Predictive AI applications for estimating water quality constituents as causal factors of harmful algal blooms.

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Ensemble regressions to predict suspended sediment, total nitrogen, total phosphorus, algal pigments, and algal cell abundances and image-based estimation of suspended sediment concentration via machine learning used in combination with custom developed python code to help build on our correlative understanding of what drives harmful algal blooms and introduces a technique to better understand and attribute causality.