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Simulating wheat yield in New South Wales of Australia using interpolation and neural networks
Accurate modeling of wheat production in advance provides wheatgrowers, traders, and governmental agencies with a great advantage in planning the distribution of wheat production. The conventional approach in dealing with such prediction is based on time series analysis through statistical or intelligent means. These time-series based methods are not concerned about the factors that cause the sequence of the events. In this paper, we treat the historical wheatdata in New South Wales over 130 years as non-temporal collection of mappings between wheat yield and both wheat plantation area and rainfall through data expansion by 2D interpolation. Neural networks are then used to define a dynamic system using these mappings to achieve modeling wheat yield with respect to both the plantation area and rainfall. No similar study has been reported in the world in this field. Our results demonstrate that a four-layer multilayer perceptron model is capable of producing accurate modeling for wheat yield.