Behnam Ababaei et al./ Elixir Comp. Sci. & Engg. 42 (2012) 6074-6077 6074 Introduction Information about the parameters defining water resources availability is a key factor in their management which improves the operation policies for water resources systems. One of the most important parameters in this area is river streamflow. Moreover, climate change impact assessment studies often need models capable in simulating river streamflow on a daily time basis. The modeling of streamflow time process has essentially followed two approaches (Razavi and Araghinejad 2009): empirical and statistical simulation of the hydrological system. In the first approach, the hydrological system is described by theoretical and/or empirical (physical) relationships (e.g. Garrote and Bras 1995), whereas, in the statistical approach, the objective is to develop a model in order to represent the most relevant statistical characteristics of the historical series (Araghinejad et al. 2006). Artificial Neural Networks (ANNs) Many statistical-based methods are used to model and forecast streamflow time series. In the recent years, Artificial Neural Networks (ANNs), have been widely studied and applied to simulate and forecast the hydrological variables (Hsu et al. 1995; Coulibaly et al. 2001; Razavi and Karamouz 2007; Altunkaynak 2007; Razavi and Araghinejad 2009; Ahmed and Sarma 2007; El-Shafiel et al. 2007). Radial Basis Neural Network (RBN) The radial basis function approach traces its roots from the work of Powell (1987), whose use as an alternative tool to learning in neural networks is particularly suited to multivariable interpolation given irregularly positioned data points. Their use in neural networks has found applications in solution of classification problems, function approximation, noisy interpolation, and regularization (Ke´gl et al. 2000) in various engineering fields due to their advantages over traditional multilayer perceptrons (Kagoda et al. 2010), namely faster convergence, smaller extrapolation errors, and higher reliability (Girosi and Pogio 1990). In hydrology and considering the complex nature of the rainfall–runoff process which is usually highly non-linear, the most suitable neural networks for modeling the process should have the ability to approximate any continuous function. The RBF technique provides good generalization ability with a minimum number of nodes to avoid unnecessarily lengthy calculations, in comparison with multilayer perceptron networks (Moradkhani et al. 2004). The architecture of radial basis function neural networks consists of an input layer, one hidden layer and one output layer. Each node in the hidden layer evaluates a radial basis function on the incoming input. In this study, the radial basis function applied was the Gaussian function and the neural network output was then evaluated as the weighted linear summation of the radial basis functions. General Regression Neural Network (GRNN) General Regression Neural Networks (GRNNs), falls into the category of probabilistic neural networks. This neural network like other probabilistic neural networks needs only a fraction of the training samples a backpropagation neural network would need. The use of a probabilistic neural network is especially advantageous due to its ability to converge to the underlying function of the data with only few training samples available. The additional knowledge needed to get the fit in a satisfying way is relatively small and can be done without additional input by the user. This makes GRNN a very useful tool to perform predictions and comparisons of system performance in practice. GRNN consists of four layers namely, input layer, pattern layer, summation layer and output layer (Singh and Deo 2007). The first layer is fully connected to the second pattern layer, where each unit represents a training pattern and its output is a measure of the distance of the input from the stored patterns. Each pattern layer unit is connected to the two neurons in the summation layer: S-summation neuron and D-summation neuron. The S-summation neuron computes the sum of the weighted outputs of the pattern layer while the D- summation neuron calculates the unweighted outputs of the pattern neurons. Elixir Comp. Sci. & Engg. 42 (2012) 6074-6077 Assessment of radial basis and generalized regression neural networks in daily reservoir inflow simulation Behnam Ababaei * , Teymour Sohrabi and Farhad Mirzaei Department of Irrigation and Reclamation Engineering, University of Tehran, Iran. ABSTRACT In this study, two different type of Artificial Neural Networks (ANNs) were analyzed in simulating the daily inflow into Taleghan reservoir in Iran. These types include: General Regression Neural Network with standardized inputs (GRNN1) and with non-standardized inputs (GRNN1), and Radial Basis Networks with standardized inputs (RBN1) and with non-standardized inputs (RBN2). An iterative algorithm was designed to assess different architecture of these models. Results revealed the potential of these models, as suitable tools for simulating the daily reservoir inflow. Also, it was concluded that multiday averaging can improve the simulation results considerably. © 2012 Elixir All rights reserved. Computer Science and Engineering ARTICLE INFO Article history: Received: 2 November 2011; Received in revised form: 15 December 2011; Accepted: 26 December 2011; Keywords Taleghan Reservoir, Daily Inflow, Radial Basis Neural Networks, Generalized Regression Neural Networks. Available online at www.elixirjournal.org Tele: E-mail addresses: Behnam.ab@gmail.com © 2012 Elixir All rights reserved