Using the Artificial Neural Networks with different algorithms to estimate the daily evaporation in Mosul city by using climate Information | ||
| Buhuth Mustaqbaliya Scientific Periodical Journal | ||
| Article 1, Volume 1, Issue 1, December 2014, Pages 171-191 | ||
| Abstract | ||
| Abstract In this research a model of Neural Networks 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 using the Feed Forward Back Propagation (FFBPNN), Cascade-Forward Back Propagation (CFBPNN) and the FitNet network. Each of these networks has two architectures: architecture with four layers and five cells in the hidden layers from one hand, and architecture with five layers and five cells in the hidden layers from the other. Different algorithms were used for the training: Levenberg-Marquardt algorithm (LM), Quasi-Newton algorithm (BFGS), Conjugate Gradient algorithm (CFG), Gradient Descent algorithm (GD) and Gradient Descent with Momentum algorithm (GDM). Data were obtained from the forecast Directorate in AlRashedeyyah district in Nineveh Province for the period (1995-2008) are used in the research. Data of ten years for the period (1995-2004) were employed to develop the models and the data of four years were used to evaluate the models, to compare their outputs with the data measured. R2 and the 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 show that the FitNet with (LM) algorithm is efficient in improving a prediction model to estimate the daily Evaporation as the value of coefficient estimation was )0.98(, 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 | ||
| KEYWORDS; Gradient Descent; Gradient Descent with Momentum; Conjugate Gradient Quasi; Newton; Levenberg; Marquardt | ||
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