Summary of Applying Ranking Techniques For Estimating Influence Of Earth Variables on Temperature Forecast Error, by M. Julia Flores et al.
Applying ranking techniques for estimating influence of Earth variables on temperature forecast error
by M. Julia Flores, Melissa Ruiz-Vásquez, Ana Bastos, René Orth
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper proposes a novel approach to analyzing the impact of Earth system variables on temperature forecast errors by introducing three key novelties: using data science techniques at representative locations, enriching Spearman correlation rankings with additional metrics for robustness, and evaluating methodology through random forest regression models. The main contribution is a framework converting correlations into rankings and combining them into an aggregate ranking. Experiments are conducted on five locations to analyze the behavior of this ranking-based methodology, which shows promising results in improving performance for Random Forest models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve temperature forecast accuracy by understanding how Earth system variables affect errors. The study takes a unique approach by using data science methods at specific locations and combining rankings from different metrics. This helps identify the most important variables influencing errors. The results show that this method performs well in certain locations and seasons, making it a promising technique for real-world applications. |
Keywords
* Artificial intelligence * Random forest * Regression * Temperature
