# Realtime Tsunami Prediction System Using Ocean Floor Network for Local Regions
###### Abstract  
The ocean floor network system for earthquakes and tsunamis is one of the effective tools for the early detection of large earthquakes on plate boundaries and the tsunamis they generate. The Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET) was installed in the first rupture areas of the 1944 Tonankai and 1946 Nankai earthquakes. The DONET around the Nankai Trough, a site of huge earthquakes that have caused severe damage, has the potential to detect the genesis of a tsunami. We developed a real-time tsunami prediction system for local communities that takes advantage of the features of DONET, and we have already made it available to several local governments and a commercial company. The outputs of the prediction are the tsunami arrival time, its height, its inundation area, and inundation depth. The system makes real-time monitoring of tsunamis possible. The system should be conceptually applicable to the Nankai Trough area, which has characteristics consistent with the assumptions the system makes about tsunami propagation, crustal activities, and coastal communities. Here, we describe the conceptual basis of the system, the features used to ensure the accuracy of predictions, and the policies used to develop and implement them. t 12 November 202125 January 20225 January 20225 January 20225 January 20225 January  
To answer the needs of society, it is essential that the network system be reliable and have redundancy. The system uses commercial base cables as backbone cables, and the longevity of those cables has been satisfactory. The need for redundancy is addressed by sending the observed data to two landing stations (except for two subsystems of the S-net) to reduce the risk of losing data. In addition, each observatory has multiple sensors for earthquakes and tsunamis. Crustal activities, including tsunamis, can continue to be observed even if one of the sensors experiences problems. These safeguards are indispensable for an ocean floor network system because continuity without missing data is important to their implementation and use in societies. Data observed by these ocean floor network systems are sent in real time to the Japan Meteorological Agency (JMA), institutions related to earth science, and universities. The JMA uses the data to provide emergency early warnings of earthquakes and tsunamis. For tsunamis, a warning and emergency warning are announced to the public in areas where the tsunami height is expected to be 1-3 m or more than 3 m, respectively. The JMA sets regional classifications for these warnings and announces the warnings uniformly over all regions. We propose a different strategy to that of the JMA to announce tsunami warnings. Tsunami information produced by our system should be customized for each community and should depict not just the time of tsunami arrival and its height but also potential tsunami damage, including inundation area and depth. The published overview of our realtime tsunami predictions [9,10] can be found in Wakayama, Mie, and Chiba prefectures, the coastal city of Owase, at the Chubu Electric Power Company, and at Kagawa University. In Section 2, we provide in-depth information about the methods used in our system. The Japanese archipelago is surrounded by plate boundaries and offshore active faults, and near-field tsunamis have caused severe damage in Japan. Many earlier reports have documented the damage caused by tsunamis over more than 1000 years (e.g., [11]). The JMA has previously estimated the height of tsunamis in real time using information on hypocenters and magnitude levels determined from land-based seismic stations (e.g., [12]). [PERSON] et al. (2009) [13] have indicated that the tsunami waveform at the coast can be predicted by using pressure gauge data taken at the ocean bottom and a linear combination of pre-calculated Green's functions. [PERSON] et al. (2014) [14], [PERSON] et al. (2016) [15], and [PERSON] et al. (2016) [16] have adopted a model-based prediction methodology.