Safe to Swim

Problem Statement

Despite a wealth of water quality information for Vermont's waterways, it remains awkward to answer the simple question of "can we go swimming at the beach?" Some data is highly reliable but quickly becomes outdated. Other data sources have the reverse problem. And most online resources are difficult to find or use, particularly on a mobile device. The goal of this project is to help the public access a user-friendly application from any device that provides real time information about whether the beach they want to visit is safe to swim in.

Project Summary

The original suggestion for this project came from a casual conversation with BTV Ignight and the City of Burlington.  There are a number of resources that already try to address this problem, but the ones I've seen fall short of an elegant, comprehensive solution:

It is likely that the necessary data may be readily available.  The UVM Rubinstein school is another potential source of reliable on water quality data.  With some coordination and effort this could be mapped and quickly prototyped.

Current Status

Seeking Project Partners

Currently we do not have a organizational partner working with Code for BTV on this project.  In order to ensure that our effort results in tools that serve a real need within the community, all our projects must have an external partner vetting and directing the work.  Project partners can be any not-for-profit organization.  Project partners provide the expert knowledge surrounding the problem, and verify the direction of the project.  One of the project partners will ultimately end up hosting and maintaining any digital tools that are produced, with assistance & direction from Code for BTV.

If you or someone you know would be a suitable project partner, please contact projects@codeforbtv.org.

Protyping Image Analysis Datasource

The data that is currently available tends to be highly reliable, but frequently out of date.  We are exploring the posibility of creating a new datasource that has the opposite properties: trading off some reliability for availability.  The Image Analyzer prototype is a machine learning approach that takes images submitted via a simple mobile app and attempts to apply machine learning approaches to determine whether an image represents an algae bloom.  

This project will begin simply, and then we will explore the extent to which actionable data can be sourced from images.  Ultimately the hope is that this method can provide an additional, potentially real-time datasource to assist in determining whether an algea bloom exists in a given place and time.

This project already shows promise in it's ability to distinguish algae blooms from other false-positives.  Development of the photo-recognition algorythm has been informed by the following papers to varying degrees: