The reproduction of meteorological tsunamis utilizing physically based hydrodynamic models is complicated in light of the fact that it requires large amounts of information, for example, for modelling the limits of hydrological and water driven time arrangement, stream geometry, and balanced coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a backpropagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modelling and forecasting the maximum peak and variation with time of meteorological tsunamis in the Mekong estuary in Vietnam. The parameters, including both the nearby climatic weights and the wind field factors, for finding the most extreme meteorological waves, are first examined, through the preparation of evolved neural systems. The time series of meteorological tsunamis were used for training and testing the models, and data for three cyclones were used for model prediction. Given the 22 selected meteorological tidal waves, the exact constants for the Mekong estuary, acquired through relapse investigation, are A = 9.5 × 10⁻³ and B = 31 × 10⁻³. Results showed that both the Multilayer Perceptron Network (MLP) and evolved radial basis function (ERBF) methods are capable of predicting the time variation of meteorological tsunamis, and the best topologies of the MLP and ERBF are I₃H₈O₁ and I₃H₁₀O₁, respectively. The proposed advanced ANN time series model is anything but difficult to use, utilizing display and prediction tools for simulating the time variation of meteorological tsunamis.

中文翻译：

---

使用基于进化神经网络的算法模型在越南进行准确的气象预报

鉴于基于物理的水动力模型对海啸的再现非常复杂，因为它需要大量的信息，例如，对水文和水动力时间安排，水流几何形状和平衡系数的限制进行建模。因此，利用反向传播神经网络（BPNN）和径向基函数神经网络（RBFNN）的人工神经网络（ANN）策略被认为是建模和预测气象海啸中最大峰值和随时间变化的可行选择。越南的湄公河河口。首先，通过准备演化的神经系统，对用于查找最极端气象波的参数（包括附近的气候权重和风场因子）进行了检查。气象海啸的时间序列用于训练和测试模型，而三个气旋的数据则用于模型预测。给定22个选定的气象潮汐，通过复发调查获得的湄公河口的确切常数为A  ＝ 9.5×10 ^-3，B  ＝ 31×10 ^-3。结果表明，多层感知器网络（MLP）和演化径向基函数（ERBF）方法都能够预测气象海啸的时间变化，并且MLP和ERBF的最佳拓扑是I ₃ H ₈ O ₁和I ₃ H ₁₀ O ₁。拟议的高级人工神经网络时间序列模型几乎没有什么好用的，它利用显示和预测工具来模拟气象海啸的时间变化。