PREDICTION OF TIGRIS RIVER DISCHARGE IN BAGHDAD CITY USING ARTIFICAL NEURAL NETWORKS

Abstract

 Artificial Neural Networks (ANNs), with three layers feed- forward network of sigmoid hidden neurons and linear output neurons are performed for predicting Tigris River flow in Baghdad City, middle of Iraq. The network is trained with Levenberg-Marquradt back-propagation algorithm. The number of hidden neurons is estimated according to trial and error procedure. The best model is selected according to trial and error procedure based on root mean square error and coefficient of correlation. The selected model is used to predicate the river discharge for one, two, and three months ahead. Results indicate that the ANNs with Levenberg-Marquradt back-propagation algorithm are a powerful tool for forecasting the river discharge for short term duration. But this ability begins to decrease when increasing the period of forecasting.