Summary of Comparative Evaluation Of Weather Forecasting Using Machine Learning Models, by Md Saydur Rahman et al.
Comparative Evaluation of Weather Forecasting using Machine Learning Models
by Md Saydur Rahman, Farhana Akter Tumpa, Md Shazid Islam, Abul Al Arabi, Md Sanzid Bin Hossain, Md Saad Ul Haque
First submitted to arxiv on: 2 Feb 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 explores the application of machine learning algorithms to improve weather forecasting, leveraging techniques like data mining and analysis. By analyzing a 20-year dataset from Dhaka city, the study evaluates the performance of various algorithms, including Gradient Boosting, AdaBoosting, Artificial Neural Network, Stacking Random Forest, Stacking Neural Network, and Stacking KNN, using metrics like Confusion matrix measurements. The results show significant progress in predicting precipitation and temperature patterns, providing valuable insights into the performances and features correlation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning can help us better understand weather and make more accurate predictions. Scientists used data from a weather station in Dhaka city to test different types of machine learning algorithms. They wanted to see which ones worked best for predicting things like rain and temperature. The results are impressive, showing that these algorithms can really help us improve our understanding of the weather. |
Keywords
* Artificial intelligence * Boosting * Confusion matrix * Machine learning * Neural network * Random forest * Temperature
