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1322-:d:331642
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/6/1322/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/6/1322/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. ZhenHua Li & Yujie Zhang & Ahmed Abu-Siada & Xingxin Chen & Zhenxing Li & Yanchun Xu & Lei Zhang & Yue Tong, 2021. "Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network," Energies, MDPI, vol. 14(6), pages 1-14, March.
    2. Fernando Sánchez Lasheras, 2021. "Predicting the Future-Big Data and Machine Learning," Energies, MDPI, vol. 14(23), pages 1-2, December.

    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. Kadukothanahally Nagaraju Shivaprakash & Niraj Swami & Sagar Mysorekar & Roshni Arora & Aditya Gangadharan & Karishma Vohra & Madegowda Jadeyegowda & Joseph M. Kiesecker, 2022. "Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    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. 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).
    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. 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).

    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:jeners:v:13:y:2020:i:6:p:1322-:d:331642. 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.