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A Convolutional Neural Network Model for Soil Temperature Prediction under Ordinary and Hot Weather Conditions: Comparison with a Multilayer Perceptron Model

Author

Listed:
  • Vahid Farhangmehr

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

  • Juan Hiedra Cobo

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

  • Abdolmajid Mohammadian

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

  • Pierre Payeur

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

  • Hamidreza Shirkhani

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

  • Hanifeh Imanian

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

Abstract

Soil temperature is a critical parameter in soil science, agriculture, meteorology, hydrology, and water resources engineering, and its accurate and cost-effective determination and prediction are very important. Machine learning models are widely employed for surface, near-surface, and subsurface soil temperature predictions. The present study employed a properly designed one-dimensional convolutional neural network model to predict the hourly soil temperature at a subsurface depth of 0–7 cm. The annual input dataset for this model included eight hourly climatic features. The performance of this model was assessed using a wide range of evaluation metrics and compared to that of a multilayer perceptron model. A detailed sensitivity analysis was conducted on each feature to determine its importance in predicting the soil temperature. This analysis showed that air temperature had the greatest impact and surface thermal radiation had the least impact on soil temperature prediction. It was concluded that the one-dimensional convolutional model performed better than the multilayer perceptron model in predicting the soil temperature under both normal and hot weather conditions. The findings of this study demonstrated the capability of the model to predict the daily maximum soil temperature.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7897-:d:1144819
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    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. 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.
    3. Xike Zhang & Qiuwen Zhang & Gui Zhang & Zhiping Nie & Zifan Gui & Huafei Que, 2018. "A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition," IJERPH, MDPI, vol. 15(5), pages 1-23, May.
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    Cited by:

    1. Sebastián Vázquez-Ramírez & Miguel Torres-Ruiz & Rolando Quintero & Kwok Tai Chui & Carlos Guzmán Sánchez-Mejorada, 2023. "An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model," Mathematics, MDPI, vol. 11(14), pages 1-26, July.

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