Harmful Algal Blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been working in devising a state-of-the-art monitoring and forecasting system for these HAB events. These modernized HAB systems provide useful and forewarning information to a varied user community. However, the lack of standardization in the data exchange mechanism with the current available systems causes an impediment to the wide area coastal observation and management. Hence, there is a need for the system to adapt the services oriented architecture and the OGC (Open Geospatial Consortium) sensor web enablement framework. We propose a HAB monitoring system by adopting the standardized OGC sensor web and using machine learning approaches for the detection of HAB events in the region of Gulf of Mexico. Various feature extraction techniques have been used in obtaining features of both HAB and Non-HAB data. Kernel based Support vector machines have been used as a classifier in the detection of HAB's. The performance of this approach is analyzed by accuracy measures like Kappa Coefficient, N-fold cross validation average and Confusion Matrix on a considerable test data.
['Balakrishna Gokaraju', 'Surya S. Durbha', 'Roger L. King', 'Nicolas H. Younan']
Sensor web and data mining approaches for Harmful algal bloom detection and monitoring in the Gulf of Mexico region