As a developer,
I would like to use GEE machine learning functions (ee.Classifier) to find relationships between weekly cyan bloom concentration and various influencing parameters (precip, temp)
so that I can predict the likelihood cyan blooms will increase, decrease, or stay the same 1-4 weeks into the future.
Acceptance criteria: given a cyan timeseries (derived from geotif image collections), lake boundaries, and influencing parameters, I would expect a timeseries with future 1-4 week cyan concentration predictions.
time-dynamic datasets
PAR
Wind
precip
temp
longwave radiation
shortwave radiation
time-static datasets
percent landcover in watershed
size of watershed
lake size
lake bloom frequency
lake avg annual max CI
lake avg annual max bloom area
As a developer, I would like to use GEE machine learning functions (ee.Classifier) to find relationships between weekly cyan bloom concentration and various influencing parameters (precip, temp) so that I can predict the likelihood cyan blooms will increase, decrease, or stay the same 1-4 weeks into the future.
Acceptance criteria: given a cyan timeseries (derived from geotif image collections), lake boundaries, and influencing parameters, I would expect a timeseries with future 1-4 week cyan concentration predictions.
time-dynamic datasets PAR Wind precip temp longwave radiation shortwave radiation
time-static datasets percent landcover in watershed size of watershed lake size lake bloom frequency lake avg annual max CI lake avg annual max bloom area