Summary of Cluster-segregate-perturb (csp): a Model-agnostic Explainability Pipeline For Spatiotemporal Land Surface Forecasting Models, by Tushar Verma and Sudipan Saha
Cluster-Segregate-Perturb (CSP): A Model-agnostic Explainability Pipeline for Spatiotemporal Land Surface Forecasting Models
by Tushar Verma, Sudipan Saha
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV)
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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 introduces a pipeline for understanding the complex relationship between meteorological variables and land surface evolution in regional climate change effects forecasting. The approach combines perturbation-based explainability techniques like LIME with global marginal explainability techniques like PDP, addressing constraints of applying these methods to high-dimensional spatiotemporal deep models. The proposed pipeline enables diverse investigative analyses on complex land surface forecasting models, such as marginal sensitivity analysis and lag analysis. The study uses ConvLSTM as the surface forecasting model and analyzes the Normalized Difference Vegetation Index (NDVI) of the surface forecasts in various regions in Europe. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how changes in weather affect plants and crops across different parts of Europe. It combines two types of techniques to explain complex computer models that forecast future climate conditions. The study finds that precipitation has the biggest impact on plant growth, followed by temperature and pressure. Pressure doesn’t have a direct effect on plant growth. Additionally, the researchers discover interesting connections between weather patterns and plant growth. |
Keywords
* Artificial intelligence * Spatiotemporal * Temperature
