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dc.contributor.authorBehzadian, Kourosh
dc.contributor.authorArdeshir, Abdollah
dc.contributor.authorKapelan, Zoran
dc.contributor.authorSavic, Dragan
dc.date.accessioned2015-04-29T13:49:09Z
dc.date.issued2008
dc.description.abstractA novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (WDS) for the purpose of its hydraulic model calibration is presented. The problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. The objectives are to maximise the calibrated model accuracy and to minimise the number of sampling devices as a surrogate of sampling design cost. Model accuracy is defined as the average of normalised traces of model prediction covariance matrices, each of which is constructed from a randomly generated sample of calibration parameter values. To resolve the computational time issue, the optimisation problem is solved using a multi-objective genetic algorithm and adaptive neural networks (MOGA-ANN). The verification of results is done by comparison of the optimal sampling locations obtained using the MOGA-ANN model to the ones obtained using the Monte Carlo Simulation (MCS) method. In the MCS method, an equivalent deterministic sampling design optimisation problem is solved for a number of randomly generated calibration model parameter samples.The results show that significant computational savings can be achieved by using MOGA-ANN compared to the MCS model or the GA model based on all full fitness evaluations without significant decrease in the final solution accuracy.en_GB
dc.identifier.citationVol. 6 (1), pp. 48 - 57en_GB
dc.identifier.urihttp://hdl.handle.net/10871/17054
dc.language.isoenen_GB
dc.publisherIran University of Science and Technology (IUST) and Iran Society of Civil Engineersen_GB
dc.relation.urlhttp://ijce.iust.ac.ir/browse.php?a_code=A-10-952-5&slc_lang=en&sid=1en_GB
dc.subjectsampling designen_GB
dc.subjectwater distribution modelen_GB
dc.subjectcalibrationen_GB
dc.subjectgenetic algorithmen_GB
dc.titleStochastic Sampling Design for Water Distribution Model Calibrationen_GB
dc.typeArticleen_GB
dc.date.available2015-04-29T13:49:09Z
dc.identifier.issn1735-0522
dc.descriptionCopyright © 2008 International Journal of Civil Engineeringen_GB
dc.identifier.journalInternational Journal of Civil Engineeringen_GB


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