The City of Chicago's Clear Water project brings an innovative approach to beach water quality monitoring. It uses a machine learning prediction technique to better forecast the bacteria levels at Chicago beaches. The model works by interpreting patterns in the results of DNA tests at a handful of beaches across the City, which are then extrapolated to forecast the water quality at other, untested beaches. This method provides a new way for beach managers to save money on expensive rapid water quality tests.

Initial evaluation of the model has shown a significant improvement over current methods of predicting beach water quality. Testing is ongoing, and the 2017 beach season is being analyzed to further improve and evaluate the model's performance.

This project uses R, which you can download here. We recommend you also install RStudio. You can open this project in RStudio by opening the clear-water.Rproj file.

If you are new to R, check out some basics here.

To generate the model, open the Master.R file. Inside the file, you will see settings that you can tweak to change the predictors and other facets of the model. Once you're ready, run all the code in the file. If you've successfully generated the model, you'll see ROC and Precision/Recall plots appear in RStudio. You'll also have a Data Frame in R called model_summary that contains the results of the model evaluation.

This repo is one of two GitHub repos that make up the Clear Water project. The other one can be found here and is an application that automatically generates water quality predictions based on daily DNA test results that are published on Chicago's Data Portal.

If you are interested in contributing to this project, see our Contribution Guide.

This project began as an attempt to improve on a model that was produced by the United States Geological Survey (USGS). As part of model development, an embedded feature was engineered that provides a way to do an A/B model peformance comparison against the USGS's model performance.

This feature can be switched on and off, and another model could be substituted for the USGS model to do an A/B evaluation.

This project originated as a breakout group at Chi Hack Night.

• Repository: https://github.com/Chicago/clear-water
• Issue tracker: https://github.com/Chicago/clear-water/issues
• Project management board: https://waffle.io/Chicago/clear-water
• Documentation and notes: https://github.com/Chicago/clear-water/wiki

Copyright (c) 2015 City of Chicago

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