Integrating graph neural networks and attention mechanisms for high-accuracy precipitation prediction
With the advent of big data and meteorological modernization, the role of meteorological data analysis in various fields, such as daily life and social production, has become increasingly prominent. Taking the Guangxi Meteorological Information Center as an example, the volume of meteorological...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/7320/1/fyp_CCA_2025_ZT.pdf http://eprints.utar.edu.my/7320/ |
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| Summary: | With the advent of big data and meteorological modernization, the role of
meteorological data analysis in various fields, such as daily life and social
production, has become increasingly prominent. Taking the Guangxi
Meteorological Information Center as an example, the volume of
meteorological observation data is enormous, posing challenges in data storage,
processing, and real-time throughput. Traditional methods of meteorological
data analysis are no longer sufficient to deal with the complexity and scale of
big data, necessitating the adoption of new approaches to enhance the
efficiency and accuracy of weather forecasting.
This thesis addresses these challenges by integrating innovative methodologies
to organize and analyze meteorological data in Guangxi, focusing on big data
processing, neural networks, and other advanced techniques. The research
makes three significant contributions:
1. Development of GraphAT-NET: A novel forecasting model named
iv
GraphAT-NET is proposed, which combines graph neural networks and
channel attention mechanisms to enhance short-term precipitation forecasting.
By extracting key information from TRAJGRU's feature maps and constructing
a Graph structure with an Efficient Channel Attention (ECA) mechanism,
GraphAT-NET achieves superior performance. Experiments using the moving
MNIST dataset and real-world radar echo data show that GraphAT-NET
reduces Mean Squared Error (MSE) to 1.23 and improves the Structural
Similarity Index (SSIM) by an average of 16.26% compared to other models.
This model demonstrates a substantial average enhancement of 65.4% in MSE
and a 10.29% improvement in SSIM on real-world datasets, highlighting its
effectiveness in predicting cumulonimbus cloud distribution.
2. Application of the Informer Model: The research explores the use of
the Informer model for time series analysis of ground station data. The
Informer model achieves a mean absolute error (MAE) of 0.0077, significantly
outperforming traditional numerical weather prediction (NWP) techniques with
an MAE of 0.02. This deep learning-based framework combines feature
engineering and model optimization, enhancing the precision and reliability of
short-term precipitation forecasting. The Informer model improves predictive
accuracy by 74.2% over conventional methods and by 72.3% over other deep
learning-based models, as measured by the root mean square error (RMSE).
v
3. Integration and Comparison of Methods: The study integrates
GraphAT-NET and the Informer model to forecast rainfall using CAPPI data
and ground station data, respectively. By comparing these methods, the results
indicate that predictions based on ground station data using the Informer model
are more accurate, outperforming radar reflectivity signal-based predictions by
5.16. This highlights the effectiveness of ground station data in precipitation
forecasting.
In conclusion, this research significantly advances the field of
meteorology by introducing innovative deep learning models that improve the
accuracy and reliability of short-term precipitation forecasting. The findings
have the potential to enhance meteorological services, aiding in better
decision-making for weather-related activities and emergency planning.
In the future, we will focus on integrating multimodal data for rainfall
prediction, combining radar, satellite imagery, ground sensors, and
meteorological models. This comprehensive approach aims to enhance
prediction accuracy and robustness. We also plan to develop a unified
evaluation framework to assess multimodal models fairly, considering each
modality's unique characteristics. This will help identify effective approaches,
advancing rainfall prediction and improving meteorological services for better
decision-making. |
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