Neural networks in optimal calibration of water distribution systems

An optimization model based on genetic algorithms is applied for the optimal calibration of water distribution systems. The model uses a neural network for function evaluation in conjunction with a rigorous mathematical simulation model. An efficient training scheme, which greatly reduces the training period compared to the popular backpropagation scheme, is employed to increase the computational efficiency. For large water distribution systems, the use of neural networks in conjunction with a genetic optimization framework can increase the computational efficiency significantly.