Summary of Accurate Prediction Of Temperature Indicators in Eastern China Using a Multi-scale Cnn-lstm-attention Model, by Jiajiang Shen et al.
Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model
by Jiajiang Shen, Weiyan Wu, Qianyu Xu
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 machine learning-based weather prediction model is proposed to address the complexity and nonlinearity of climate data. The multi-scale CNN-LSTM-Attention architecture integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and attention mechanisms for time series forecasting of temperature data in China. Experimental results show high accuracy in predicting temperature trends with a Mean Squared Error (MSE) of 1.978295 and Root Mean Squared Error (RMSE) of 0.8106562. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This model uses deep learning techniques to improve weather forecasting accuracy, providing a valuable tool for decision-making in areas such as urban planning, agriculture, and energy management. |
Keywords
» Artificial intelligence » Attention » Cnn » Deep learning » Lstm » Machine learning » Mse » Temperature » Time series
