Summary of Large Language Model Predicts Above Normal All India Summer Monsoon Rainfall in 2024, by Ujjawal Sharma et al.
Large Language Model Predicts Above Normal All India Summer Monsoon Rainfall in 2024
by Ujjawal Sharma, Madhav Biyani, Akhil Dev Suresh, Debi Prasad Bhuyan, Saroj Kanta Mishra, Tanmoy Chakraborty
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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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 abstract discusses a machine learning research paper that aims to improve the accuracy of predicting the All India Summer Monsoon Rainfall (AISMR). The authors adapt and fine-tune a language model called PatchTST to predict AISMR with a lead time of three months. They train the model using historical AISMR data, climate indices, and categorical ocean dipole values. The results show that the fine-tuned model outperforms other neural network models and statistical models, achieving an exceptionally low root mean square error (RMSE) percentage of 0.07% and a high Spearman correlation of 0.976. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve the accuracy of predicting the All India Summer Monsoon Rainfall (AISMR). The authors use a machine learning model called PatchTST to make predictions about AISMR. They train the model using past data and other important factors that affect the monsoon. The results show that this model is very good at making accurate predictions. |
Keywords
» Artificial intelligence » Language model » Machine learning » Neural network
