Summary of Harmful Algal Bloom Forecasting. a Comparison Between Stream and Batch Learning, by Andres Molares-ulloa et al.
Harmful algal bloom forecasting. A comparison between stream and batch learning
by Andres Molares-Ulloa, Elisabet Rocruz, Daniel Rivero, Xosé A. Padin, Rita Nolasco, Jesús Dubert, Enrique Fernandez-Blanco
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators seeking to predict Diarrhetic Shellfish Poisoning (DSP) outbreaks can rely on a novel machine learning workflow developed by researchers. The study tackles the challenge of predicting harmful algal blooms (HABs), which are linked to DSP, using a combination of historical data and advanced algorithms. Seven machine learning models were compared within two paradigms: Batch Learning and Stream Learning. The latter proved promising in addressing time-series-based problems with concept drifts. Notably, the study employed ocean hydrodynamic model CROCO as primary dataset, overcoming limitations of traditional data collection methods. The most effective predictor emerged as DoME, achieving an average R2 of 0.77 in 3-day-ahead predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting shellfish toxins that cause Diarrhetic Shellfish Poisoning is crucial for public health and the industry. Scientists developed a new way to do this by combining old data with special computer programs. They tested many different models to see which one worked best. One model, called DoME, did really well and can predict when there will be toxins in shellfish three days ahead of time. |
Keywords
* Artificial intelligence * Machine learning * Time series
