Ⅰ. Background and Aims of Research
1. Research outline
o Research title: Development and application of an algal bloom forecast system using artificial intelligence deep learning technology
o Research period: January 1, 2020 ~ December 31, 2020
2. Necessity and purpose of research
o Limitations of the current algal bloom warning system
ㅇ The Ministry of Environment and the National Institute of Environmental Research implemented an algal bloom warning system based on the measured values ooof harmful blue-green algae and the EFDC model.
ㅇ Limitations of physics-based models
- They have a solid theoretical background but there is a difficulty in securing the detailed data required by the model.
- Since algal blooms are living organisms, the law of conservation of mass does not apply to the number of harmful blue-green algae cells. Therefore, the physics-based model has limitations.
- Deep learning-based forecasting can be considered as an alternative and a complementary method.

Ⅱ. Current Algal Bloom Response Policy
1. Algal bloom warning system
o Year of introduction: 1998
o Legal basis: Article 21 of the Water Environment Conservation Act
o Target
ㅇ 28 branches of water supply sources and hydrophilic activities
ㅇ Issuer: Basin Environmental Office and local governments
o Analysis items
ㅇ Measured numbers of harmful blue-green algae cells
ㅇ Based on water source section
- Attention: 1,000 (cells/mL) or more
- Alert: 10,000 (cells/mL) or more
- Large bloom: 1,000,000 (cells/mL) or more
ㅇ Based on hydrophilic activities section
- Attention: 20,000 (cells/mL) or more
- Al