Machine Learning in Forecasting the Amount of Sewage for the Wastewater Treatment Plant of the City of Rzeszów Based on Weather Conditions

The parameters of wastewater treatment plants in Poland must meet certain standards that regulate the quality of wastewater after the treatment process. The quantity and quality of incoming sewage depend on many factors, including weather conditions. Forecasting these parameters can ensure optimal operation of the treatment plant, which will reduce operating costs. For this purpose, using weather data, an attempt was made to estimate the amount of sewage flowing into the sewage treatment plant in Rzeszow. Over 1000 machine learning (ML) models were used, including statistical models such as ARIMA and SARIMAX, and ML algorithms such as KNN and neural networks, in various configurations and time frames. The lowest mean absolute error (MAE) of 3598 m3 and the root mean square error (RMSE) of 4808 m3 were obtained. The study showed how the selection of parameters and different types of predictive models (static, dynamic, machine learning) affects forecast accuracy. It also highlighted that forecasting based solely on basic time-series data is a challenging process.