LG Sonic will make use of Artificial Intelligence (AI) to further develop the software algorithm which will make it possible to predict algal blooms based on water quality data automatically. Furthermore, it will provide detailed information about the intensity and the impact of the algal bloom on the ecosystem of the water body. The updated algorithm will be implemented in LG Sonic’s water quality software, MPC-View.
Predicting algal blooms has been done manually so far by LG Sonic water quality experts by analysing the water quality data combined with Remote Sensing images and meteorological parameters. The updated algorithm will be able to automatically predict algal blooms based on the water quality data stored in the MPC-View software. The software receives its data from the MPC-Buoy, a floating solar-powered system that combines real-time water quality monitoring and ultrasonic sound waves to control algae effectively. MPC-View receives water quality parameters related to phytoplankton dynamics such as Chlorophyll-a, Temperature, DO, pH, and Turbidity which are all essential for the prediction of (harmful) algal blooms.
Importance of algae prediction
Algal blooms can cause health threats to humans and animals; furthermore, it disturbs the whole ecosystem of the water. Algae reduce light penetration, deplete oxygen and release toxins. Such blooms should be prevented as they cause unfavourable conditions. However, many contributors can cause a rise in algal trends, and once an algae bloom is visible, it is more difficult to treat, and the water ecosystem will already be harmed.
Data driven water treatment
LG Sonic combines water quality and ultrasound technology to provide a complete algae solution for large water surfaces. LG Sonic has been gathering water quality information for many years in different water bodies all over the world. At the moment, LG Sonic is running projects in amongst other countries, such as Argentina, Chile, England, Singapore, Belgium and the United States.