Multiobjecitve Sampling Design for Calibration of Water Distribution Network Model Using Genetic Algorithm and Neural Network

Document Type : Research Paper

Authors

1 PhD Candidate of Hydraulic Engineering, Department of Civil and Environmental Engineering, Amir Kabir University of Technology

2 Assistant Professor, Department of Civil & Environmental Engineering, Amir Kabir University of Technology

Abstract

In this paper, a novel multiobjective optimization model is presented for selecting optimal locations in the water distribution network (WDN) with the aim of installing pressure loggers. The pressure data collected at optimal locations will be used later on in the calibration of the proposed WDN model. Objective functions consist of maximization of calibrated model prediction accuracy and minimization of the total cost for sampling design. In order to decrease the model run time, an optimization model has been developed using multiobjective genetic algorithm and adaptive neural network (MOGA-ANN). Neural networks (NNs) are initially trained after a number of initial GA generations and periodically retrained and updated after generation of a specified number of full model-analyzed solutions. Trained NNs are replaced with the fitness evaluation of some chromosomes within the GA progress. Using cache prevents objective function evaluation of repetitive chromosomes within GA. Optimal solutions are obtained through pareto-optimal front with respect to the two objective functions. Results show that jointing NNs in MOGA for approximating portions of chromosomes’ fitness in each generation leads to considerable savings in model run time and can be promising for reducing run-time in optimization models with significant computational effort.

Keywords

References

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Journal of Water and Wastewater; Ab va Fazilab ( in persian )
Volume 19, Issue 1 - Serial Number 1
Serial number:65
March 2008
Pages 13-22

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