4:00 PM - 4:15 PM
[HDS10-26] Real-time tsunami inundation forecast system: Application of machine learning for matching algorithm
Keywords:Real-time Tsunami Inundation Forcast, Tsunami Scenario Bank, Machine Learning
In this study, we propose two new predicting methods for tsunami inundation using a machine learning. One is a multiple regression analysis for a maximum inundation at a evaluation point, and the other is a classification analysis of inundated or not inundated at a evaluation point using Support Vector Machine (SVM). The methods learn the relation between the offshore tsunami heights and tsunami inundations from the tsunami scenarios in the TBS.
We tried to combine the multi-index method and the present two method. First, the system picked up several tsunami scenarios using the multi-index method. Next, the tsunami scenarios were screened by comparing the tsunami inundation distribution with the predicted results of a multiple regression analysis and classification analysis. This two-step forecast method provided tsunami scinarios with smaller variance and prediction error than a previous procedure.