Flood Routing Model Using Genetic Expression Programing

A change depending on the time of the flood wave moving in a stream using flood routing approach is examined. Flood routing of flood discharge along the river with their account and calculating the changes in the water level of flood protection structure size is determined to safety. The aim of this study, Sutculer flood event will be modeled by Genetic Expression Programing (GEP) method. The GEP method makes use of few hydrologic parameters such as inflow, outflow, and time. Simulation results indicate that the proposed a predictive model is an appropriate for the flood routing. Case study is presented to demonstrate that the GEP model is an alternative in implementation of the Muskingum model

River Flow Estimation from Upstream Flow Records Using Support Vector Machines

A novel architecture for flood routing model has been proposed and its efficiency is validated on several problems by employing support vector machines. The architecture is designed by including the inputs and observed and calculated outflows from the previous time step output. Whole observed data have been used for determining the model parameters in the heuristic methods given in the literature, which constitutes the major disadvantage of the existing approaches. Moreover, using the whole data for training may lead to overtraining problem that causes overfitting of estimations and data. Therefore, in this study, 60-90% of the data are randomly selected for training and then the remaining data are used for validation. In order to take the effects of the measurement errors into consideration, the data are corrupted by some additive noise. The results show that the proposed architecture improves the model performance under noisy and missing data conditions and that support vector machines can be powerful alternative in flood routing modeling

Predicting Muskingum flood routing parameters using spreadsheets

In this study, two easy to use and high precision solution methods that can be used for educational and engineering purposes are proposed. The first of these methods is the TrialError with Spreadsheet procedure combining basic properties of spreadsheets and the other method is the Nonlinear optimization with Solver procedure that uses the nonlinear optimization code and the Iterative calculation which is one of the most effective properties of spreadsheets. Both of the proposed methods are tested on three samples given in literature and the comparisons of the model results, observation results, and the results of other solution techniques in literature are made. (c) 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 280286, 201