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

Ghazouli, Khalid El; Khattabi, Jamal El; Shahrour, Isam; Soulhi, Aziz

doi:10.2166/h2oj.2021.107

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Instantaneous dry weather flows at watershed outlets are predicted with a wastewater flow forecasting model (WWFFM). The WWFFM is an artificial neural network (ANN) black-box model that handles nonlinear problems taking real-time water consumption, and previous wastewater flow records as inputs (El Ghazouli et al 2021). The output of the model is a 5-h forecasted wastewater flow time series.…”

Section: Flow Forecasting In Sewer Networkmentioning

confidence: 99%

Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

Ghazouli

Khatabi

Soulhi³

et al. 2021

Water Science and Technology

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Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing.

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Section: Flow Forecasting In Sewer Networkmentioning

confidence: 99%

Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

Ghazouli

Khatabi

Soulhi³

et al. 2021

Water Science and Technology

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“…El Ghazouli et al. (2021) predicted the inflow using an autoregressive ANN with real‐time and predicted water consumption as well as infiltration flows as exogenous variables. Langeveld et al.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al (2019) combined Convolutional ANN and LSTM ANN in order to forecast WTP inflow chemical oxygen demand (COD) from temperature, pH, NH 3 -N, inflow rate and COD data. El Ghazouli et al (2021) predicted the inflow using an autoregressive ANN with real-time and predicted water consumption as well as infiltration flows as exogenous variables. Langeveld et al (2017) developed an empirical model for inflow quality prediction, modeling different water quality processes for individual inflow dynamics regimes.…”

mentioning

confidence: 99%

Probabilistic Intraday Wastewater Treatment Plant Inflow Forecast Utilizing Rain Forecast Data and Sewer Network Sensor Data

Sonnenschein,

Ziel

2023

Water Resources Research

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Forecasting of wastewater treatment plant inflow dynamics constitutes an enabler technology for wastewater treatment process optimization using model predictive control. However, accurate inflow prediction is still challenging, especially for strong rainfall events, where complex system dynamics and missing information on future rainfall represent limiting factors. We propose a seasonal probabilistic time series model for modeling the short‐term wastewater inflow accurately while providing quantification of forecast uncertainty. To ensure suitability for practical implementation, the unconstrained parameters of the predictive distribution are modeled as linear functions of the input variables in the framework of Generalized Additive Models for Location Scale and Shape. Non‐linear effects are approximated by Rectified Linear Units, accounting for buffering within the sewer network and flow‐dependent catchment response time. In addition to water level measurements from within the sewer network and rain rate measurements, rain forecasts are incorporated as exogenous regressors, where historical rain forecasts are used for model calibration. The model performance is evaluated on historical data from a German wastewater treatment plant using deterministic and probabilistic scoring rules. We benchmark against an autoregressive time series model and a long short‐term memory artificial neural network. Our results show that the proposed model unites the benefits of high prediction accuracy of the neural network and enhanced intelligibility of the autoregressive model, but accurate real‐time rain forecasts are mandatory for successful real‐world implementation.

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Tertiary treatment using ultrafiltration in an existing sewage treatment plant for industrial reuse: a modelling approach using artificial neural network with uncertainty estimation

Ramkumar,

Jothiprakash

2023

Water Reuse

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Navi Mumbai Municipal Corporation of Maharashtra state, India, unified a tertiary treatment plant (TTP) of 20 million litres per day (MLD) capacity with ultrafiltration technology in an existing Koparkhairane sewage treatment plant (STP) for producing effluent quality usable for industrial purposes. As prior art, an artificial neural network-genetic algorithm (ANN-GA) along with uncertainty estimation using prediction interval is employed to model secondary treated effluent (STE) flow rate (QT) and other quality parameters such as biochemical oxygen demand, chemical oxygen demand, and total suspended solids (TSS) to conclude the reliability of the range in which the input available to TTP. ANN-GA model provides a coefficient of determination above 0.90 for all STE parameters modelled other than TSS. Inferring that a good quantity and quality of 20 MLD STP treated water is currently available, where a decreasing trend of QT is also noticed and highlighted. Further, the Wilcoxon signed-rank test on the quality parameter of effluent TTP for industrial reuse standard infers TSS shows infringement during the initial period but started adhering to standards over time. The research delineates at the outset of exploring water reuse policy in India, emphasizing Maharashtra state, modelling STE using ANN-GA and performance evaluation of TTP.

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

Cited by 3 publications

References 27 publications

Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

Probabilistic Intraday Wastewater Treatment Plant Inflow Forecast Utilizing Rain Forecast Data and Sewer Network Sensor Data

Tertiary treatment using ultrafiltration in an existing sewage treatment plant for industrial reuse: a modelling approach using artificial neural network with uncertainty estimation

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