Version 1
: Received: 3 February 2022 / Approved: 7 February 2022 / Online: 7 February 2022 (16:26:00 CET)
Version 2
: Received: 14 February 2022 / Approved: 17 February 2022 / Online: 17 February 2022 (09:56:27 CET)
How to cite:
Imanian, H.; Hiedra Cobo, J.; Payeur, P.; Shirkhani, H.; Mohammadian, A. A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction. Preprints2022, 2022020101. https://doi.org/10.20944/preprints202202.0101.v2
Imanian, H.; Hiedra Cobo, J.; Payeur, P.; Shirkhani, H.; Mohammadian, A. A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction. Preprints 2022, 2022020101. https://doi.org/10.20944/preprints202202.0101.v2
Imanian, H.; Hiedra Cobo, J.; Payeur, P.; Shirkhani, H.; Mohammadian, A. A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction. Preprints2022, 2022020101. https://doi.org/10.20944/preprints202202.0101.v2
APA Style
Imanian, H., Hiedra Cobo, J., Payeur, P., Shirkhani, H., & Mohammadian, A. (2022). A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction. Preprints. https://doi.org/10.20944/preprints202202.0101.v2
Chicago/Turabian Style
Imanian, H., Hamidreza Shirkhani and Abdolmajid Mohammadian. 2022 "A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction" Preprints. https://doi.org/10.20944/preprints202202.0101.v2
Abstract
Soil temperature is a fundamental parameter in water resources and engineering. A cost-effective model which can forecast soil temperature accurately is extensively needed. Recently, many studies have applied artificial intelligence (AI) at both surface and underground levels for soil temperature prediction. However, there is no comprehensive and detailed assessment of the performance of different AI approaches in soil temperature estimation, and primarily limited atmospheric variables are used as input data for AI models. In the present study, great varieties of various land and atmospheric variables are applied to evaluate the performance of a wide range of AI methods on soil temperature prediction. Herein, thirteen approaches, from classic regressions to well-established methods of random forest and gradient boosting to advanced AI techniques like multi-layer perceptron and deep learning are taken into account. The results show that AI is a promising approach in climate parameter forecast and deep learning demonstrates the best performance among other models. It has the highest R-squared ranging from 0.957 to 0.980, the lowest NRMSE ranging from 2.237% to 3.287% and the lowest MAE, ranging from 0.510 to 0.743 in predicting soil temperature. The prediction is repeated for different sizes of data, and prediction outcomes confirm the conclusion mentioned above.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Hanifeh Imanian
Commenter's Conflict of Interests: Author