Summary of Evaluating Deep Learning Approaches For Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models, by Jared D. Willard et al.
Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models
by Jared D. Willard, Fabio Ciulla, Helen Weierbach, Vipin Kumar, Charuleka Varadharajan
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Atmospheric and Oceanic Physics (physics.ao-ph)
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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 Deep learning models have shown great promise in predicting environmental variables like streamflows and temperatures at large spatial scales, but there are still open questions about the best approach to use. This study explores these questions by comparing different machine learning (ML) model designs and evaluating their performance on a dataset of stream temperature predictions across the conterminous United States. The results suggest that top-down models, which utilize data from a large number of basins, significantly outperform bottom-up and grouped models. Additionally, it is possible to achieve acceptable accuracy by reducing both dynamic and static inputs, enabling predictions for more sites with lower model complexity and computational needs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how machine learning can be used to predict stream temperatures in areas where there isn’t much data. Researchers compared different ways of building these models and found that one approach, called top-down, works really well. This means that by using data from many different places, you can make pretty accurate predictions even in areas with limited information. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Temperature
