Prospects in Wastewater Management Using Deep Learning

The failure of devices in Wastewater Pumping Stations (WWPS) presents significant environmental and financial challenges. Sensors in WWPS, coupled with the Supervisory Control And Data Acquisition (SCADA), generate real-time data for device management. One-third of lift pumps experience intermittent, often undetected, electrical or mechanical issues. Energy costs for WWPS account for 20-30% of the overall budget, with repairs at 10-15%. Utilizing deep learning and energy-efficient systems can reduce costs by 15-20%.

This study aims to categorize deep learning methods to detect anomalies in this data, preventing intrusion attacks, false alarms, valve shutdowns, unexpected water levels, and effectively managing equipment. Few methods specifically address anomaly detection in WWPS. This study proposes an innovative categorization based on six criteria: machine learning, predictive autoregression, hybridization, performance, statistical classification, and clustering. Four detection methods were examined, highlighting their advantages and limitations, to improve municipal managers' decision-making and prevent destructive breakdowns.

To our knowledge, this is the first study to consider anomaly detection methods in WWPS in this specific manner, facilitating their identification and repair. It shows that the future of anomaly detection lies in hybridizing four main methods: autoencoders (AE), variational autoencoders (VAE), long short-term memory (LSTM), and convolutional neural networks (CNN).

Deep learning algorithms can continuously monitor wastewater parameters like water levels, flows, velocity, and pressure. They can predict failures and maintenance needs by analyzing data from sensors and historical records, reducing downtime and extending equipment lifespan, ensuring immediate anomaly detection and rapid corrective actions.

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