IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7677-d1141319.html
   My bibliography  Save this article

A Review of Machine Learning Approaches to Soil Temperature Estimation

Author

Listed:
  • Mercedeh Taheri

    (Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Helene Katherine Schreiner

    (Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Abdolmajid Mohammadian

    (Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Hamidreza Shirkhani

    (National Research Council Canada, Ottawa, ON K1A 0R6, Canada)

  • Pierre Payeur

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Hanifeh Imanian

    (Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Juan Hiedra Cobo

    (National Research Council Canada, Ottawa, ON K1A 0R6, Canada)

Abstract

Soil temperature is an essential factor for agricultural, meteorological, and hydrological applications. Direct measurement, despite its high accuracy, is impractical on a large spatial scale due to the expensive and time-consuming process. On the other hand, the complex interaction between variables affecting soil temperature, such as topography and soil properties, leads to challenging estimation processes by empirical methods and physical models. Machine learning (ML) approaches gained considerable attention due to their ability to address the limitations of empirical and physical methods. These approaches are capable of estimating the variables of interest using complex nonlinear relationships with no assumptions about data distribution. However, their sensitivity to input data as well as the need for a large amount of training ground truth data limits the application of machine learning approaches. The current paper aimed to provide a review of ML techniques implemented for soil temperature modeling, their challenges, and milestones achieved in this domain.

Suggested Citation

  • Mercedeh Taheri & Helene Katherine Schreiner & Abdolmajid Mohammadian & Hamidreza Shirkhani & Pierre Payeur & Hanifeh Imanian & Juan Hiedra Cobo, 2023. "A Review of Machine Learning Approaches to Soil Temperature Estimation," Sustainability, MDPI, vol. 15(9), pages 1-26, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7677-:d:1141319
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7677/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/9/7677/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hanifeh Imanian & Juan Hiedra Cobo & Pierre Payeur & Hamidreza Shirkhani & Abdolmajid Mohammadian, 2022. "A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events," Sustainability, MDPI, vol. 14(13), pages 1-25, July.
    2. Xing, Lu & Li, Liheng & Gong, Jiakang & Ren, Chen & Liu, Jiangyan & Chen, Huanxin, 2018. "Daily soil temperatures predictions for various climates in United States using data-driven model," Energy, Elsevier, vol. 160(C), pages 430-440.
    3. Fanhua Yu & Huibowen Hao & Qingliang Li, 2021. "An Ensemble 3D Convolutional Neural Network for Spatiotemporal Soil Temperature Forecasting," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    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. Vahid Farhangmehr & Juan Hiedra Cobo & Abdolmajid Mohammadian & Pierre Payeur & Hamidreza Shirkhani & Hanifeh Imanian, 2023. "A Convolutional Neural Network Model for Soil Temperature Prediction under Ordinary and Hot Weather Conditions: Comparison with a Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    2. Hanifeh Imanian & Juan Hiedra Cobo & Pierre Payeur & Hamidreza Shirkhani & Abdolmajid Mohammadian, 2022. "A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events," Sustainability, MDPI, vol. 14(13), pages 1-25, July.
    3. Hong, Feng & Long, Dongteng & Chen, Jiyu & Gao, Mingming, 2020. "Modeling for the bed temperature 2D-interval prediction of CFB boilers based on long-short term memory network," Energy, Elsevier, vol. 194(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:jsusta:v:15:y:2023:i:9:p:7677-:d:1141319. 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.