A hybridization of feedforward neural network and differential evolution to forecast fertilizer consumption emphasizing on selecting optimal architecture

A hybridization of feedforward neural network and differential evolution to forecast fertilizer consumption emphasizing on selecting optimal architecture

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A fertilizer marketing or producing mabis dmi healthcare sector has played an important role in agricultural productivity and also food security around the world for many years.However, the demand of fertilizer consumption is uncertain and difficult to be forecasted by using simple approaches.Therefore, an accuracy of future demand concerning fertilizer is very interesting task to support decision making.

In this research, a hybrid model of feedforward neural networks and differential evolution emphasizing on architectural evolution is developed citroen c4 picasso boot liner to forecast ten datasets of fertilizer consumption and is compared with conventional models based on five accuracy measures.The empirical results indicated that the developed model can provide more accuracy than conventional models at 0.05 significance levels.

Furthermore, the capability of the developed model can also provide the highest precision compared with both ARIMA and SVR models.Consequently, the developed model can be a promising tool to predict future demands of fertilizer consumption.