Using nonlinear autoregressive neural network for estimation daily evaporation: a comparison of neural networks with different algorithms | ||
| Tikrit Journal of Pure Science | ||
| Article 1, Volume 19, Issue 6, December 2014, Pages 210-219 | ||
| Authors | ||
| Shahla A. AbdAlKader; Najem A.AbdulKader; Kifaa H. Thanoon | ||
| Abstract | ||
| Abstract In this research a model of Dynamic NN(NARX) was applied to estimate the daily Evaporation of Mosul city using certain climate parameters(the maximum and the minimum temperature ,rain ,relative humidity ,wind speed and the sun shine )for any day in the year , and comparison for Static NN like FFBPNN, CFBPNN . Each of these networks has two architecture: an architecture with four layers and five cells in hidden layers from one hand, and an architecture with five layers and five cells in the hidden layers from the other. Different algorithm were used for the training like: Levenberg-Marquardt algorithm (LM), Quasi-Newton algorithm (BFGS), Conjugate Gradient algorithm (CFG), Gradient Descent algorithm (GD) and Gradient Descent with Momentum algorithm (GDM). Data was obtained from the forecast Directorate in AlRashedeyyah district in Nineveh Province for the period (1995-2008) and used in the research. Data of ten years for the period (1995-2004)was employed to develop the models and the data of four years was used to evaluate the models and to compare their outputs with the data measured for the period(2005-2008). Moreover; determination coefficient R_square (R2) and the Root Mean Square Error ( RMSE) methods were used to estimate the level of correspondence for the measured data and NN outputs to select the best prediction model from the models applied. Results showed that the NARX with(LM) algorithm is efficient in improving a prediction model to estimate the daily Evaporation as the value of coefficient estimation was 0.99, and this is considered the best and the fastest algorithm if temperature, rain, relative humidity ,wind speed and sunshine data available for any day in the year | ||
| Keywords | ||
| Dynamic NN; Static NN; Gradient Descent; Gradient Descent with Momentum; Conjugate gradient; Quasi; Newton; Levenberg; Marquardt | ||
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