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

Moscow State University of Civil Engineering, Yaroslavskoe shosse, 26, Moscow, 129337, Russia

^* Corresponding author: alaa-slieman@hotmail.com

Abstract

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