Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programing algorithm: A new hybrid copula-driven approach

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@Article{ALI:2018:AFM,

• author = "Mumtaz Ali and Ravinesh C. Deo and Nathan J. Downs and Tek Maraseni",
• title = "Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programing algorithm: A new hybrid copula-driven approach",
• journal = "Agricultural and Forest Meteorology",
• volume = "263",
• pages = "428--448",
• year = "2018",
• keywords = "genetic algorithms, genetic programming, Crop yield prediction, Cotton yield, Climate data, Markov Chain Monte Carlo based copula model",
• ISSN = "0168-1923",
• DOI = "doi:10.1016/j.agrformet.2018.09.002",
• URL = "http://www.sciencedirect.com/science/article/pii/S0168192318302971",
• abstract = "Reliable data-driven models designed to accurately estimate cotton yield, an important agricultural commodity, can be adopted by farmers, agricultural system modelling experts and agricultural policy-makers in strategic decision-making processes. In this paper a hybrid genetic programing model integrated with the Markov Chain Monte Carlo (MCMC) based Copula technique is developed to incorporate climate-based inputs as the predictors of cotton yield, for selected study regions: Faisalabad",

}

Genetic Programming entries for Mumtaz Ali Ravinesh C Deo Nathan J Downs Tek Maraseni

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