Real-Time Prediction of Tsunami Magnitudes in Osaka Bay, Japan, Using an Artificial Neural Network

Abstract

This study examined the validity of using an artificial neural network (ANN) to predict tsunami water levels at several locations in Osaka Bay. The metropolitan areas of Osaka Bay have short warning times for tsunamis; a real-time tsunami forecast will allow for improved evacuation plans and will reduce the effect of these coastal disasters. Different tsunami conditions changing the relative strength of the asperities and background sources, such as fault displacement, fault length, fault width, fault slope, depth from sea bottom, and strike, were used for training the ANN; the data sets were generated by applying the nonlinear shallow water wave equations assuming different earthquake fault models. The linear activation function produced optimal results for the ANN output units, and the tangent-sigmoid function yielded good results for the ANN hidden layer units. The Levenberg-Marquardt method with Bayesian regulation was employed for the training of the ANN. Output from the trained ANN was the preliminary and secondary tsunami waves; these ANN output data agreed well with numerically obtained tsunami simulation results.