Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

Behzadian, Kourosh ORCID:, Kapelan, Zoran, Savic, Dragan and Ardeshir, Abdollah (2008) Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks. Environmental Modelling & Software, 24 (4). pp. 530-541. ISSN 13648152

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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

Item Type: Article
Identifier: 10.1016/j.envsoft.2008.09.013
Subjects: Construction and engineering > Civil and environmental engineering
Depositing User: Kourosh Behzadian
Date Deposited: 05 Jan 2022 19:43
Last Modified: 11 Jul 2024 08:02

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