Early detection and prediction of harmful algal blooms using low cost, networked IOT sensors and machine learning

Harmful Algal Blooms (HABs) are an increasing problem in waterways all over the world, costing an average of $17 billion in damages each each year. HABs have shut down water supplies to entire US cities in recent years. Blooms occur when nutrient-rich waters stimulate cyanobacterial growth, resulting in unsightly sludges that discolor waterways, rendering them dangerous to humans, liverstock and wildlife because of the cyanotoxins released. Cyanotoxins make HABs very costly to clean up; as the algae themselves must be removed, and the water purified before it is safe to drink, enter or even be close to because of the risk of toxin aerosolization by wind-blown spray. The treatments for HABs are expensive, environmentally damaging and of limited efficacy unless applied early in a bloom. The solution to HABs is early detection/prediction so that action can be taken at the earliest possible moment: reducing costs environmental and economic damage; and preserving access to clen drinking water. Floating sensor buoys are the answer, but the current marketplace is crowded with complex sensor platforms that can cost $30,000 per unit and which require specialist knowledge to deploy, use and maintain. These platforms are beyond the means of millions of small and medium sized stakehoulders who mange small lakes, reserviors, ponds or stretches of beach and who are often hardest hit by HABs. AquaRealTime was founded to provide a turnkey HAB monitoring solution for the small and medium sized stake-holder market, estimated at $900 million worldwide. Our innovative HAB sensor AlgeTracker, is affordable ($400), 8lbs in weight and can be deployed by a non-specialist in 30 minutes. AlgaeTracker also has an optimized detector suite that is best-in-class for HAB monitoring. And because AlgaeTracker transmits its data wirelessly over the cellular network and is accessed by a web-browser dashboard, it is convenient and easy to use. This grant aims to further develop the beta version of AlgaeTracker to make it ready for the markeplace. In addition, we propose a predictive analytics system that will use machine learning to analyze the data collected by all AlgaeTrackers and allow us to make HABs predictions 7 to 14 days in advance. If funded it'll create a commercial network of HAB sensors whose data can be sold to US Government agencies, and other entities with an interest in controlling HABs. This will save billions of dollars as treatments happen earlier, cost less and have fewer environmental effects.