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Cognitive computing models for estimation of reference evapotranspiration: A review
Affiliation:1. Department of Computer Science & Engineering, JSS Academy of Technical Education, Bengaluru & Research Scholar, Visvesvaraya Technological University – RRC, Belgaum, Karnataka, India;2. Department of Computer Science & Technology, School of Engineering, Dayananda Sagar University, Bengaluru, Karnataka, India;3. Department of Crop Physiology, University of Agricultural Sciences, Bengaluru, Karnataka, India;4. Land & Water Management Engineering, Indian Council of Agricultural Research, Central Coastal Agricultural Research Institute, Goa, India;5. JSS Academy of Technical Education, Bengaluru, Karnataka, India;1. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China;2. Water Engineering Department, Urmia University, Urmia, Iran;1. Department of Agricultural Engineering, Federal University of Viçosa, Av. Peter Henry Rolfs, s/n, CEP: 36570 000 Viçosa, MG, Brazil;2. Department of Soil, Federal University of Viçosa, Av. Peter Henry Rolfs, s/n, CEP: 36570 000 Viçosa, MG, Brazil;1. University of Niš, Faculty of Civil Engineering and Architecture, Aleksandra Medvedeva 14, 18000 Niš, Serbia;2. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;3. Institute of Ocean and Earth Sciences (IOES), University of Malaya, 50603 Kuala Lumpur, Malaysia;4. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;5. University of Niš, Faculty of Mechanical Engineering, Department for Mechatronics and Control, Aleksandra Medvedeva 14, 18000 Niš, Serbia;6. Department of Civil and Environmental Engineering, ITM University, Gurugaon, Haryana 122017, India;1. Department of Agricultural Engineering, Federal University of Viçosa, Av. Peter Henry Rolfs, s/n, CEP: 36570 000 Viçosa, MG, Brazil;2. Department of Soil, Federal University of Viçosa, Av. Peter Henry Rolfs, s/n, CEP: 36570 000 Viçosa, MG, Brazil
Abstract:Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET0) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET0. An efficient ET0 prediction model plays an important role in irrigation system to increase water productivity. In the present study, a review has been carried out over cognitive computing models used for the estimation of ET0. Review exhibits that artificial neural network (ANN) approach outperforms support vector machine (SVM) and genetic programming (GP). Second order neural network (SONN) is the most promising approach among ANN models.
Keywords:Crop water requirements  Irrigation system  Artificial neural networks  Support vector machine  Genetic programming
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