Afterword

The tsunamis discussed in this book are a drop in the ocean of the vast number of catastrophic events that have affected the planet. Tsunamis can be traced back almost 3.5 billion years, so it seems that people would be prepared for their wrath. This chapter notes the value of education, and it is through the work of organizations such as the Pacific Tsunami Museum in Hilo, Hawaii, that such education reaps vast benefits. Without organizations such as this, people can only realistically expect to die, or perhaps survive through sheer luck. This chapter discusses the exposure of modern society to global issues related to tsunamis, such as the breaking of submarine cables. This chapter also raises the specter of meteorological tsunamis—another unexpected hazard.

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

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.

Meteorological Tidal Predictions in the Mekong Estuary Using an Evolved ANN Time Series

The reproduction of meteorological waves utilizing physically based hydrodynamic models is very difficult in light of the fact that it requires enormous amounts of information, for example, hydrological and water-driven time arrangement limits, stream geometry, and balance coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modeling and forecasting the maximum and time variation of meteorological tsunamis in the Mekong Estuary in Vietnam. The parameters, including both the nearby climatic and breeze field factors, for finding the most extreme meteorological waves are first examined, depending on the preparation of the evolved neural systems. The time series for meteorological tsunamis are used for training and testing the models, and data for three cyclones are used for model prediction. This study finds that the proposed advanced ANN time series model is easy to utilize with display and prediction tools for simulating the time variation of meteorological tsunamis.