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摘要：
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| 简要介绍国内外海啸检测方法现状；设计并比较反向传播神经网络（BP）、径向基函数神经网络（RBF）两种神经网络检测算法，采用美国“海啸事件深海评估及报告系统”（DART）数据验证算法有效性，同时将其与传统多项式插值检测算法的预测残差进行对比，使用均方根误差衡量预测值和观测值的残差。仿真结果表明新算法可降低均方根误差，其中绝对值大于10 mm的大残差数量降幅约90%～100%。 |
| This study briefly introduces the current status of tsunami detection methods; Back Propagation(BP) and Radial Basis Function(RBF) neural network detection algorithms is designed and compared, and the effectiveness of the algorithms are proved using DART data, and these prediction residuals are compared with the traditional polynomial interpolation detection algorithm, the root mean square error is used to measure the residual between predicted values and observed values. Simulation results prove that the new algorithms can reduce the root mean square error, especially reducing the number of residuals with an absolute value greater than 10 mm 90%～100%. |
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