# Realtime Tsunami Prediction System Using Ocean Floor Network for Local Regions
## Real-Time Tsunami Prediction Technique
### Concept of the System  
Our system adopted a model-based prediction method based on [PERSON] et al. (2014) [14] to predict tsunamis and their amplified heights using ocean floor network system data [9; 10]. The real-time tsunami prediction system that we developed predicts arrival times of the fastest tsunami, the tsunami height, and the inundation area with depth. The system also concurrently provides a visualization of the waveforms of the tsunami at the coastal points where the tsunami is predicted to make landfall using extracted fault models (Figure 2). These predictions are provided for landfall sites in local regions.  
We prefer not to select a unique fault model for these predictions because we consider tsunamis to originate from seafloor landslides (Figure 3). Such a tsunami may have local effects, and the determination of a unique model may lead to underestimation. Forearc regions around Japan are covered with soft sediment, and a local tsunami sometimes occurs. In fact, many seafloor slides can be traced on the forearc region along the Nankai Trough. During an amplified tsunami produced by a Nankai megathrust earthquake, a local tsunami generated by a seafloor slide may occur somewhere between the origin of the amplified tsunami and its arrival at the coast; in this case, the local tsunami height is added to the height of the amplified tsunami. Such local effects on the tsunami should be reflected in the prediction. Determination of one fault model may lead to underestimation of the tsunami height because it is difficult to take into account local effects on tsunami heights.  
Figure 3: Schematic figure of the tsunami propagation by plate boundary earthquake and seafloor slide. The effect on the maximum height of the coastal locations depends on the location and timing of the seafloor slides. Dark and light brown ellipses, blue stars, and green circles indicate the seafloor slides, observatories, and predicted locations.  
Figure 2: Example of the real-time tsunami prediction system we have developed. An example of the inundation is Tanabe City, Wakayama Prefecture, Japan. The table on the left side indicates the arrival time and the height our system predicted. The upper right figure indicates a bar graph of the DONET pressure gauges showing the situation tsunami propagation. The lower right side has two figures, which are the inundation map and the calculated tsunami waveforms at the target location.  
Although there are some approaches and characteristics to extract fault models for the prediction [14; 15; 16], the fact that the correlation between the observed pressure data and calculated tsunami height was positive with dispersion is common. [PERSON] et al. (2014) [14] estimated correlation using pressure values for the first arrival and uses the average for selected models corresponding input data. Our system uses those for the entire waveform and extracts the absolute maximum tsunami height for all observatories and prediction points. How to extract the fault models considering the accuracy depends on the aim of users. For example, a local government prefers to avoid underestimation, so it can choose to extract models that predict the maximum tsunami height. Our system prepares options based on methods that users can select to extract models based on the goals of the users.  
Here, we have developed our system using ocean floor network systems like DONET and S-net. However, coastal areas covered by the ocean floor network system like DOENT and S-net may be restricted. In such a case, buoy data is also available in this system. In fact, the entirety of Japan is not surrounded by such infrastructure. Although the number of the buoy station depends on the covered coastal area and the topography, our system has the extendibility to install on other areas and the logic of the prediction is suitable for easy adaptability.