References
[1] Petković, D., Gocic, M., Trajkovic, S., Shamshirband, S., Motamedi, S., Hashim, R. and Bonakdari, H. (2015). Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology. Computers and Electronics in Agriculture, 114: 277-284.
[2] Ramoelo, A., Majozi , N., Mathieu , R., Jovanovic , N., Nickless , A. and Dzikiti , S. (2014). Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa. Remote Sensing, 6: 7406-7423.
[3] Allen, R. G., Jensen, M. E., Wright, J. L. and Burman, R. D. (1989). Operational Estimates of Reference Evapotranspiration. Agronomy Journal, 81: 650-662.
[4] Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop Evapotranspiration - Guidelines for Computing Crop Water Requirements. Food and Agriculture Organisation. Available: http://www.fao.org/docrep/x0490e/x0490e0b.htm. (Accessed 16/8/2015).
[5] Ikudayisi, A., Adeyemo, J., Odiyo, J. and Enitan, A. (2018). Optimum irrigation water allocation and crop distribution using combined Pareto multi-objective differential evolution. Cogent Engineering, 5 (1): 1-16.
[6] Gocic, M. and Trajkovic, S. (2010). Software for estimating reference evapotranspiration using limited weather data. Computers and Electronics in Agriculture, 71 (2): 158-162.
[7] Kisi, O. (2006). Evapotranspiration estimation using feed-forward neural networks. Nord. Hydrology, 37 (3): 247-260.
[8] Kisi, O. (2008). The potential of different ANN techniques in evapotranspiration modelling. Hydrological Processes, 22: 1449–2460.
[9] Kumar, M., Raghuwanshi, N. and Singh, R. (2009). Development and validation of GANN model for evapotranspiration estimation. Journal of Hydrological Engineering, 14 (2): 131-140.
[10] Kisi, O. (2011). Modeling Reference Evapotranspiration Using Evolutionary Neural Networks. Journal of Irrigation and Drainage Engineering - ASCE, 137 (10): 636-643.
[11] Gocić, M., Motamedi, S., Shamshirband, S., Petković, D., Ch, S., Hashim, R. and Arif, M. (2015). Soft computing approaches for forecasting reference evapotranspiration. Computers and Electronics in Agriculture, 113: 164-173.
[12] Kisi, O. (2013). Applicability of Mamdani and Sugeno fuzzy genetic approaches for modeling reference evapotranspiration. Journal of Hydrology, 504: 160-170.
[13] Shiri, J., Nazemi, A. H., Sadraddini, A. A., Landeras, G., Kisi, O., Fard, A. F. and Marti, P. (2013). Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration. Journal of Hydrology, 480: 46-57.
[14] Shiri, J., Nazemi, A. H., Sadraddini, A. A., Landeras, G., Kisi, O., Fakheri Fard, A. and Marti, P. (2014). Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Computers and Electronics in Agriculture, 108: 230-241.
[15] Kisi, O. (2016). Modeling reference evapotranspiration using three different heuristic regression approaches. Agricultural Water Management, 169: 162-172.
[16] Kamble, B., Irmak, A., Hubbard, K. and Gowda, P. (2013). Irrigation Scheduling using Remote sensing data assimilation approach. Advances in Remote Sensing, 2: 258-268.
[17] Raziei, T. and Pereira, L. S. (2013). Estimation of ETo with Hargreaves–Samani and FAO-PM temperature methods for a wide range of climates in Iran. Agricultural Water Management, 121: 1-18.
[18] Todorovic, M., Karic, B. and Pereira, L. S. (2013). Reference evapotranspiration estimate with limited weather data across a range of Mediterranean climates. Journal of Hydrology, 481: 166-176.
[19] Pereira, L. S., Allen, R. G., Smith, M. and Raes, D. (2015). Crop evapotranspiration estimation with FAO56: Past and future. Agricultural Water Management, 147: 4-20.
[20] Gocic, M., Petković, D., Shamshirband, S. and Kamsin, A. (2016). Comparative analysis of reference evapotranspiration equations modelling by extreme learning machine. Computers and Electronics in Agriculture, 127: 56-63.
[21] Yassin, M. A., Alazba, A. A. and Mattar, M. A. (2016). Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate. Agricultural Water Management, 163: 110-124.
[22] Xing, X., Liu, Y., Zhao, W. g., Kang, D. g., Yu, M. and Ma, X. (2016). Determination of dominant weather parameters on reference evapotranspiration by path analysis theory. Computers and Electronics in Agriculture, 120: 10-16.
[23] Lu, H., Huang, G. and He, L. (2011). An inexact rough-interval fuzzy linear programming method for generating conjunctive water-allocation strategies to agricultural irrigation systems. Applied Mathematical Modelling, 35 (9): 4330-4340.
[24] Tahmasebi, P. and Hezarkhani, A. (2012). A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Computers & Geosciences, 42 (Supplement C): 18-27.
[25] Jha, G. K. (2007). Artificial neural networks and its applications. http://www.iasri.res.in/ebook/ebadat/5-Modeling%20 and%20Forecasting%20Techniques%20in%20Agriculture/5-ANN_GKJHA_2007.pdf: Accessed (15th May 2015).
[26] Abdulkadir, T. S., Sule, B. F. and Salami, A. W. (2012). Application of Artificial Neural Network Model to the Management of Hydropower Reservoirs along River Niger, Nigeria. International Journal of Engineering, 3 (3): 123-127.
[27] Nasr, M., Moustafa, M., Seif, H. and El-Kobrosy, G. (2014). Application of fuzzy logic control for Benchmark simulation model.1. Sustainable Environment Research, 24 (4): 235-243.
[28] Katambara, Z. and Ndiritu, J. (2009). A fuzzy inference system for modelling streamflow: Case of Letaba River, South Africa. Physics and Chemistry of the Earth, Parts A/B/C, 34 (10–12): 688-700.
[29] Nasr, M., Mahmoud, A., Fawzy, M. and Radwan, A. (2015). Artificial intelligence modeling of cadmium(II) biosorption using rice straw. Applied Water Science, 10: 295-394.
[30] Sanchez, E., Celikovsky, S., Gonzalez, J. and Ramirez, E. (2001). Wastewater treatment plant control by combining fuzzy logic and nonlinear estimation. In: Proceedings of 2001 IEEE. International Symposium on Intelligent Control. Mexico City, September, 2001.
[31] MATLAB. (2002). Fuzzy Logic Toolbox for use with MATLAB. MA, USA: The Mathworks, Natick.
[32] Fiter, M., Güell, D., Comas, J., Colprim, J., Poch, M. and Rodríguez-Rodal, I. (2005). Energy saving in a wastewater treatment process: an application of fuzzy logic control. Environmental Technology, 26 (11): 1263-1270.
[33] Vijayalaksmi, D. P. and Babu, K. S. J. (2015). Water Supply System Demand Forecasting Using Adaptive Neuro-fuzzy Inference System. Aquatic Procedia, 4: 950-956.
[34] Whelan, D. (2019). Water, settlement and food provision in Natal Colony: The Winterton Irrigation Settlement, 1902-1904. Historia, 64 (1): 42-64.
[35] De Jager, J. W. and Mottram, R. (2018). Research on Maximising Irrigation project efficiency in different soil-climate-irrigation situations. Pretoria: Water Research Council. Available: www.wrc.org.za (Accessed 15 February 2021).