Prediction of Physico-Chemical Parameters of Surface Waters Using Autoregressive Moving Average Models: A Case Study of Kis-Balaton Water Protection System, Hungary

• Published in: Water (Basel) Vol. 16; no. 16; p. 2314
• Main Authors: Kovács, Zsófia, Tarcsay, Bálint Levente, Tóth, Piroska, Csenge, Judit Juhász, Németh, Sándor, Shahrokhi, Amin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Accuracy; Biodiversity; continuous monitoring; Explicit knowledge; Hydrology; Monitoring systems; Sediments; sensors; Supervision; Surface water; Time series; time series regression; Trends; Water quality; Wetlands; Hungary; Lake Balaton
• ISSN: 2073-4441
• DOI: 10.3390/w16162314

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3).