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Consolidation Of Soft Computing Approaches For Predictingthe Wheat Yield In India
[Full Text]
AUTHOR(S)
Surjeet Kumar, Manas Kumar Sanyal
KEYWORDS
Statistical Equations, Artificial Neural Network (ANN), Genetic Algorithm (GA), Root Mean Square Error (RMSE), Mean Square Error (MSE) and Average Error.
ABSTRACT
this paper prognosticates the production of wheat crop by developing a hybrid model through the combination of soft computing approaches. This material illustrates a brief study on the time series forecasting to predict the future data on the basis of the previous year data. The proposed model has been developed with the combination of Statistical Equations, Artificial Neural Network (ANN) and Genetic Algorithm (GA) to speculate the wheat production and to get more explicit outcomes. This model has been tested on the wheat production data of India from 1980 to 2018. Thereafter, by using statistical error computing techniques like Mean Square Error (MSE), Root Mean Square Error (RMSE) and Average Error, the Prediction Performances have been evaluated. It has been observed that due to the use of our proposed model compared to the Standalone Soft Computing, error prediction decreased.
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