A Comparative Study Between Adaptive Filtering and Artificial Neural Networks Methods with Applications

 In this research, the phenomenon of evaporation was studied for the period     1980 - 2020 for the city of Mosul, and the adaptive filtering method and neural networks were used in order to predict the monthly evaporation values in the city of Mosul. if the adaptive filtering is characterized by correcting the parameters of the time series model to the extent that the parameters that give the lowest mean of the error squares MSE for the purpose of predicting the future values of the phenomenon under study. The NARNET network was used as a neural network to compare the two methods, as statistical criteria were adopted to compare them and identify the best method for prediction.

A factorial seasonal model was diagnosed and its parameters were found using the computer programs Excel, Minitab and MATLAB, and future values were predicted. Through the comparison, it was found that the neural networks represented by the NARNET network give relatively better results compared to the conditioned purification.