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Advanced machine learning model for better prediction accuracy of soil temperature at different depths

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Listed:
  • Meysam Alizamir
  • Ozgur Kisi
  • Ali Najah Ahmed
  • Cihan Mert
  • Chow Ming Fai
  • Sungwon Kim
  • Nam Won Kim
  • Ahmed El-Shafie

Abstract

Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models’ outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

Suggested Citation

  • Meysam Alizamir & Ozgur Kisi & Ali Najah Ahmed & Cihan Mert & Chow Ming Fai & Sungwon Kim & Nam Won Kim & Ahmed El-Shafie, 2020. "Advanced machine learning model for better prediction accuracy of soil temperature at different depths," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-25, April.
    • Handle: RePEc:plo:pone00:0231055
    DOI: 10.1371/journal.pone.0231055
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    References listed on IDEAS

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    1. Alizamir, Meysam & Kim, Sungwon & Kisi, Ozgur & Zounemat-Kermani, Mohammad, 2020. "A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions," Energy, Elsevier, vol. 197(C).
    2. Ozgur Kisi & Meysam Alizamir & Mohammad Zounemat-Kermani, 2017. "Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 367-381, May.
    3. Zhang, Donghai & Gao, Penghui & Zhou, Yang & Wang, Yijiang & Zhou, Guoqing, 2020. "An experimental and numerical investigation on temperature profile of underground soil in the process of heat storage," Renewable Energy, Elsevier, vol. 148(C), pages 1-21.
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    Cited by:

    1. Jung Ryeol Park & Yituo Feng, 2023. "Trajectory tracking of changes digital divide prediction factors in the elderly through machine learning," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-20, February.

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