Summary of Location Agnostic Adaptive Rain Precipitation Prediction Using Deep Learning, by Md Shazid Islam et al.
Location Agnostic Adaptive Rain Precipitation Prediction using Deep Learning
by Md Shazid Islam, Md Saydur Rahman, Md Saad Ul Haque, Farhana Akter Tumpa, Md Sanzid Bin Hossain, Abul Al Arabi
First submitted to arxiv on: 2 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 A novel adaptive deep learning-based framework is proposed to tackle the challenges of rain precipitation prediction, which depends on location-specific weather patterns and meteorological features. The model’s performance degrades when applied to different locations due to distribution shifts, and global warming exacerbates this issue by rapidly changing weather patterns over time. To address these challenges, the authors develop an adaptive method that generalizes well for predicting precipitation at any location where non-adaptive methods fail. Empirical results show significant improvements of 43.51%, 5.09%, and 38.62% in predicting precipitation for Paris, Los Angeles, and Tokyo, respectively, using a deep neural network. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rain prediction is hard because it depends on weather patterns that are different from place to place. Even if a model works well in one area, it won’t work as well elsewhere. And with global warming, the weather keeps changing, making old models less effective over time. To solve this problem, scientists created an adaptive deep learning method that can predict rain at any location where other methods don’t work. They tested their idea and found significant improvements in predicting rain for Paris, Los Angeles, and Tokyo. |
Keywords
* Artificial intelligence * Deep learning * Neural network
