Research on Lightning Prediction Based on GCN-LSTM Model Integrating Spatiotemporal Features Article Swipe
Wei Zhou
,
Wenqiang Wang
,
Xupeng Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/atmos16040447
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/atmos16040447
To overcome the limitations of spatiotemporal feature extraction that are inherent in conventional lightning warning algorithms relying solely on temporal analysis, we propose a novel prediction framework integrating a Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM) architecture, and a multi-head attention mechanism. The methodology innovatively constructs station adjacency matrices based on geographical distances between meteorological monitoring stations in Qingdao, Shandong Province, China, where GCN layers capture inter-station spatial dependencies while LSTM units extract localized temporal dynamics. A dedicated multi-head attention module was developed to enable adaptive fusion of global spatiotemporal patterns, significantly enhancing lightning warning level prediction accuracy at target locations. The GCN-LSTM model achieved 93% accuracy, 59% precision, 64% recall, and a 59% F1 score. Experimental evaluation on operational meteorological data demonstrated the model’s superior performance: it achieved statistically significant accuracy improvements of 6% (p = 0.019), 3% (p = 0.026), and 2% (p = 0.03) over conventional LSTM, TGCN, and CNN-RNN baselines, respectively. Comprehensive assessments through precision–recall analysis, confusion matrix decomposition, and spatial generalizability tests confirmed the framework’s robustness. The key theoretical advancement introduced by this study lies in the synergistic coupling of graph-based spatial modeling with deep temporal sequence learning, augmented by attention-driven feature fusion—an architectural innovation addressing critical gaps in existing single-modality approaches. This methodology establishes a new paradigm for extreme weather prediction with direct applications in lightning hazard mitigation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/atmos16040447
- https://www.mdpi.com/2073-4433/16/4/447/pdf?version=1744367610
- OA Status
- gold
- Cited By
- 2
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409367937
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409367937Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/atmos16040447Digital Object Identifier
- Title
-
Research on Lightning Prediction Based on GCN-LSTM Model Integrating Spatiotemporal FeaturesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-11Full publication date if available
- Authors
-
Wei Zhou, Wenqiang Wang, Xupeng WangList of authors in order
- Landing page
-
https://doi.org/10.3390/atmos16040447Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-4433/16/4/447/pdf?version=1744367610Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2073-4433/16/4/447/pdf?version=1744367610Direct OA link when available
- Concepts
-
Lightning (connector), Meteorology, Computer science, Environmental science, Climatology, Remote sensing, Geology, Geography, Physics, Quantum mechanics, Power (physics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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