水力发电2026，Vol.52Issue(4)：9-18,68,11.
融合改进蛇鹭优化算法与深度学习耦合模型的径流预测方法
A Runoff Forecasting Method Integrating Improved Snake-Eagle Optimization Algorithm with a Deep Learning Coupled Model
摘要
Abstract
To address the challenges in capturing high-dimensional nonlinear features and optimizing complex model parameters for runoff prediction,this study proposes an Improved Snake-Eagle Optimization Algorithm(ISBOA)that integrates a Circle chaotic map and a Gaussian mutation strategy.The proposed ISBOA is then employed to intelligently optimize the hyperparameters of a constructed deep learning coupled model,CNN-BiGRU-Attention.Using runoff data from the Tangnaihai Hydrological Station in the upper reaches of Yellow River,simulation validations are conducted across multiple temporal scales(monthly,half-monthly,and ten-day)and with forecast lead times of 1-3 steps.The performance is also benchmarked against various baseline models,including CNN,GRU and CNN-BiGRU.The results indicate that the CNN-BiGRU-Attention model optimized by ISBOA achieves the highest prediction accuracy and stability,significantly outperforming its unoptimized counterpart and other comparative models,which thoroughly validates the effectiveness and superiority of the proposed ISBOA algorithm in optimizing runoff prediction models across multiple time scales.关键词
径流预测/卷积神经网络/双向门控循环单元/注意力机制/改进的蛇鹭优化算法Key words
runoff prediction/Convolutional Neural Network/Bidirectional Gated Recurrent Unit/Attention mechanism/Improved Snake-Eagle Optimization Algorithm分类
天文与地球科学引用本文复制引用
杨小鹏,周千涵,侯添甜,王心怡,胡可意,纪徐洋,朱非林..融合改进蛇鹭优化算法与深度学习耦合模型的径流预测方法[J].水力发电,2026,52(4):9-18,68,11.基金项目
国家自然科学基金资助项目(52009029) （52009029）
中央高校基本科研业务费专项资金资助(B240201123) （B240201123）
中国长江电力股份有限公司科研项目资助(Z242302021) （Z242302021）
