App gauges algae levels in lakes: Tool can predict future algae growth


The app can be used to create a graph.

University of Wisconsin-Parkside App Factory
Innovation: Algae Estimator App

Students perform experiments to test the model in the Algae App.
Students perform experiments to test the model in the Algae App.

When conditions are just right for toxic algae to bloom in a lake, the body of water not only begins to smell, but also can asphyxiate fish, induce rashes and vomiting in people, and in extreme cases kill pets and children.

Those consequences – cited by Derek Riley, Ph.D., assistant professor of computer science at the University of Wisconsin-Parkside – could be more easily prevented with a mobile application recently developed by UW-Parkside students in partnership with an international mix of other students.

The Algae Estimator app, a free app available on Android devices, gives lakeside homeowners and lake managers a tool to measure algae and toxic algae levels in a particular body of water. The app can also predict what a lake’s algae levels will look like in the future.

Regular algae, also known as green algae, and toxic algae, known as blue-green algae, thrive under different conditions – primarily different nutrient levels in lakes, according to Riley.

In general, algae grows in shallow waters and needs plenty of light and warm temperatures, Riley said.

Algae Estimator is designed to educate people about indicators that point to water quality, according to Riley, who is also co-director of UW-Parkside’s App Factory.

Those indicators include temperature of water, nutrient concentration, water depth and water clarity.

In order to understand water quality and measure algae levels in lakes, people have traditionally had to collect a sample of water, send it off to a laboratory and wait to hear back about results – a process that can take several days.

The app can be used to create a graph.
The app can be used to create a graph.

“But (with the Algae Estimator) even just a homeowner can take some basic measurements and get a sense of whether the algae is at a concerning level,” Riley said.

To evaluate a lake’s nutrient concentration – primarily its concentration of phosphate – an individual can use fish tank test strips, according to Riley.

To assess the clarity of a lake, an individual can test it with a tool known as a Secchi disk, which gauges how deep one can see into the water, he said.

Once an app user plugs each measurement into the Algae Estimator app, the tool reveals the estimated amount of algae currently in the lake, as well as a prediction of future growth.

If algae levels appear to sit at an alarming level, the app user then knows to send a water sample in to a lab for further evaluation.

Algae Estimator’s ability to project future levels of algae is unique to the app, according to Riley.

“We’re kind of applying weather forecasting to algae growth, which has never been done before to my knowledge,” he said.

Development of the app emerged out of an exchange program between UW-Parkside and Ostfalia University of Applied Sciences in Germany. Nearly two years ago, Riley traveled to the German school with UW-Parkside students to teach a course on biochemical systems modeling. The class pushed students to create and test a model for algae growth with help from Ostfalia faculty knowledgeable about algae and toxic algae patterns.

After collecting water samples in lakes in Germany and studying the algae in those lakes, students analyzed their findings and data in the classroom and devised a mathematical model to convey how fast algae grows.

That mathematical model was then applied to lakes in southeastern Wisconsin as German students in classes at UW-Parkside tested the model and developed an app for it.

The initial app development project involved a cross-sector of students from computer sciences, geosciences and biology.

In addition to German students and American students, app development incorporated input from Mexican and Indian students also studying at UW-Parkside through an exchange program.

As faculty and students have prepared to release the second version of the app, they have improved their mathematical model for algae growth and improved data collection methods. App developers have also started to apply crowdsourcing to the app so that when a user enters his or her algae data, it anonymously travels to a database. The hub of information will begin to produce a picture of algae growth across the world and help illuminate patterns in growth, Riley said.

As another cohort of students venture to Germany in May, they will work to continue enhancing the process of evaluating algae samples.

The students are slated to create a remote sampling device that will complete water sampling and communicate back results, potentially through texting, according to Riley.

Students’ front and center role in the Algae Estimator is among the components that have impressed him about its development.

“Most of the great innovations that exist in the app came because a student thought, ‘Hey, this would be nice to have,’” Riley said.

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