# Realtime Tsunami Prediction System Using Ocean Floor Network for Local Regions
## Real-Time Tsunami Prediction Technique
### Improvement of the Prediction  
Immediate and accurate predictions are needed to answer users' needs. Our system therefore uses not only pressure data but also seismometer data. It is important to make accurate predictions using real-time information about source direction based on the concepts of our system. Event detection works in a normal manner on our system. After a seismometer and pressure gauge on the ocean floor network system are triggered, a judgment of the source direction is made using the time series of information from the seismometer and the pressure gauge data at the observatories. The speed of detection is faster for the seismometer than for the pressure gauge, and the first extraction from the tsunami database is performed using only seismometer data. The second extraction is then performed using the temporal order of tsunami detection among the observatories. As the extraction proceeds, the candidates among the fault models are narrowed as the data on pressure from observatories increases. Finally, the top four models and one model that predicts the highest tsunami and the fastest arrival time are selected and visualized. The accuracy of the prediction depends on the dispersion of the relationship between the observed pressure data and calculated tsunami height [9]. Although statistical processing can be applied (e.g., [14; 15; 16]) to extract appropriate fault models, our system filters the selected models before statistical processing (Figure 4). Dispersion means that there are two cases for each combination of input data: one case is a relatively high tsunami at the predicted points, and the other is a relatively low tsunami at the same points. The first case corresponds to positive interference between the tsunami and the subsequent or reflected wave, which should be included in the tsunami prediction. The other case corresponds to the scattering of the tsunami. During tsunami propagation from the source, the tsunami that reaches the coast can become scattered. For example, a snaking tsunami often reaches the coast in a narrow bay but the energy of a snaking or scattered tsunami is low compared with the energy of a direct tsunami. This fact means that it is essential to take into consideration the direction of an approaching tsunami to improve the accuracy of tsunami prediction. Our system, therefore, judges the direction of an approaching tsunami using seismometers on the ocean floor network system and the temporal order of tsunami triggering. How the predicted information is used depends on the users. Local governments prefer to avoid underestimating the worst-case scenario, but other users might prefer to predict only tsunamis in the M8 category. [PERSON] et al. (2017) [9] have considered the evaluation of both the M9 and M8 models and have concluded that they might slightly overestimate compared to a model of the 1946 Nankai earthquake. We, therefore, prepared some alternatives to the fault models (Figure 5). One is the extraction of the case corresponding to the greatest predicted height. The other is the extraction of models using simple equations for the average (\(\mu\)) and standard deviation (\(\zeta\)). Users can extract fault models that correspond to a value equal to \(\mu+\tau_{\zeta}\) (\(\mathrm{r}\): constant). The constant \(\mathrm{r}\) can be set in the system as users prefer, and in this way, we have maintained flexibility with respect to users' needs. Another issue to be considered is the prediction of inundation. The importance of inundation has been shown by [PERSON] et al. (2018) [24], who reported the predicted inundation area compared to the observed area for the 1946 Nankai earthquake based on old monuments of the tsunami around the coastal area of Wakayama Prefecture. In cases with large crustal uplift at the points predicted by the extracted models, the inundation area could be underestimated, even if the predicted tsunami height was accurate.