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Aiming at the shortcomings of slow convergence and easily falling into the local minimum in the learning process for the traditional BP neural network, particle swarm algorithm with fast convergence speed and strong global optimization ability was introduced so as to establish the PSO-BP model. Taking the seepage monitoring data of an earth and rockfill dam as an example, the seepage was predicted. Compared with the prediction model, the BP model and the traditional statistical regression model, the results show that the PSO-BP model has a higher goodness-of-fit and convergence.

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针对传统BP神经网络在学习过程中存在的收敛缓慢、容易陷入局部极小化等缺点，引入收敛速度快、全局寻优能力强的粒子群算法，从而建立了PSO-BP模型，并以某土石坝渗流监测数据为例，对土石坝渗流进行了预测，对比预测模型、BP模型、传统统计回归模型的结果表明，PSO-BP模型具有更高的拟合性和收敛性。

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胡孟凡（1997-），女，硕士研究生，研究方向为大坝安全监测，E-mail：humengfan0425@163.com

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胡孟凡（1997-），女，硕士研究生，研究方向为大坝安全监测，E-mail：humengfan0425@163.com

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