Risk-based tsunami early warning using random forest

Abstract

A new risk-based tsunami early warning method is developed using Random Forest (RF) with an extensive tsunami monitoring network (S-net) deployed off the Northeastern region of Japan. To consider a wide range of possible tsunami waves that may occur in the future, the RF model is developed using simulated 4000 tsunami wave time series at the S-net sensors. The response variable is the total aggregate loss of buildings caused by the tsunami, and the explanatory variables include earthquake information (magnitude, epicenter latitude, and epicenter longitude) and tsunami wave amplitudes at the S-net sensors. Unlike the conventional tsunami early warning method for predicting the tsunami wave amplitude along the shoreline, the response variable adopted in this study is a tsunami risk metric that reflects the tsunami impact to people and assets in coastal areas. The RF model is suitable for predicting highly nonlinear and scattered tsunami loss due to its characteristics of nonparametric regression and ensemble learning. The result indicates that compared with conventional linear regression-based algorithms, RF predicts the tsunami loss significantly better, reducing the mean square error by 90%. Furthermore, RF does not rely heavily on earthquake information, making it useful for announcing early warning for tsunamis generated by other sources.