Forecasting and Early Warning System for Wastewater Treatment Plant Sensors Using Multitask and LSTM Neural Networks: A Simulated and Real-World Case Study

Abstract

The increasing global water scarcity has made the safe reuse of treated wastewater essential, especially in agriculture, where untreated water poses risks to public health. Digitalizing Wastewater Treatment Plants (WWTPs) can enhance real-time water quality monitoring and optimize plant operations. This study implements an Early Warning System (EWS) at the Peschiera Borromeo WWTP in Milan, Italy, using predictive models based on simulated and real datasets to estimate key water quality parameters like Chemical Oxygen Demand (COD) and Total Suspended Solids (TSS). A Multi-Task Learning (MTL) neural network provided real-time predictions and sensor malfunction detection, while a Long Short-Term Memory (LSTM) network forecasted water quality up to six hours ahead. Simulated data showed high correlation coefficients above 0.98, but real-world data reduced performance to 0.31–0.67. Despite this, the EWS shows strong potential for improving treated water reuse reliability and operational efficiency in WWTPs.