Forecasting wastewater flows and pollutant loads: A comparison of data-driven models within the urban water system framework.

The ability to forecast key features of wastewater treatment plant (WWTP) influent, is emerging as an important tool to enable advanced WWTP operational strategies. Various data driven models have been reported in the scientific literature for WWTP influent forecast however, there has been no quantitative comparison of them against each other. The present study is the first to undertake this comparison utilising a high-frequency reference dataset generated from the Urban Water System model. Specifically, the performance of autoregressive models was compared against time-delay networks, nonlinear autoregressive networks and long short-term memory networks. Time-delay networks were generally found to outperform the other tested methods, although the reliability of the generated forecasts decreases as the prediction horizon exceeds one hour. While longer prediction horizons would be desirable, there is a trade-off between model accuracy and overall optimisation of plant operation. This study also highlights the challenge of dealing with both concentration and flow variations suggesting the need for future research to analyse the separate impacts of flow and concentration variations on model performance. • Data driven models can forecast WWTP influent flows and composition. • time delay neural networks achieved the highest average FIT and correlation factors. • using mass load data rather than concentration data gave better predictions. • model predictions improved by including total suspended solids data. [ABSTRACT FROM AUTHOR]