Ajayram, K. A., Jegadeeshwaran, R., Sakthivel, G., Sivakumar, R. and Patange, A. D. (2021). Condition monitoring of carbide and non-carbide coated tool insert using decision tree and random tree – A statistical learning. Mater. Today: Proceedings.,
DOI: 10.1016/j.matpr.2021.02.065.
Allen, R.G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspiration (Guidelines for computing crop water requirements). FAO irrigation and drainage Paper No. 56. Food and Agricultural Organization of the United Nations, Rome, 300p.
Ayaz, A., Rajesh, M., Kumar, S. and Rehana, S. (2021). Estimation of reference evapotranspiration using machine learning models with limited data. AIMS Geosci., 7(3), 268–290. DOI: 10.3934/geosci.2021016.
Bui, D. T., Ho, T. C., Pradhan, B., Pham, B. T., Nhu, V. H. and Revhaug, I. (2016). GIS-based modeling of rainfall-induced landslides using data mining based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ. Earth Sci., 75(14), 1101. DOI : 10.1007/s12665-016-5919-4.
Chen, W., Hong, H., Li, S., Shahabi, H., Wang, Y. and Wang, W. (2019). Flood susceptibility modeling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. J. Hydrol., 864-873. DOI:
10.1016/j.jhydrol.2019.05.089.
Demirci, M. (2019). Prediction of precipitation flow relationship using support vector machines and M5 decision tree methods. DUMF Eng. J., 10: 1113−1124.
Hamoud, A., Hashim, A. S. and Awadh, W. A. (2018). Predicting student performance in higher education institutions using decision tree analysis. Int. J. Interact. Multimed. Artif. Intell., 5- 26. DOI: 10.9781/ijimai.2018.02.004.
Hansen, S. (1984). Estimation of potential and actual evapotranspiration. Nordic Hydrol., 15, 205–212. DOI: 10.2166/nh.1984.0017.
Hashemi, E., Ahmadpari, H. and Kohneh, K. (2017). Comparison of different methods of estimating potential evapotranspiration with FAO Penman-Mantith method (Case study: Sepidan region). Nivar, 41, 13-22. DOI: 10.30467/nivar.2017.51886. [In Persian]
Hozhabr, H., Moazed, H. and Shokrikhoochak, S. (2014). Estimation of reference evapotranspiration (ETo) using empirical models, artificial neural network modeling and their comparison with lysimeter data in Urmia Kahrizi Station. Journal of Irrigation and Water Engineering., 15, 13-25 [In Persian].
Khoshhal J., Zareh H. and Joshani A. (2015). Different methods for estimating reference evapotranspiration by FAO evaporation pan method in the east and southeast of the country. Quart. J. Nat. Geogr., 8(28), 1-16. [In Persian]
Moradi, A., Ziaean, A. H. (2019). Evaluation of eleven models for reference crop evapotranspiration estimation in Haji Abad region of Hormozgan. Iran. J. Irrig. Drain., 6, 1623-1637 [In Persian].
Nasrolahi, A., Sabzevari, Y., Sharifipour, M. and Shahinejad. (2020). Evaluation of bayesian network and support vector machine models in estimation of reference evapotranspiration (Case Study: Khorramabad). Iran. J. Irrig. Drain., 2, 522-534 [In Persian].
Oudina L, Hervieua F, Michela C, Perrina C, Andre´assiana V, Anctilb, F. and Loumagnea, C. (2005). Which potential evapotranspiration input for a lumped rainfall–runoff model? Part 2—Towards a simple and efficient potential evapotranspiration model for rainfall–runoff modeling. Journal of Hydrology., 303(1–4), 290-306. DOI: 10.1016/j.jhydrol.2004.08.026.
Piri, H. and Pouzan, M. T. (2019). Evaluation of 24 evapotranspiration models of reference plants in different climates of Iran. Echohydrol., 6, 611-622 [In Persian].
Priestley, C. and Taylor, R. )1972(. On the assessment of surface heat flux and evaporation using large-scale parameters. Month. Weather Rev., 100(2), 81- 92.
Salam, R. and Towfiqul Islam, A. R. (2020). Potential of RT, Bagging and RS ensemble learning algorithms for reference evapotranspiration prediction using climatic data-limited humid region in Bangladesh. Journal Hydrology, DOI: 10.1016/j.jhydrol.2020.125241.
Samadianfard, S., Salarifar, M., Javidan, S. and Mikaeili, F. (2020). Estimation of daily reference evapotranspiration in humid climates using data-driven methods of gaussian process regression, support vector regression and random forest. Environ. Water Eng., 4, 360-373. DOI: 10.22034/jewe.2020.241690.1394 [In Persian].
Sattari, M. T., Apaydin, H. and Shamshirband, S. (2020). Performance evaluation of deep learning-based gated recurrent units (GRUs) and tree-based models for estimating ETo by using limited meteorological variables. Mathemat., 8. DOI:10.3390/math8060972.
Satta