Predictive AI applications for estimating water quality constituents as causal factors of harmful algal blooms.
Description
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.
Detailed example
Estimates of water quality constituents: suspended sediment, total nitrogen, total phosphorus, algal pigments, and algal cell abundances.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Better understanding of the causes of harmful algal blooms in order for localities and state agencies to take proactive action to prevent harmful algal blooms that potentially have impact on life and property.
Controls / human review
ATO: No; PIA: No privacy use case