Early warning for maximum tsunami heights and arrival time based on an artificial neural network

Abstract

Tsunamis can cause extensive damages and loss of lives in coastal communities. Early warning for tsunami can help save lives and mitigate damages from tsunamis. This study aimed to develop an early warning for tsunamis using an artificial neural network (ANN) that can predict maximum tsunami heights and arrival time. Imwon Port, located on the eastern coast of Korea was selected as the target area. A weighted logic tree approach that assigns weights to fault parameters of earthquake based on their importance was proposed to establish tsunami scenarios and generate tsunami big data. Nine offshore observations in the East Sea were used as standard observations for predicting maximum tsunami height and arrival time at Imwon Port. ANN was developed to predict maximum tsunami heights and arrival time. The Kriging method was adopted to investigate the spatial distribution of the maximum tsunami height in the port, and the root mean square error, and coefficient of determination were used to evaluate the model’s performance. The estimates of maximum tsunami heights and arrival times generated by the proposed model agreed with the results of the numerical model. Furthermore, the ANN can generate these estimation quickly, enhancing the effectiveness of early tsunami warnings.