Machine Learning Approaches for One-Day Ahead Soil Temperature Forecasting

Year 2023, Volume: 29 Issue: 1, 221 - 238, 31.01.2023

Mehmet Bilgili , Şaban Ünal , Aliihsan Şekertekin , Cahit Gürlek

https://doi.org/10.15832/ankutbd.997567

Cited By: 2

Abstract

Present study investigates the capabilities of six distinct machine learning techniques such as ANFIS network with fuzzy c-means (ANFIS-FCM), grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), feed-forward neural network (FNN), Elman neural network (ENN), and long short-term memory (LSTM) neural network in one-day ahead soil temperature (ST) forecasting. For this aim, daily ST data gathered at three different depths of 5 cm, 50 cm, and 100 cm from the Sivas meteorological observation station in the Central Anatolia Region of Turkey was used as training and testing datasets. Forecasting values of the machine learning models were compared with actual data by assessing with respect to four statistic metrics such as the mean absolute error, root mean square error (RMSE), Nash−Sutcliffe efficiency coefficient, and correlation coefficient (R). The results showed that the ANFIS-FCM, ANFIS-GP, ANFIS-SC, ENN, FNN and LSTM models presented satisfactory performance in modeling daily ST at all depths, with RMSE values ranging 0.0637-1.3276, 0.0634-1.3809, 0.0643-1.3280, 0.0635-1.3186, 0.0635-1.3281, and 0.0983-1.3256 °C, and R values ranging 0.9910-0.9999, 0.9903-0.9999, 0.9910-0.9999, 0.9911-0.9999, 0.9910-0.9999 and 0.9910-0.9998 °C, respectively.

Keywords

ANFIS, Daily soil temperature, LSTM, Elman neural network (ENN), Feed-forward neural network

