Improved Harmful Algal Blooms Prediction with Hybrid Models
This grant opportunity seeks to address the lack of developed methods to quantify and predict harmful algal blooms (HABs) in lakes and reservoirs, which can lead to water quality issues and costly emergencies. Current detection methods are limited and do not effectively diagnose the causes of HABs. The project aims to create a hybrid modeling system combining multiple types of models to improv…
Methods to quantify and predict vulnerability to harmful algal blooms (HABs) has not been developed for most lakes and reservoirs in the U.S. (and the world). This limits the ability for water quality stakeholders to 1) avoid costly emergency events, 2) efficiently design source water monitoring…
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