Anacostia Riverkeeper Uses DataRobot to Predict Water Quality in the Anacostia River
Region
- America
Country
- United States
Product
- DataRobot
Tech Stack
- DataRobot
- Tableau
Impact Metrics
- Environmental Impact Reduction
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Cities & Municipalities
Use Cases
- Predictive Quality Analytics
Services
- Data Science Services
About The Customer
Anacostia Riverkeeper is a nonprofit organization based in Washington, DC. Their mission is to protect and restore the Anacostia River for all who live, work, and play in its watershed, and to advocate for a clean river for all its communities. The Anacostia River runs through the heart of Washington, DC and parts of Maryland. Like many urban waterways, it is heavily polluted, and swimming has been illegal since the 1970s due to health concerns about pollution.
The Challenge
Anacostia Riverkeeper is a nonprofit organization dedicated to protecting and restoring the Anacostia River, which runs through Washington, DC and parts of Maryland. The river is heavily polluted, and swimming has been illegal since the 1970s due to health concerns about pollution. The current methods for testing water quality take days to return results, creating a delay between when the water is tested and when the results are shared with the public. Moreover, water quality can rapidly change with weather conditions, such as rain, making test results outdated before they’re even returned. Anacostia Riverkeeper needed a more efficient and timely way to monitor and predict water quality in the Anacostia River.
The Solution
Through DataRobot’s AI for Good program, Anacostia Riverkeeper partnered with DataRobot to develop a system to predict if E. coli levels are above safe levels. The team collected data about the discharge, gauge height, temperature, and more from 28 sensors at different locations in the Anacostia River from the United States Geological Survey (USGS). These features were aggregated over the preceding 12 and 24 hours to capture a historical sense of the river’s conditions. The team used DataRobot to build and train dozens of binary classification models, and then selected and deployed the best model. The solution engineers then built a script that pulls the USGS sensor data, aggregates it, and sends it to DataRobot for scoring. The results are stored in a database and visualized using Tableau.
Operational Impact
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