Presentation Title

Early Detection of Blue Green Algae Blooms Using Artificial Intelligence

University

Shawnee State University

Major

Digital Simulation and Gaming Engineering Technology

Student Type

Undergraduate Student

Presentation Types

Oral Group Presentation

Abstract

Chronic blue-green algae blooms in drinking water and ecosystems consume oxygen and produce nitrogen at levels toxic to the environment and its inhabitants, including humans who rely on clean drinking water. We describe a neural network-based artificial intelligence which aims to find the algae blooms early enough to prevent or mitigate the effects of such blooms. We further discuss the training procedure of the neural network used to detect algae blooms as early as possible. As an added benefit for remote or economically distressed regions, the detection equipment needed for implementation is simple to use and extremely cost effective.

We conclude with further potential applications of the technique to a variety of problems related to water testing.

Human Subjects

no

Faculty Mentor Name

R. Duane Skaggs

Faculty Mentor Title

Associate Professor

Faculty Mentor Academic Department

Engineering Technologies

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Early Detection of Blue Green Algae Blooms Using Artificial Intelligence

Chronic blue-green algae blooms in drinking water and ecosystems consume oxygen and produce nitrogen at levels toxic to the environment and its inhabitants, including humans who rely on clean drinking water. We describe a neural network-based artificial intelligence which aims to find the algae blooms early enough to prevent or mitigate the effects of such blooms. We further discuss the training procedure of the neural network used to detect algae blooms as early as possible. As an added benefit for remote or economically distressed regions, the detection equipment needed for implementation is simple to use and extremely cost effective.

We conclude with further potential applications of the technique to a variety of problems related to water testing.