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Aiming at the problems of difficulty in determining the optimal parameters and low accuracy of the deep learning method in dam prediction, the sparrow search algorithm (SSA) was improved, and the parameters of the gated recurrent unit (GRU) were optimized by the improved sparrow search algorithm (ISSA). Then a dam deformation prediction model based on the ISSA-GRU was constructed, and this model was applied to the deformation prediction of the Longyangxia Dam of Qinghai Section in the upper reaches of the Yellow River. The results show that the dam deformation prediction model based on ISSA-GRU has higher prediction accuracy and stability, which can be used as a reference for dam deformation prediction.

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针对大坝预测中采用深度学习方法难以确定最优参数和精度不高等问题，改进了麻雀搜索算法（SSA），采用改进麻雀搜索算法（ISSA）对门控循环单元（GRU）的参数进行寻优，构建了基于ISSA-GRU的大坝变形预测模型，并将该模型应用于黄河上游青海段龙羊峡大坝变形预测中。结果表明，基于ISSA-GRU的大坝变形预测模型具有更高的预测精度和稳定性，可为大坝变形预测提供参考。

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李书剑（1998-），男，硕士研究生，研究方向为变形监测与数据处理，E-mail：860045373@qq.com

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李书剑（1998-），男，硕士研究生，研究方向为变形监测与数据处理，E-mail：860045373@qq.com

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