EGU24-21973, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Back Propagation (BP) Neural Network for a Short-term Forecasting Tool in Wastewater Treatment Plant Influent

Wenchuang Zhang1, Eoghan Clifford3, and Páraic Ryan1,2
Wenchuang Zhang et al.
  • 1Discipline of Civil, Structural and Environmental Engineering, School of Engineering, University College Cork, Ireland
  • 2Environmental Research Institute, University College Cork, Cork, T23 XE10, Ireland
  • 3School of Engineering, University of Galway, H91 TK33 Galway, University road, Galway, Ireland

Influent flow volumes play a crucial role in the management and operation of wastewater treatment plants (WWTP). In regions like Europe, characterized by abundant rainfall and aging wastewater systems, it is common to discharge stormwater into the sewerage system, i.e.  the use of combined sewer systems in urban areas, employing a single pipe for transporting both rainfall and wastewater. Consequently, large amounts of rainfall can cause WWTP to become overloaded, which can lead to wastewater overflows i.e. discharge of untreated wastewater into receiving water bodies. This can have a significant impact on the surrounding environment, damaging ecological systems hindering the use of public amenities such as beaches. Recently researchers have used machining learning methods to predict WWTP influent volumes to help manage these issues. There are however a number of outstanding challenges such as a lack of parameter selection processes, high likelihood of incorporating noise information with the intervention of more input data and the existence of model uncertainty.  This study aims to use Back Propagation Neural Network (BPNN) combined with a sensitivity analysis to help address these existing shortcomings, and provide a short-term forecasting tool which seeks to provide WWTP managers and operators ban eastly warning of potential overflow events. Gaussian Process Regression (GPR) has also been applied herein to provide probabilistic estimates of predictions. This provides information on the level of confidence associated with the predicted values by assessing the uncertainty of the model.

In this research, the input variables from five aspects have been considered: i) an energy-water balance model; ii) infiltration; iii) the effect of seasonal variation; iv) the influence of changes in tidal and river level; v) lag effects. Different combinations of input variables were used using recorded weather data and wastewater influent data for a WWTP in Ireland. In Group 1, daily precipitation, previous daily precipitation, max air temperature, soil moisture deficit, groundwater level and seasonal index have been used to predict WWTP influent, with a resulting   value of 0.68. Group 2 considers daily precipitation, previous daily precipitation, soil moisture deficit, tidal level and river level, with an  of 0.79. Then we added previous daily influent into group 1 and 2, respectively, having the same result, with the value of  equalling to 0.88. These results provide new insights for timely warning of influent variations and potential overflow, improve the practicality of machine learning in WWTP.

How to cite: Zhang, W., Clifford, E., and Ryan, P.: Back Propagation (BP) Neural Network for a Short-term Forecasting Tool in Wastewater Treatment Plant Influent, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21973,, 2024.