New York Improves Water Data Quality With New Machine Learning Technology

The water supply system for New York City provides drinking water to almost half the state’s population, which includes over 8.5 million people in the city and 1 million people in upstate counties. New York’s Catskill/Delaware System is one of the world’s most extensive unfiltered surface water supplies. The city’s water is supplied from a network of 19 reservoirs and three controlled lakes that contain a total storage capacity of approximately 570 billion gallons.

The reservoir levels are primarily determined by the balance between streamflow into the reservoirs, diversions (withdrawals) for water supply, and releases to maintain appropriate flows in the rivers below the dams.

The New York City Department of Environmental Protection (NYCDEP) pulls sensor data from 445 locations every five minutes to monitor the safe flow of water. Some of these locations have multiple sensors measuring hydrological data such as water levels, dissolved oxygen, temperature, pH, turbidity, etc. All this data, as well as U.S. Geological Survey and National Weather Service information, is managed in Aquarius, an analytics software platform that water monitoring agencies around the world use to acquire, process, model, and publish water information in real-time. The data is used for operational modeling and daily awareness of what is happening in the system, so having reliable information is essential.

Data quality can be affected by various factors, such as faulty sensors, measurement errors, missing values, outdated information, or anomalies caused by maintenance, resulting in an unusual spike or a drop in data. Poor quality data in water flow management can lead to inaccurate predictions, inefficient operations, and water quality issues. To ensure reliable and effective water flow management, quality data that reflects the current and future conditions of the water system is essential.