Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network

Wastewater flow forecasts are key components in the short- and long-term management of sewer systems. Forecasting flows in sewer networks constitutes a considerable uncertainty for operators due to the nonlinear relationship between causal variables and wastewater flows. This work aimed to fill the...

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Autores principales:	Khalid El Ghazouli, Jamal El Khattabi, Isam Shahrour, Aziz Soulhi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	artificial intelligence narx neural network sewer network urban drainage system wastewater flow forecast River, lake, and water-supply engineering (General) TC401-506 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/acaba128efb44022aa99f9859b5b0891
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spelling	oai:doaj.org-article:acaba128efb44022aa99f9859b5b08912021-11-08T07:59:23ZWastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network2616-651810.2166/h2oj.2021.107https://doaj.org/article/acaba128efb44022aa99f9859b5b08912021-01-01T00:00:00Zhttp://doi.org/10.2166/h2oj.2021.107https://doaj.org/toc/2616-6518Wastewater flow forecasts are key components in the short- and long-term management of sewer systems. Forecasting flows in sewer networks constitutes a considerable uncertainty for operators due to the nonlinear relationship between causal variables and wastewater flows. This work aimed to fill the gaps in the wastewater flow forecasting research by proposing a novel wastewater flow forecasting model (WWFFM) based on the nonlinear autoregressive with exogenous inputs neural network, real-time, and forecasted water consumption with an application to the sewer system of Casablanca in Morocco. Furthermore, this research compared the two approaches of the forecasting model. The first approach consists of forecasting wastewater flows on the basis of real-time water consumption and infiltration flows, and the second approach considers the same input in addition to water distribution flow forecasts. The results indicate that both approaches show accurate and similar performances in predicting wastewater flows, while the forecasting horizon does not exceed the watershed lag time. For prediction horizons that exceed the lag time value, the WWFFM with water distribution forecasts provided more reliable forecasts for long-time horizons. The proposed WWFFM could benefit operators by providing valuable input data for predictive models to enhance sewer system efficiency. HIGHLIGHTS Implementation of a novel wastewater flow forecasting model based on the NARX neural network.; New tool for flow forecasting in urban drainage catchments.; The wastewater flow forecasting model provides accurate input data for predictive modeling.;Khalid El GhazouliJamal El KhattabiIsam ShahrourAziz SoulhiIWA Publishingarticleartificial intelligencenarx neural networksewer networkurban drainage systemwastewater flow forecastRiver, lake, and water-supply engineering (General)TC401-506Water supply for domestic and industrial purposesTD201-500ENH2Open Journal, Vol 4, Iss 1, Pp 276-290 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence narx neural network sewer network urban drainage system wastewater flow forecast River, lake, and water-supply engineering (General) TC401-506 Water supply for domestic and industrial purposes TD201-500
spellingShingle	artificial intelligence narx neural network sewer network urban drainage system wastewater flow forecast River, lake, and water-supply engineering (General) TC401-506 Water supply for domestic and industrial purposes TD201-500 Khalid El Ghazouli Jamal El Khattabi Isam Shahrour Aziz Soulhi Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network
description	Wastewater flow forecasts are key components in the short- and long-term management of sewer systems. Forecasting flows in sewer networks constitutes a considerable uncertainty for operators due to the nonlinear relationship between causal variables and wastewater flows. This work aimed to fill the gaps in the wastewater flow forecasting research by proposing a novel wastewater flow forecasting model (WWFFM) based on the nonlinear autoregressive with exogenous inputs neural network, real-time, and forecasted water consumption with an application to the sewer system of Casablanca in Morocco. Furthermore, this research compared the two approaches of the forecasting model. The first approach consists of forecasting wastewater flows on the basis of real-time water consumption and infiltration flows, and the second approach considers the same input in addition to water distribution flow forecasts. The results indicate that both approaches show accurate and similar performances in predicting wastewater flows, while the forecasting horizon does not exceed the watershed lag time. For prediction horizons that exceed the lag time value, the WWFFM with water distribution forecasts provided more reliable forecasts for long-time horizons. The proposed WWFFM could benefit operators by providing valuable input data for predictive models to enhance sewer system efficiency. HIGHLIGHTS Implementation of a novel wastewater flow forecasting model based on the NARX neural network.; New tool for flow forecasting in urban drainage catchments.; The wastewater flow forecasting model provides accurate input data for predictive modeling.;
format	article
author	Khalid El Ghazouli Jamal El Khattabi Isam Shahrour Aziz Soulhi
author_facet	Khalid El Ghazouli Jamal El Khattabi Isam Shahrour Aziz Soulhi
author_sort	Khalid El Ghazouli
title	Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network
title_short	Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network
title_full	Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network
title_fullStr	Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network
title_full_unstemmed	Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network
title_sort	wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (narx) neural network
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/acaba128efb44022aa99f9859b5b0891
work_keys_str_mv	AT khalidelghazouli wastewaterflowforecastingmodelbasedonthenonlinearautoregressivewithexogenousinputsnarxneuralnetwork AT jamalelkhattabi wastewaterflowforecastingmodelbasedonthenonlinearautoregressivewithexogenousinputsnarxneuralnetwork AT isamshahrour wastewaterflowforecastingmodelbasedonthenonlinearautoregressivewithexogenousinputsnarxneuralnetwork AT azizsoulhi wastewaterflowforecastingmodelbasedonthenonlinearautoregressivewithexogenousinputsnarxneuralnetwork
_version_	1718442842342293504

Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network

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