Wastewater flow forecasting model based on the nonlinear autoregressive with exogenous inputs (NARX) neural network - Archive ouverte HAL Access content directly
Journal Articles H2Open Journal Year : 2021

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

Abstract

Abstract 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.

Dates and versions

hal-03770701 , version 1 (06-09-2022)

Identifiers

Cite

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. H2Open Journal, 2021, 4 (1), pp.276-290. ⟨10.2166/h2oj.2021.107⟩. ⟨hal-03770701⟩
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