Summary of Random Forest Regression Feature Importance For Climate Impact Pathway Detection, by Meredith G. L. Brown et al.
Random Forest Regression Feature Importance for Climate Impact Pathway Detection
by Meredith G. L. Brown, Matt Peterson, Irina Tezaur, Kara Peterson, Diana Bull
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 technique to identify and rank the chain of spatio-temporal downstream impacts of a climate source, referred to as a source-impact pathway. This is achieved by utilizing Random Forest Regression (RFR) and SHapley Additive exPlanation (SHAP) feature importances in a fundamentally new workflow. The authors train random forest regressors on spatio-temporal features of interest, calculate pair-wise feature importances using SHAP weights, and translate these importances into a weighted pathway network. This allows for tracing out and ranking interdependencies between climate features and/or modalities. The paper applies this methodology to ensembles of data generated by running two benchmarks: synthetic coupled equations and a fully coupled simulation of the 1991 eruption of Mount Pinatubo in the Philippines performed using E3SMv2. The results show that the RFR feature importance-based approach can accurately detect known pathways of impact for both test cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how climate disturbances, whether natural or human-caused, affect different parts of the Earth’s system. It develops a new way to identify and rank these effects using machine learning techniques. The authors train special models on data that includes information about where and when certain events happen. They then use this training to create a kind of map that shows how these events are connected. This allows them to see which effects are most important and how they relate to each other. The paper uses two sets of fake data to test their new method, and it works well for both cases. |
Keywords
» Artificial intelligence » Machine learning » Random forest » Regression
