Prediction of wheat yield using Artificial Neural Network and Fuzzy time series models in Eastern agro climatic zone of Haryana
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Date
2019
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CCSHAU
Abstract
This study deals with the prediction of wheat yield using Artificial Neural Network and Fuzzy time series models in Eastern agro climatic zone of Haryana. It also includes the algorithms for the model development and computations. ANN and fuzzy time series models for wheat yield prediction in Eastern agro climatic zone of Haryana have been developed using meteorological parameters. The predicted yield obtained by the fuzzy time series model have been compared with that of the Artificial neural network model along with the actual wheat yield, and the results are found encouraging. Best fitted architecture for Artificial Neural network was selected based on goodness of fit statistic criterion for wheat yield prediction and is used for prediction of wheat yield in Eastern agro climatic zone of Haryana using meteorological parameters. We found that Logsig transfer function was the best fitted neural network with five neurons in a single hidden layer. The values of R2, MSE, RMSE and MARD criterion were used to compare the performance of ANN and Fuzzy time series models. These criterions indicates that ANN model is slightly better than the fuzzy time series model for prediction of wheat yield in Eastern agro climatic zone of Haryana.
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