Behzadian, Kourosh ORCID: https://orcid.org/0000-0002-1459-8408, Kapelan, Zoran, Savic, Dragan and Ardeshir, Abdollah (2008) Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks. Environmental Modelling and Software, 24 (2009). pp. 530-541. ISSN 1364-8152

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Abstract

This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm,
which uses metamodels to determine optimal sampling locations for installing pressure loggers in
a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA–ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The
objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from
a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the ‘full’ fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was
applied to a real case study. The results show that significant computational savings can be achieved by
using the MOGA–ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA–ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution.

Item Type:	Article
Identifier:	doi10.1016/j.envsoft.2008.09.013
Keywords:	Water distribution system; Calibration; Uncertainty; Multi-objective genetic algorithm; Adaptive neural networks
Subjects:	Computing Construction and engineering
Depositing User:	Kourosh Behzadian
Date Deposited:	20 Sep 2023 13:46
Last Modified:	06 Feb 2024 16:16
URI:	https://repository.uwl.ac.uk/id/eprint/10188

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