Korea Institute of Civil Engineering and Building Technology has successfully developed a real-time, low-cost algal bloom monitoring system utilizing inexpensive optical sensors and a novel labeling logic. The system achieves higher accuracy than state-of-the-art AI models such as Gradient Boosting and Random Forest. The findings are published in the journal Environmental Monitoring and Assessment.
Affordable sensor system detects algal bloom in real time
