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

Vahid Farhangmehr (), Juan Hiedra Cobo, Abdolmajid Mohammadian, Pierre Payeur, Hamidreza Shirkhani and Hanifeh Imanian
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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

Sustainability, 2023, vol. 15, issue 10, 1-21

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.

Keywords: machine learning; convolutional neural network; multilayer perceptron; soil temperature prediction; time-series regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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