References

• Araghi A, Mousavi‐Baygi M, Adamowski J, Martinez C, Van der Ploeg, M (2017) Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network. Met Apps 24:603-611. https://doi.org/10.1002/met.1661.
• Araghi A, Adamowski J, Martinez CJ, Olesen JE (2019) Projections of future soil temperature in northeast Iran. Geoderma 349:11-24. https://doi.org/10.1016/j.geoderma.2019.04.034.
• Benmouiza K, Cheknane A (2019) Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theor Appl Climatol 137:31–43. https://doi.org/10.1007/s00704-018-2576-4.
• Cai Q, Yan B, Su B, Liu S, Xiang M, Wen Y, Cheng Y, Feng N (2020) Short-term load forecasting method based on deep neural network with sample weights. Int Trans Electr Energy Syst 30:e12340. https://doi.org/10.1002/2050-7038.12340.
• Chen S, Mao J, Chen F, Hou P, Li Y (2018) Development of ANN model for depth prediction of vertical ground heat exchanger. International Journal of Heat and Mass Transfer 117:617-626. https://doi.org/10.1016/j.ijheatmasstransfer.2017.10.006.
• Cho MY, Chang JM, Huang CC (2020) Application of parallel Elman neural network to hourly area solar PV plant generation estimation. Int Trans Electr Energy Syst 30:e12470. https://doi.org/10.1002/2050-7038.12470.
• Citakoglu H (2017) Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Theor Appl Climatol 130:545-556. https://doi.org/10.1007/s00704-016-1914-7.
• Feng Y, Cui N, Hao W, Gao L, Gong D (2019) Estimation of soil temperature from meteorological data using different machine learning models. Geoderma 338:67–77. https://doi.org/10.1016/j.geoderma.2018.11.044.
• Gang W, Wang J, Wang S (2014) Performance analysis of hybrid ground source heat pump systems based on ANN predictive control. Applied Energy 136:1138-1144. https://doi.org/10.1016/j.apenergy.2014.04.005.
• George RK (2001) Prediction of soil temperature by using artificial neural networks algorithms. Non-linear Analysis: Theory, Methods & Applications 47:1737-1748. https://doi.org/10.1016/S0362-546X(01)00306-6.
• Gill J, Singh S (2015) An efficient neural networks based genetic algorithm model for soil temperature prediction. International Journal of Emerging Technologies in Engineering Research (IJETER) 3:1-5.
• Hao H, Yu F, Li Q (2021) Soil temperature prediction using convolutional neural network based on ensemble empirical mode decomposition. IEEE Access 9:4084-4096. https://.doi.org/10.1109/ACCESS.2020.3048028.
• Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computation. 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
• Inyurt S, Sekertekin A (2019) Modeling and predicting seasonal ionospheric variations in Turkey using artificial neural network (ANN). Astrophys Space Sci. https://doi.org/10.1007/s10509-019-3545-9.
• Jang JSR (1993) ANFIS: Adaptive-network-based fuzzy ınference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541.
• Karakuş O, Kuruoǧlu EE, Altınkaya MA (2017) One-day ahead wind speed/power prediction based on polynomial autoregressive model. IET Renew Power Gener 11:1430–1439. https://doi.org/10.1049/iet-rpg.2016.0972.
• Kayaci N, Demir H (2018) Numerical modelling of transient soil temperature distribution for horizontal ground heat exchanger of ground source heat pump. Geothermics 73:33-47. https://doi.org/10.1016/j.geothermics.2018.01.009.
• Li C, Zhang Y, Ren X (2020) Modeling hourly soil temperature using deep BiLSTM neural network. Algorithms. https://doi.org/10.3390/a13070173.
• Mathworks (2020a) Multilayer Shallow Neural Network Architecture. https://www.mathworks.com/help/deeplearning/ug/multilayer-neural-network-architecture.html. Accessed 17 May 2020
• Mathworks (2020b) Elman Networks. http://matlab.izmiran.ru/help/toolbox/nnet/recur94.html. Accessed 17 May 2020
• Mathworks (2020c) Long Short-Term Memory Networks. Accessed. https://www.mathworks.com/help/deeplearning/ug/long-short-term-memory-networks.html. Accessed 17 May 2020
• Mehdizadeh S, Behmanesh J, Khalili K (2017) Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data. Environ Earth Sci 76:325. https://doi.org/10.1007/s12665-017-6607-8.
• Mehdizadeh S, Ahmadi F, Sales AK (2020) Modelling daily soil temperature at different depths via the classical and hybrid models. Meteorol Appl 27:e1941. https://doi.org/10.1002/met.1941.
• Naranjo-Mendoza C, Wright AJ, Oyinlola MA, Greenough RM (2018) A comparison of analytical and numerical model predictions of shallow soil temperature variation with experimental measurements. Geothermics 76:38-49. https://doi.org/10.1016/j.geothermics.2018.06.003.
• Penghui L, Ewees AA, Beyaztas BH, Qi C, Salih SQ, Al-Ansari N, Bhagat SK, Yaseen ZM, Singh VP (2020) Metaheuristic optimization algorithms hybridized with artificial intelligence model for soil temperature prediction: Novel Model. IEEE Access 8:51884-51904. https://doi.org/10.1109/ACCESS.2020.2979822.
• Piotrowski AP, Napiorkowski MJ, Napiorkowski JJ, Osuch M (2015) Comparing various artificial neural network types for water temperature prediction in rivers. J Hydrol 529:302–315. https://doi.org/10.1016/j.jhydrol.2015.07.044.
• Salman AG, Heryadi Y, Abdurahman E, Suparta W (2018) Single layer & multi-layer long short-term memory (LTSM) model with intermediate variables for weather forecasting. Procedia Computer Sci 135:89–98. https://doi.org/10.1016/j.procs.2018.08.153.
• Samadianfard S, Asadi E, Jarhan S, Kazemi H, Kheshtgar S, Kisi O, Sajjadi S, Manaf AA (2018) Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths. Soil and Tillage Research 175:37-50. https://doi.org/10.1016/j.still.2017.08.012.
• Sattari MT, Avram A, Apaydin H, Matei O (2020) Soil temperature estimation with meteorological parameters by using tree-based hybrid data mining models. Mathematics. https://doi.org/10.3390/math8091407.
• Shahabi, M., Khojastehpour, M., & Sadrnia, H. (2021). Production and Evaluation of Agricultural Biodegradable Mulch through Heat and Moisture Distribution in Soil. Journal of Agricultural Sciences.
• Shamshirband S, Esmaeilbeiki F, Zarehaghi D, Neyshabouri M, Samadianfard S, Ghorbani MA, Mosavi A, Nabipour N, Chau KW (2020) Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths. Engineering Applications of Computational Fluid Mechanics 14:939-953. https://doi.org/10.1080/19942060.2020.1788644.
• Singhal M, Gairola AC, Singh N (2021) Artificial neural network-assisted glacier forefield soil temperature retrieval from temperature measurements. Theoretical and Applied Climatology 143:1157-1166. https://doi.org/10.1007/s00704-020-03498-5.
• Stajkowski S, Kumar D, Samui P, Bonakdari H, Gharabaghi B (2020) Genetic-algorithm-optimized sequential model for water temperature prediction. Sustainability 12:5374. https://doi.org/10.3390/su12135374.
• Tabari H, Kisi O, Ezani A, Talaee PH (2012) SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. J Hydrol 444–445: 78–89. https://doi.org/10.1016/j.jhydrol.2012.04.007.
• Xing L, Li L, Gong J, Ren C, Liu J, Chen H (2018) Daily soil temperatures predictions for various climates in United States using data-driven model. Energy 160:430-440. https://doi.org/10.1016/j.energy.2018.07.004.
• Xu C, Qu JJ, Hao X, Zhu Z, Gutenberg L (2020) Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements. Int. J. Appl. Earth Obs. Geoinformation 91:102156. https://doi.org/10.1016/j.jag.2020.102156.
• Yan L, Hu P, Li C, Yao Y, Xing L, Lei F, Zhu N (2016) The performance prediction of ground source heat pump system based on monitoring data and data mining technology. Energy and Buildings 127:1085-1095. https://doi.org/10.1016/j.enbuild.2016.06.055.
• Zahroh S, Hidayat Y, Pontoh RS (2019) Modeling and forecasting daily temperature in Bandung. In: Paper Presented at Proc. Int. Conf. Ind. Eng. Oper. Manag., Riyadh, Saudi Arabia.
• Zeynoddin M, Bonakdari H, Ebtehaj I, Esmaeilbeiki F, Gharabaghi B, Haghi DZ (2019) A reliable linear stochastic daily soil temperature forecast model. Soil & Tillage Research 189:73-87. https://doi.org/10.1016/j.still.2018.12.023.
• Zeynoddin M, Ebtehaj I, Bonakdari H (2020) Development of a linear based stochastic model for daily soil temperature prediction: One step forward to sustainable agriculture. Computers and Electronics in Agriculture 176:105636. https://doi.org/10.1016/j.compag.2020.105636.
• Zhou S, Chu X, Cao S, Liu X, Zhou Y (2020) Prediction of the ground temperature with ANN, LS-SVM and fuzzy LS-SVM for GSHP application. Geothermics 84:101757. https://doi.org/10.1016/j.geothermics.2019.101757.