Summary of Deep Learning For Weather Forecasting: a Cnn-lstm Hybrid Model For Predicting Historical Temperature Data, by Yuhao Gong et al.
Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data
by Yuhao Gong, Yuchen Zhang, Fei Wang, Chi-Han Lee
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Atmospheric and Oceanic Physics (physics.ao-ph)
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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 This hybrid model combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict historical temperature data. The CNNs extract spatial features while LSTMs handle temporal dependencies, resulting in improved prediction accuracy and stability. The model uses Mean Absolute Error (MAE) as the loss function, demonstrating excellent performance in processing complex meteorological data. This study offers valuable insights for fields such as agriculture, energy management, and urban planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to predict weather by combining two types of artificial intelligence networks called CNNs and LSTMs. These networks help us better understand patterns in temperature data from the past. The model is really good at making predictions and can handle missing information or lots of data points. This helps us make more accurate forecasts, which is important for things like farming, energy use, and planning cities. |
Keywords
» Artificial intelligence » Loss function » Lstm » Mae » Temperature
