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Cutting-edge AI system predicts changes in water quality before they happen
- A powerful combination of artificial intelligence, sensor data, and streamflow models enables researchers to predict changes in water quality before they occur, aiding water systems in anticipating threats like sediment spikes, algal blooms, and fertilizer runoff.
- Scientists at the University of Vermont have demonstrated the feasibility of predicting water quality using AI and the National Water Model, which traditionally forecasts water flow patterns.
- By applying machine learning and sensor data to the NWM, the researchers developed a system capable of predicting turbidity levels in water bodies, such as the Esopus Creek in New York.
- Turbidity, caused by sediment runoff during storms, presents a significant challenge for water sources like the Ashokan Reservoir that supplies a significant proportion of New York City's drinking water.
- The AI model forecasts turbidity levels up to three days in advance with high accuracy, offering insights into how varying streamflow patterns influence water clarity.
- This innovative approach transforms a streamflow forecasting tool into a water quality forecasting tool, potentially benefiting communities nationwide facing water quality issues.
- The success of the model in the Catskills suggests its broader applicability across the U.S., where many communities grapple with water quality concerns and could leverage such forecasting tools.
- The integration of the model with local sensor data allows for tailored predictions, enabling water treatment plants, health departments, and farmers to proactively address water quality challenges.
- This collaborative effort between data science and environmental science showcases the potential of predictive models to mitigate health risks, safeguard ecosystems, and optimize water management practices.
- Findings of the study can be accessed online in the Journal of the American Water Resources Association, pointing towards a promising future for water quality forecasting and resource management.
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