Modelling of Bektas Creek Daily Streamflow with Generalized Regression Neural Network Method

Abstract

The dependence of agricultural production on water resources is a known fact. Therefore, understanding hydrological processes
and events in agricultural production form the basis of effective and reliable management of water resources. Many traditional
methods used for the analysis of time-dependent variables in hydrology and meteorology assume linear relationships between
these variables. However, the temporal changes of these parameters are quite complex and therefore cannot be easily modeled by conventional estimation methods. Artificial neural networks (ANNs), on the other hand, allow the analysis of nonlinear relationships or processes whose statistical or mathematical calculations cannot be determined in such systems. ANNs have been
accepted as a successful model in multidimensional research involving dynamic processes in the field of hydrology.

This study, it is aimed at the modeling of stream flows in Bektas Creek. With the aim of modeling, daily meteorological parameters (precipitation, temperature, sunbathing time, relative humidity) measured in Kangal Region and one day delayed flows were used. Streamflow forecasts are simulated with the Generalized Regression Neural Network (GRNN). To reveal the difference of the GRNN model from other ANNs, the same data were also used in the feed-forward neural network (FFNN) model. Model performances were evaluated taking into account the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Determination coefficient (R).