Artificial neural network for algal blooms

Incorporating factors such as turbulence, water temperature, turbidity, color, and nitrogen and phosphorus concentrations, a doctoral candidate at the University of Adelaide in South Australia has developed a computer model to predict outbreaks of blue‐green algae (cyano‐bacteria) in rivers weeks before they occur. Artificial neural networks (ANNs) used in the computer‐based modeling enable the model to learn key factors that can lead to an algal outbreak, according to Gavin Bowden, a student in the Department of Civil and Environmental Engineering who developed the model. ANNs, according to Bowden, attempt to imitate the complex, non‐linear, and parallel mechanisms involved in the interpretation of information by biological neural networks.