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Forecasting Daily Stream Flows of Vaniar River Using Genetic Programming and Neural Networks Approaches Original Article, C31 Roushangar K, Vojoudi Mehrabani F, Alami M.T Journal. Civil Eng. Urban. 3(4):197-200. 2013 ABSTRACT:This study compares three different artificial intelligence approaches, namely, gene expression programming (GEP), and artificial neural networks (ANN), in daily as well as monthly streamflow forecasting. Daily streamflow data from Vaniar River in the Northwestern Iran were used. Coefficient of determination, root mean square error and scatter index were used to compare simulation results. The study demonstrates that the optimal results were obtained from the triple-input models including streamflows of current and two previous days and that the GEP model performed better than the ANN model in daily streamflow forecasting. Keywords: Gene Expression Programming, Neural Networks, Forecasting منبع: [Hidden Content] دانلود: [Hidden Content].,31-197-200.pdf J. Civil Eng. Urban.,31-197-200.pdf

Gene Expression Programming for Prediction of Flow Discharge in Compound Channels Oiginal Article, B29 Zahiri A, Eghbali P. J. Civil Eng. Urban. 2(4): 164-169. 2012. ABSTRACT:There are many studies on the hydraulic calculation of steady uniform flows in compound open channels. Based on these studies, various methods have been developed with different assumptions. In general, these methods either have long computations or need numerical solution of differential equations. Furthermore, their accuracy for all compound channels with different geometric and hydraulic conditions may not be guaranteed. In this paper, to overcome with these limitations, a new simple and dimensionless equation has been proposed based on gene-expression programming (GEP), which is an extension to genetic programming (GP). In this equation only three parameters (e.g. depth ratio, coherence and ratio of computed total flow discharge to bankful discharge) have been used to simplify its applications for hydraulic and river engineers. By compiling 394 stage-discharge data from laboratories and fields of 30 compound channels, this new equation has been applied to estimate the flow conveyance capacity. The proposed GEP approach gives satisfactory results (R2 = 0.98 and RMSE = 0.32) compared to traditional method (VDCM) with R2 = 0.69 and RMSE = 1.72 for flow discharge computations. Keywords:Compound Channels, Gene Expression Programming, Stage-Discharge Curve. منبع: [Hidden Content] دانلود: [Hidden Content],%20B29.pdf JCEU, B29.pdf