JDR Vol.13 No.2 pp. 245-253
doi: 10.20965/jdr.2018.p0245


Selection of Tsunami Observation Points Suitable for Database-Driven Prediction

Junichi Taniguchi*1,†, Kyohei Tagawa*1, Masashi Yoshikawa*2, Yasuhiko Igarashi*3, Tsuneo Ohsumi*4, Hiroyuki Fujiwara*4, Takane Hori*5, Masato Okada*6, and Toshitaka Baba*1

*1Graduate School of Science and Technology, Tokushima University
2-1 Minamijosanjima, Tokushima, Tokushima 770-8506, Japan

Corresponding author

*2Graduate School of Frontier Science, Tokyo University, Chiba, Japan

*3Japan Science and Technology Agency, Saitama, Japan

*4National Research Institute for Earth Science and Disaster Resilience, Ibaraki, Japan

*5Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan

*6The University of Tokyo, Chiba, Japan

October 31, 2017
January 5, 2018
Online released:
March 19, 2018
March 20, 2018
tsunami prediction, tsunami database, simulated annealing

During the Great East Japan Earthquake in 2011, real-time estimate of the earthquake’s magnitude was quite low, and consequently, the first report about the tsunami also understated its severity. To solve this issue, some proposed a massive overhaul of Japan’s offshore tsunami observation networks and methods to predict tsunamis in real time. In this study, we built a database containing 3,967 scenarios of tsunamis caused by earthquakes with hypocenters along the Nankai Trough, and tested a tsunami prediction method that uses this database along with offshore tsunami observation networks. Thus, we found that an uneven distribution of observation points had a negative effect on predictive accuracy. We then used simulated annealing to select the observation points to be used at each observation site and found that the predictive accuracy improved while using a few selected observation points compared to using every point.

Cite this article as:
J. Taniguchi, K. Tagawa, M. Yoshikawa, Y. Igarashi, T. Ohsumi, H. Fujiwara, T. Hori, M. Okada, and T. Baba, “Selection of Tsunami Observation Points Suitable for Database-Driven Prediction,” J. Disaster Res., Vol.13 No.2, pp. 245-253, 2018.
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