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Aiming at the problem that the traditional methods of dam displacement prediction are difficult to handle long-term time series, this paper proposes an interval intelligent prediction model that integrates the Golden Damped Sine Cosine Improved Particle Swarm Optimization(GDPSO), Long Short-Term Memory(LSTM), self-attention mechanism, and Quantile Regression(QR). By incorporating the self-attention mechanism to enhance LSTM's ability for capturing global dependencies, and utilizing GDPSO for hyperparameter optimization, a high-precision point prediction model(GDPSO-LSTM-attention) is developed. Further combining QR to quantify model uncertainty, the model achieved interval prediction of displacement. Case studies demonstrate that the proposed model outperforms comparative models in both accuracy and trend tracking, and effectively quantifies prediction uncertainty, which provides foundational support for the "Four Preventions" construction of digital twin platforms in smart water conservancy.
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Basic Information:
DOI：10.20040/j.cnki.1000-7709.2026.20251779
China Classification Code:TV698.11
Citation Information:
[1]BAO Zhen-dong,XU Hai-feng,HU Jin ,et al.Intelligent Interval Prediction Model for Concrete Dam Deformation Based on GDPSO-LSTM-attention-QR[J].Water Resources and Power,2026,44(03):142-146+141.DOI:10.20040/j.cnki.1000-7709.2026.20251779.
Fund Information:
国家自然科学基金面上项目(51579085); 江苏省水利科技项目(2021068,2022011)
2025-10-17
2025
2025-11-14
2025
1
2025-12-19
2025-12-19
2025-12-19