Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm

The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a genetic algorithm (GA) and use of a growing window approach for training the model are also evaluated. The result...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Majid Gholami Shirkoohi, Mouna Doghri, Sophie Duchesne
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	artificial neural network genetic algorithm hyperparameter optimization short term time series model urban water demand prediction Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/578f40e218044c579c04269965ef2c78
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:578f40e218044c579c04269965ef2c78
record_format	dspace
spelling	oai:doaj.org-article:578f40e218044c579c04269965ef2c782021-11-06T07:19:19ZShort-term water demand predictions coupling an artificial neural network model and a genetic algorithm1606-97491607-079810.2166/ws.2021.049https://doaj.org/article/578f40e218044c579c04269965ef2c782021-08-01T00:00:00Zhttp://ws.iwaponline.com/content/21/5/2374https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a genetic algorithm (GA) and use of a growing window approach for training the model are also evaluated. The results are compared to those of commonly used time series models, namely the Autoregressive Integrated Moving Average (ARIMA) model and a pattern-based model. The evaluations are based on data sets from two Canadian cities, providing 15 min water consumption records over respectively 5 years and 23 months, with a respective mean water demand of 14,560 and 887 m3/d. The GA optimized ANN model performed better than the other models, with Nash–Sutcliffe Efficiencies of 0.91 and 0.83, and relative root mean square errors of 6 and 16% for City 1 and City 2, respectively. The results of this study indicate that the optimization of the hyperparameters of an ANN model can lead to better 15 min urban water demand predictions, which are useful for many real-time control applications, such as dynamic pressure control. HIGHLIGHTS ANN models were used for short-term (15 min) urban water demand predictions.; The hyperparameters of the ANN model were optimized with a genetic algorithm for better model performance.; The results of the ANN approach were compared to an ARIMA and a pattern-based models for two different datasets.; The performance results proved GA optimized ANN model as an efficient approach for short-term UWD predictions.;Majid Gholami ShirkoohiMouna DoghriSophie DuchesneIWA Publishingarticleartificial neural networkgenetic algorithmhyperparameter optimizationshort termtime series modelurban water demand predictionWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 5, Pp 2374-2386 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural network genetic algorithm hyperparameter optimization short term time series model urban water demand prediction Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	artificial neural network genetic algorithm hyperparameter optimization short term time series model urban water demand prediction Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Majid Gholami Shirkoohi Mouna Doghri Sophie Duchesne Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm
description	The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a genetic algorithm (GA) and use of a growing window approach for training the model are also evaluated. The results are compared to those of commonly used time series models, namely the Autoregressive Integrated Moving Average (ARIMA) model and a pattern-based model. The evaluations are based on data sets from two Canadian cities, providing 15 min water consumption records over respectively 5 years and 23 months, with a respective mean water demand of 14,560 and 887 m3/d. The GA optimized ANN model performed better than the other models, with Nash–Sutcliffe Efficiencies of 0.91 and 0.83, and relative root mean square errors of 6 and 16% for City 1 and City 2, respectively. The results of this study indicate that the optimization of the hyperparameters of an ANN model can lead to better 15 min urban water demand predictions, which are useful for many real-time control applications, such as dynamic pressure control. HIGHLIGHTS ANN models were used for short-term (15 min) urban water demand predictions.; The hyperparameters of the ANN model were optimized with a genetic algorithm for better model performance.; The results of the ANN approach were compared to an ARIMA and a pattern-based models for two different datasets.; The performance results proved GA optimized ANN model as an efficient approach for short-term UWD predictions.;
format	article
author	Majid Gholami Shirkoohi Mouna Doghri Sophie Duchesne
author_facet	Majid Gholami Shirkoohi Mouna Doghri Sophie Duchesne
author_sort	Majid Gholami Shirkoohi
title	Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm
title_short	Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm
title_full	Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm
title_fullStr	Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm
title_full_unstemmed	Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm
title_sort	short-term water demand predictions coupling an artificial neural network model and a genetic algorithm
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/578f40e218044c579c04269965ef2c78
work_keys_str_mv	AT majidgholamishirkoohi shorttermwaterdemandpredictionscouplinganartificialneuralnetworkmodelandageneticalgorithm AT mounadoghri shorttermwaterdemandpredictionscouplinganartificialneuralnetworkmodelandageneticalgorithm AT sophieduchesne shorttermwaterdemandpredictionscouplinganartificialneuralnetworkmodelandageneticalgorithm
_version_	1718443795115147264

Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm

Ejemplares similares