A Comparative Study between Artificial Neural Networks and Fuzzy Inference System for Estimation and Filling of Missing Runoff Data at Al-Jawadiyah Station

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 compares artificial neural networks and fuzzy inference systems for estimation of runoff data at the Al-Jawadiyah hydrometric station. 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. Also, many fuzzy inference models have been prepared, changing the number and type of membership functions until the most accurate model has been reached. The results showed the high reliability of both the artificial neural network models and the fuzzy inference models in estimating runoff in the study area, and the comparison between the results showed the great convergence of the two models with a slight preference for the fuzzy models. This study recommends using the rest of the artificial intelligence models and comparing them to arrive at the most accurate model. This helps prepare a complete series of hydrological and climatic measurements that form a basis for preparing an accurate hydrological model for the study area.