Abstract
Accurate soil temperature prediction in cold climates is crucial for optimizing agricultural practices, hydrological processes, water resource management, minimizing frost damage, and mitigating flood risks. The capacity of deep learning methods to capture intricate patterns and relationships in climate data enhances the accuracy of soil temperature predictions and offers substantial benefits for reducing climate change impacts. In the present study, a comparative analysis of different deep learning techniques, including long short-term memory (LSTM), convolutional neural network (CNN), and multi-layer perceptron (MLP), for predicting the soil temperature is provided. The study examined cold climate areas across Canada, from snowy regions to Arctic conditions. Input datasets were considered both as time series and shuffled order. To comprehensively evaluate the predictive approaches for soil temperature, four machine learning (ML) models—CNN, LSTM, MLP in time series, and MLP on shuffled data—were employed. The results showed ML models using input data as time series have struggled with accurate soil temperature prediction, especially in very cold and polar climates, likely due to the presence of ice layers on the soil, limiting fluctuations near the freezing point. The normalized RMSE (NRMSE) for the CNN, LSTM, and MLP was calculated to be 8.6%. 7.4%, and 6.9%, respectively, and the scatter index (SI) for CNN, LSTM, and MLP was calculated to be 1.0%, 0.9%, and 0.9%, respectively. On the other hand, MLP-shuffled that employs shuffled input data outperformed others with an NRMSE of 5.4% and an SI of 0.7%, by creating a generalized data representation, free from presentation sequence bias. This study showed that predicting soil temperature in very cold climates poses a challenge for machine learning, yet the MLP-shuffled model excels, attaining superior accuracy through the creation of a generalized data representation independent of the sample sequence.
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Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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This research was funded by the National Research Council Canada through the Artificial Intelligence for Logistics Supercluster Support Program, grant number AI4L-120.
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by HI, AM, and VF. The first draft of the manuscript was written by HI. All authors commented on previous versions of the manuscript and all authors read and approved the final manuscript.
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Imanian, H., Mohammadian, A., Farhangmehr, V. et al. A comparative analysis of deep learning models for soil temperature prediction in cold climates. Theor Appl Climatol 155, 2571–2587 (2024). https://doi.org/10.1007/s00704-023-04781-x
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DOI: https://doi.org/10.1007/s00704-023-04781-x