Description
Main TitleForecasting Algal blooms in Surface Water Systems with Artificial Neural Networks: Final Report
PublisherNOAH L.L.C.
Date Created2006-03-01
Subject (Geographic - Hierarchical)
Country: United States
State: New Jersey
County: NA
Subject (Topical)community awareness; contaminated sites; contamination; environmental data; environmental education; environmental funding; environmental impact; environmental monitoring; environmental technologies; occupational/environmental health; pollution; algal blooms; cyanobacteria (blue-green algae); chrysophytes; chlorophytes; artificial neural network technology (ANN)
DescriptionAlgal blooms (AB) in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. AB events can cause taste and odor problems, damage the environment, and some algal classes like cyanobacteria (blue-green algae) may release toxins that can cause human illness or even death. There is a need to develop models that can accurately forecast algal bloom events on the basis of predictive physical, meteorological, chemical, and biological information. Such forecasting models can provide valuable lead time for water treatment systems to implement measures to minimize the consequences of the AB event, if not actually prevent it. Given the multitude, interplay, and complexity of the various controlling environmental factors, modeling and forecasting AB is a daunting challenge. This research focused on the feasibility of using artificial neural network (ANN) technology as an accurate, realtime modeling and forecasting tool. Previously-collected data from a NJ water utility served as the test case. AB forecasting periods included one-week and two-weeks prior to the event. Despite a less than ideal number of historical AB events, the high predictive accuracy achieved in this study indicates that with sufficient data, both in terms of the number of historical AB events and availability of important predictor data, ANNs can serve as reliable, accurate, real-time AB forecasting tools.
NoteThe research project summary and appendix associated with this study can be found at the Document URL.
Note'With participation of: New Jersey American Water Company and Passaic valley Water Company' and 'Sponsored by: New Jersey Department of Environmental Protection'
Genrereports
Organization NameRutgers University Libraries
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