Modeling soil temperature using air temperature features in diverse climatic conditions with complementary machine learning models

Bayatvarkeshi, Maryam and Bhagat, Suraj Kumar and Mohammadi, Kourosh and Kisi, Ozgur and Farahani, M. and Hasani, A. and Deo, Ravinesh ORCID: https://orcid.org/0000-0002-2290-6749 and Yaseen, Zaher Mundher (2021) Modeling soil temperature using air temperature features in diverse climatic conditions with complementary machine learning models. Computers and Electronics in Agriculture, 185:106158. ISSN 0168-1699


Abstract

Soil temperature (ST) is an essential catchment property strongly influenced by air temperature (Ta). ST is also the key factor in sustainable agricultural developments, so researchers are still motivated to develop robust machine learning (ML) models to predict ST more reliably. Four different ML models, utilizing the standalone algorithms (i.e., artificial neural networks: ‘ANN’ and co-active neuro-fuzzy inference systems: ‘CANFIS’) and complementary algorithms (i.e., wavelet transformation combined with ANN: ‘WANN’ and wavelet transformation combined with CANFIS: ‘WCANFIS’) were developed to predict the ST at six meteorological stations incorporating a wide range of climatic features to improve the overall performance. The study has utilized data over the period 2000–2010, collected at 12 locations in Iran. In the first phase of this research, the effects of climate variability on the changes in ST at different depths (i.e., 5, 10, 20, 30, 50 and 100 cm) were explored using air temperature as the exploratory and ST as the response variable. Assessing the performance of the predictive models used in ST prediction, the results indicated good predictive capability of the WCANFIS model, thus, advocating its potential utility in ST prediction problems, especially over diverse climatic regions. This study has also ascertained that the minimum and the maximum predictive errors were encountered at a depth of about 20 cm and 100 cm, respectively. The assessment of climatic features based on air temperature datasets on the performance of the models indicated the highest efficacy demonstrated by the ANN model for the case A–C–W climate type (i.e., a moist climate regime: Arid, temperature regime in winter: Cool, and temperature regime in summer: Warm), in comparison with the PH–C–W climate type (moist regime: Per-humid) for the other best ML models (i.e., WANN, WCANFIS and CANFIS). The order of the model accuracies based on the root mean square error (RMSE) can be ranked with error values of as: WCANFIS = 0.43 °C, ANN = 0.69 °C, CANFIS = 2.16 °C and WANN = 2.31 °C, demonstrating the wavelet-based CANFIS model to exceed the performance of the counterpart comparative models. The present study provides evidence of successfully developing new ML models, improved through wavelet transform for effective feature extraction, and the importance of such hybrid models that have practical implications in studying soil temperature based on air temperature feature inputs in diverse climatic conditions. The outcomes of this study are expected to support key decisions in sustainable agriculture and other related areas where soil health, based on air temperature changes, needs to be monitored or predicted.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 04 May 2021 04:37
Last Modified: 04 May 2021 04:37
Uncontrolled Keywords: agricultural sustainability; air temperature; artificial neural networks; co-active neuro-fuzzy inference systems; soil temperature; machine learning
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070101 Agricultural Land Management
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300207 Agricultural systems analysis and modelling
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: https://doi.org/10.1016/j.compag.2021.106158
URI: http://eprints.usq.edu.au/id/eprint/41924

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