Automated detection of anomalies in high-frequency water quality sensor data using machine learning

Wastewater treatment facilities are increasingly installing more and more high-frequency water quality sensors, as high-quality data is essential for plant operation and optimization. The sheer volume of data being collected and the necessity to avoid the collection of erroneous data, has created a need for automated tools to assess the quality of that data and signal for maintenance as the need arises. As these datasets have increased in size …