Using Evolving ANN-Based Algorithm Models for Accurate Meteorological Forecasting Applications in Vietnam

Abstract

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.