Cognitive computing models for estimation of reference evapotranspiration: A review

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@Article{HEBBALAGUPPAEKRISHNASHETTY:2021:CSR,

• author = "Pradeep {Hebbalaguppae Krishnashetty} and Jasma Balasangameshwara and Sheshshayee Sreeman and Sujeet Desai and Archana {Bengaluru Kantharaju}",
• title = "Cognitive computing models for estimation of reference evapotranspiration: A review",
• journal = "Cognitive Systems Research",
• volume = "70",
• pages = "109--116",
• year = "2021",
• ISSN = "1389-0417",
• DOI = "doi:10.1016/j.cogsys.2021.07.012",
• URL = "https://www.sciencedirect.com/science/article/pii/S1389041721000620",
• keywords = "genetic algorithms, genetic programming, Crop water requirements, Irrigation system, Artificial neural networks, Support vector machine",
• 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",

}

Genetic Programming entries for Pradeep Hebbalaguppae Krishnashetty Jasma Balasangameshwara Sheshshayee Sreeman Sujeet Desai Archana Bengaluru Kantharaju

Citations