Abirami S and Chitra P, 2020. Energy-efficient edge based real-time healthcare support system. Advances in Computers 117(1): 339-368.
Alizamir M, Kis O, Ahmed AN, Mert C, Fai CM, Kim S, Kin N and El-Shafie A, 2020. Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS One 15(4), 0231055.
Asadi L, Hezarjaribi A, Ghorbani K, Zakernia M and Agha Shariatmadari Z, 2014. Estimating soil temperature using modern methods of data analysis. Iranian Journal of Irrigation and Drainage 8(1): 145-152 (in Persian with English abstract).
Bilgili M, 2010. Prediction of soil temperature using regression and artificial neural network models. Meteorology and Atmospheric Physics 110: 59-70.
Børresen MH, Barnes DL and Rike AG, 2007. Repeated freeze–thaw cycles and their effects on mineralization of hexadecane and phenanthrene in cold climate soils. Cold Regions Science and Technology 49(3): 215-225.
Boser BE, Guyon IM and Vapnik VN, 1992. A training algorithm for optimal margin classifiers. Pp.144-152. In: Haussler D, (Ed.), 5th Annual ACM Workshop on COLT, Pittsburgh.
Brooks PD, McKnight D and Elder K, 2005. Carbon limitation of soil respiration under winter snowpacks: potential feedbacks between growing season and winter carbon fluxes. Global Change Biology 11(2): 231-238.
Carranza C, Nolet C, Pezij M and van der Ploeg M, 2021. Root zone soil moisture estimation with Random Forest. Journal of Hydrology 593, 125840.
Das LC, Zhang Z and Crabbe MJC, 2023. Optimization of data-driven soil temperature forecast-The first model in Bangladesh. Applied Sciences 13(23): 12616.
Delbari M, Sharifazari S and Mohammadi E, 2019. Modeling daily soil temperature over diverse climate conditions in Iran-a comparison of multiple linear regression and support vector regression techniques. Theoretical and Applied Climatology 135 (3-4): 991–1001.
Emeksiz C and Demir G, 2018. An investigation of the effect of meteorological parameters on wind speed estimation using bagging algorithm. International Journal of Intelligent Systems and Applications in Engineering 6(4): 311-321.
Elsayed S, Gupta M, Chaudhary G, Taneja S, Gaur H, Gad M, Eid MH, Kovács A, Péter S, Gaagai A and Schmidhalter U, 2023. Interpretation the influence of hydrometeorological variables on soil temperature prediction using the potential of deep learning model. Knowledge-Based Engineering and Sciences 4(1): 55-77.
Farasat M, Seyedian M and Daab K, 2021. Evaporation modeling of free surface water using SVM and LSSVM models. Journal of Irrigation and Water Engineering 11(3): 272-288 (in Persian with English abstract).
Feng Y, Cui N, Hao W, Gao L and Gong D, 2019. Estimation of soil temperature from meteorological data using different machine learning models. Geoderma 338 (2019): 67–77.
Gill MK, Asefa T, Kemblowski MW and McKee M, 2006. Soil moisture prediction using support vector machines 1. JAWRA Journal of the American Water Resources Association 42(4): 1033-1046.
Hao L, Yang C and Li X, 2023. Prediction of soil temperature field in panax notoginseng plough layer based on PSO-LSTM neural network. 6th International Symposium on Autonomous Systems (ISAS), Nanjing, China. IEEE.
Huang R, Huang JX, Zhang C, Wen ZHUO, Chen YY, Zhu DH, Qingling WU and Mansaray LR, 2020. Soil temperature estimation at different depths, using remotely-sensed data. Journal of Integrative Agriculture 19(1): 277-290.
Li HJ, Yan JX, Yue XF and Wang MB, 2008. Significance of soil temperature and moisture for soil respiration in a Chinese mountain area. Agricultural and Forest Meteorology 148(3): 490-503.
Li Q, Zhu Y, Shangguan W, Wang X, Li L and Yu F, 2022. An attention-aware LSTM model for soil moisture and soil temperature prediction. Geoderma 409, 115651.
