2021-02-18 All-Hands Presentation Meeting Notes

Owned by Luke Van Roekel
Last updated: Sept 09, 2021 by Renata McCoy

Presenter: Zhendong Cao (Unlicensed)

Title: A Machine Learning Approach to Extract Nutrient-Ocean State Relationship for Macroalgae Mariculture

Abstract: 

Macroalgae mariculture has the potential to mitigate harmful algal blooms in nutrient rich coastal
waters and provide a sustainable biofuel feedstock, as is being evaluated within the Advanced
Research Projects Agency–Energy Macroalgae Research Inspiring Novel Energy Resources
(MARINER) program. The evaluation of viable mariculture field sites requires a detailed understanding
of nutrients and ocean conditions on site. The fundamental challenge is that existing observational
and reanalysis data products do not have nutrient information for these purposes. Nutrients
computed within the US Department of Energy’s Energy Exascale Earth System Model (E3SM) are at
global to regional scale, but mariculture applications require nutrient information at field scale. To
bridge this gap, we build a nutrient prediction model based on calibrated ocean biogeochemistry
output from E3SM using a Random Forest Regression (RFR) model. The E3SM output includes the
ocean state variables of surface current velocities, temperature, salinity and biogeochemical
variables like nutrients at a three hour frequency. The RFR model is trained by daily data to
estimate nitrate for the rest of the variables. The optimized RFR model, fine-tuned by grid search
technique, shows great performance in predicting the spatio-temporal nitrate distribution, with an
Out-of-Bag score of 0.97 in model calibration and Goodness-of-Fitting of R2 = 0.96 in model
verification. These results highlight key factors responsible for nutrient spatio-temporal variability
and will be used to approximate nutrient concentrations for remote sensing data and in higher
resolution reanalysis products, e.g., HYCOM ocean model forecasts.


 

Time

  • PT: 8:30 am
  • ET: 11:30 am

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Attendees

Presentation

Time
Title
Presenter
Presentation
Recording
Notes

30 min


A Machine Learning Approach to Extract Nutrient-Ocean State Relationship for Macroalgae MaricultureZhendong Cao (Unlicensed)