"The main advantage of our method is the speed of predictions, which is crucial for early warning," explained Iyan Mulia, the work's lead and a scientist at RIKEN. "Conventional tsunami modeling provides predictions after 30 minutes, which is too late. But our model can make predictions within seconds." To achieve this, the coast now boasts the world's largest network of sensors for monitoring the movement of the ocean floor. About 150 offshore stations make up this network and work together in order to provide early warnings of tsunamis. To function effectively, however, the data generated by the sensors needs to be converted into tsunami heights and extents along the coastline.

Detailed predictions about how an approaching tsunami will impact the northeastern coastline in Japan can be made in fractions of a second rather than half an hour or so-buying precious time for people to take appropriate action1. This potentially life-saving technology exploits the power of machine learning. The catastrophic tsunami that struck northeast Japan on 11 March 2011 claimed the lives of about 18,500 people. Many lives might have been saved if early warning of the impending tsunami had included accurate predictions of how high the water would reach at different points along the coastline and further inland. The coast now boasts the world's largest network of sensors for monitoring movement of the ocean floor.