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Predicting Sediment transport in sewers using integrative harmony search-ANN model and factor analysis
Citation key Zounemat_Kermani_2020
Author Mohammad Zounemat-Kermani and Marzieh Fadaee and S Adarsh and Reinhard Hinkelmann
Pages 012004
Year 2020
DOI 10.1088/1755-1315/491/1/012004
Journal IOP Conference Series: Earth and Environmental Science
Volume 491
Month jul
Publisher IOP Publishing
Abstract This study evaluates the performance of an integrated version of artificial neural network namely HS-ANN (which is a combination of neural network and heuristic harmony search algorithm) as an alternative approach to predict the sediment transport in terms of sediment volumetric concentration (Cv) in sewer pipe systems. To overcome the complexities of choosing the optimum number of the input variables and to consider the effective parameters of the model, the factor analysis technique is utilized. In addition to the HS-ANN model, an empirical equation, as well as a multiple linear regression model, are also considered. The mean square error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficients (PCC) are used for evaluating the accuracy of the applied models. As the comparisons demonstrate, the HS-ANN model (PCC = 0.97) is more accurate than the existing empirical equation and MLR model and could be successfully employed in predicting sediment transport in sewer networks.
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