EPA Cyanobacteria Predictive App Challenge

August 26, 2013

Algae are natural components of marine and fresh water flora performing many roles that are vital for the health of ecosystems. However, excessive growth of algae becomes a nuisance to users of water bodies for recreation activities and to drinking water providers. Excessively dense algal growth could alter the quantity and quality of light in the water column. Some types of algae may also cause harm through the release of toxins. When conditions like light availability, warm weather, low turbulence and high nutrient levels are favorable, algae can rapidly multiply causing “blooms.” When blooms (or dense surface scums) are formed, the risk of toxin contamination of surface waters increases especially for some species of algae with the ability to produce toxins and other noxious chemicals. These are known as harmful algal blooms (HABs). This challenge will develop a predictive model (algorithm) based on the best available science to forecast the location of Cyano bloom events up to 30-days in advance of previously available satellite observation. The model will be based on the best and most appropriate scientific information available to predict the growth and movement of Cyano blooms. The first component is the predictive cyanobacteria modeling capability (which will be an algorithm) and will provide the ability to forecast the status of cyanobacteria bloom events corresponding to 7, 14 and 28 day intervals. The second component is the development of an Android application. The Android application will display water quality/color data and the predictive modeling outputs for cyanobacteria predictions in a visually appealing manner. For more information, please visit: http://www.topcoder.com/epa/