Meikle RW and Gilchrist AJ, (1983). A mathematical method for estimation of soil temperatures in England and Scotland. Agricultural Meteorology 30(3): 221-225.
Mihalakakou G, 2002. On estimating soil surface temperature profiles. Energy and Buil 34: 251-259.
Moazenzadeh R and Mohammadi B, 2019. Assessment of bio-inspired metaheuristic optimisation algorithms for estimating soil temperature. Geoderma 353: 152–171.
Mohammadi B, 2022. Use of new methods to determine the inputs effective in estimating soil temperature. Nivar 46(118): 25-36.
Pal M and Deswal S, 2010. Modelling pile capacity using Gaussian process regression. Computers and Geotechnics 37: 942-947.
Quinlan JR, 1992. Learning with continuous classes. Pp.343-348. 5th Australian Joint Conference on Artificial Intelligence, Hobart, Australia.
Rabet A, Dastranj A, Asadi S and Asadi Nalivan O, 2021. Determination of groundwater potential using artificial neural network, random forest, support vector machine and linear regression models (Case study: Lake Urmia watershed). Ecohydrology 7(4): 1047-1060 (in Persian with English abstract).
Sabziparvar AA, Tabari H and Aeini A, 2010. Estimation of mean daily soil temperature by means of meteorological data in some selected climates of Iran. JWSS-Isfahan University of Technology 14(52): 125-138 (in Persian with English abstract).
Sabziparvar AA, Zare Abyaneh H and Bayat Varkeshi M, 2010. A model comparison between predicted soil temperatures using ANFIS model and regression methods in three different climates. Journal of Water and Soil 24(2): 274–285 (in Persian with English abstract).
Samadianfard S, Ghorbani MA and Mohammadi B, 2018. Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm. Information Processing in Agriculture 5(4): 465-476.
Sanikhani H, Deo RC, Yaseen ZM, Eray O and Kisi O, 2018. Non-tuned data intelligent model for soil temperature estimation: A new approach. Geoderma 330: 52-64.
Schimel JP, Bilbrough C and Welker JM, 2004. Increased snow depth affects microbial activity and nitrogen mineralization in two Arctic tundra communities. Soil Biology and Biochemistry 36(2): 217-227.
Schmidt L, Heße F, Attinger S and Kumar R, 2020. Challenges in applying machine learning models for hydrological inference: A case study for flooding events across Germany. Water Resources Research 56(5): 025924.
Seyfried M, Harris R, Marks D and Jacob B, 2001. Geographic database, Reynolds Creek Experimental Watershed, Idaho, United States. Water Resources Research 37(11): 2825-2829.
Sihag P, Mohsenzadeh Karimi S and Angelaki A, 2019. Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity. Applied Water Science 9: 1-9.
Wahyunggoro O, Permanasari AE and Chamsudin A, 2013. Utilization of neural network for disease forecasting. 59th ISI World Statistics Congress, Hong Kong.
Wu X, Yao Z, BrŘggemann N, Shen ZY, Wolf B, Dannenmann M, Zheng X and Butterbach-Bahl K, 2010. Effects of soil moisture and temperature on CO2 and CH4 soil–atmosphere exchange of various land use/cover types in a semi-arid grassland in Inner Mongolia, China. Soil Biology and Biochemistry 42(5): 773-787.
Yin X and Arp PA, 1993. Predicting forest soil temperatures from monthly air temperature and precipitation records. Canadian Journal of Forest Research 23(12): 2521-2536.
Zeng L, Hu S, Xiang D, Zhang X, Li D, Li L and Zhang T, 2019. Multilayer soil moisture mapping at a regional scale from multisource data via a machine learning method. Remote Sensing 11 (3): 284.
Zhang Z and Li J, 2019. Big Data Mining for Climate Change, Elsevier, Amsterdam, The Netherlands.
Zounemat-Kermani M, 2013. Hydrometeorological parameters in prediction of soil temperature by means of artificial neural network: Case study in Wyoming. Journal of Hydrologic Engineering 18(6): 707-718.
)