Research Information System
Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, vol.25, no.2, pp.360-370, 2025 (Peer-Reviewed Journal)
Wastewater flow estimation plays a key role for the accurate and efficient management of wastewater treatment plants. Inconsistent data and uncertainties arising from uncontrolled urbanization, population increases, excessive rainfall due to climate change and infrastructure deficiencies make wastewater flow forecasting difficult. In this context, the need to use effective forecasting models that will cover long-term trends has become evident. In this study, it is aimed to estimate the amount of wastewater flow for Samsun's East Advanced Biological Wastewater Treatment Plant with ARIMA, a time series analysis model, and artificial neural networks. Daily flow rate data corresponding to a period of one year were used and the performances of the models were compared in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values. ARIMA (2, 1, 2) model showed higher accuracy.