A case study on long short-term memory-based modeling for predicting chlorine and free ammonia levels at water treatment plant testing sites

ABSTRACT This work explores the accurate prediction of water quality, a critical aspect of water environment protection. Leveraging advanced algorithms and statistical models, including regression analyses, moving average, and artificial neural networks (ANN), we analyze multivariate time-series datasets from tap water testing sites. Our approach involves model fitting and evaluation based on the Akaike information criterion (AIC) and root mean square error (RMSE). The work introduces the long short-term memory (LSTM) neural network and the seasonal autoregressive integrated moving average (SARIMA) model, detailing their architectures and implementations. Results highlight the effectiveness of a multivariate LSTM model in forecasting total chlorine concentrations, complemented by the SARIMA model. Evaluation metrics reveal comparable predictive accuracy, with a slight advantage observed for the mixed method combining both models. This work underscores the significance of integrating technological and statistical methodologies for enhanced water quality predictions, contributing to proactive water management strategies.