Estimation and filling of missing runoff data at Al-Jawadiyah station using artificial neural networks

Runoff is one of the most important components hydrological cycle, and having complete series runoff data essential for any modelling process. This study aims to estimate at Al-Jawadiyah hydrometric station using artificial neural networks. used only in addition values measured Al-Amiri on Syrian-Lebanese border. Many experiments were conducted a very large number networks trained with changing hidden layers, neurons training algorithms until best network was reached according regression criteria root mean error squares between predicted values, (2:12:1) adopted process filling gaps time during period. recommends working preparing climatic measurements that form basis an accurate model area.