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Determining the Amount and Location of Leakage in Water Supply Networks Using a Neural Network Improved by the Bat Optimization Algorithm Original Article, D49 Faghafur Maghrebi M., Aghaebrahimi M.R., Taherian H and Attari M. J. Civil Eng. Urban. 4(3): 322-327. 2014 ABSTRACT: At present, water waste has become a global concern. On the other hand, the amount of sweet water on the earth is fixed and limited but the demand for water is increasing. This, more than ever before, makes it necessary to modify the consumption pattern. One of the most important consumption management activities is to decrease the uncounted water. Water leakage not only results in loss of good-quality water resources, but also pollutes the drinking water and in its worst form brings about serious damages to people and building around the point of leakage. In this paper, a model is presented for determining the amount and location of leakage in water supply networks. In this model which uses a neural network improved by the bat optimization algorithm, the amount and location of leakage in the network is determined by the minimum number of pressure-measuring. The proposed model is applied on the Poulakis network when several simultaneous leakages have occurred, and the accuracy of the model is verified by the results. Keywords: Leakage Detection, Barometers Placement, Neural Network, Bat Algorithm. منبع: [Hidden Content] دانلود: [Hidden Content].,%204%20(3)%20322-327,%202014.pdf J. Civil Eng. Urban., 4 (3) 322-327, 2014.pdf

Investigation and Evaluation of Artificial Neural Networks in Babolroud River Suspended Load Estimation Original Article, C29 Kia E, Emadi A.R, Fazlola R. Journal. Civil Eng. Urban. 3(4):183-190. 2013 ABSTRACT:Estimation of sediment load is a priority of the river management, dam's reservoirs and generally water projects. Because of nonlinear structure of sediment phenomena, the classical and common methods like sediment rating curve is not able to estimate sediment rate correctly. In recent years, artificial intelligence methods such as ANNs are recommended for solving the nonlinear problems and to achieve closer result to the actual data. In the present study, various combinations of flow discharge and sediment rate in present and past days were used as input parameters, while the suspended sediment load was used as output of the model. Then, by use of MATLAB software and different Neural Network such as MLP, RBF and GRNN, optimum architecture of networks is obtained based on four statistical indices viz. mean square error, mean bias error, modeling efficiency and determination coefficient for Ghoran Talar station in Babolroud River to compare with sediment rating curve. The results showed that MLP with combination of current discharge for estimate of current sediment has a better precision than other two neural networks. Also, the use of artificial neural network has a better performance than sediment rating curve method and recommended for river suspended load estimation. Keywords: Suspended Load, Perceptron, Neural Network, Rating Curve منبع: [Hidden Content] دانلود: [Hidden Content].,29-183-190.pdf J. Civil Eng. Urban.,29-183-190.pdf