Summary of Weather Prediction Using Cnn-lstm For Time Series Analysis: a Case Study on Delhi Temperature Data, by Bangyu Li et al.
Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data
by Bangyu Li, Yang Qian
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 proposed hybrid CNN-LSTM model is designed to enhance temperature forecasting accuracy in the Delhi region by leveraging historical meteorological data from 1996 to 2017. By combining the spatial feature extraction capabilities of CNNs with the temporal dependencies captured by LSTMs, the model achieves improved prediction accuracy compared to traditional methods. The experimental results show a mean square error (MSE) of 3.26217 and a root mean square error (RMSE) of 1.80615, outperforming traditional forecasting approaches in terms of both accuracy and stability. This study contributes to the development of robust temperature prediction tools for meteorological forecasting and related fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a new way to predict temperatures in Delhi that’s more accurate than old methods. They used special kinds of computer models called CNNs and LSTMs to analyze data from 1996 to 2017. This helped them make better predictions, which can be important for things like farming, energy management, and protecting the environment. The results show that their new method is really good at predicting temperatures and could be used for other kinds of forecasting too. |
Keywords
» Artificial intelligence » Cnn » Feature extraction » Lstm » Mse » Temperature
