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JDR Vol.12 No.2 pp. 226-232
(2017)
doi: 10.20965/jdr.2017.p0226

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Fusion of Real-Time Disaster Simulation and Big Data Assimilation – Recent Progress

Shunichi Koshimura†

International Research Institute of Disaster Science, Tohoku University
Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-0845, Japan

†Corresponding author

Received:
January 7, 2017
Accepted:
February 3, 2017
Online released:
March 16, 2017
Published:
March 20, 2017
Creative Commons License
Keywords:
real-time simulation, earthquake, tsunami, high-performance computing, GIS, big data
Abstract
This paper reports the latest outcomes of the project “Establishing the Advanced Disaster Reduction Management System by Fusion of Real-time Disaster Simulation and Big Data Assimilation” that started in 2014. The objectives of targeting various kinds of damage due to earthquakes and tsunami, fusion of large-scale high-resolution numerical simulation, effective processing and analysis of big data from various observations, and data assimilation were achieved. The outcomes will be utilized to create the world’s first real-time simulation and big data analysis basis that would potentially assist with designing preliminary measures based on quantitative data and disaster responses to a disaster. Case studies using recent disasters were used in this endeavor and validation were performed. In the future, environments that rapidly provide information on possible damage situations in real time for public agencies, corporations, and citizens facing a catastrophic disaster in Japan will be developed by integrating these studies.
Cite this article as:
S. Koshimura, “Fusion of Real-Time Disaster Simulation and Big Data Assimilation – Recent Progress,” J. Disaster Res., Vol.12 No.2, pp. 226-232, 2017.
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References
  1. [1] S. Koshimura, “Establishing the Advanced Disaster Reduction Management System by Fusion of Real-Time Disaster Simulation and Big Data Assimilation,” Journal of Disaster Research, Vol.11 No.2, pp.164-174, 2016.
  2. [2] S. Koshimura, R. Hino, Y. Ohta, H. Kobayashi, A. Musa, and Y. Murashima, “Real-time tsunami inundation forecasting and damage estimation method by fusion of real-time crustal deformation monitoring and high-performance computing,” IUGG General Assembly, 2015.
  3. [3] Y. Oishi, F. Imamura, and D. Sugawara, “Near-field tsunami inundation forecast using the parallel TUNAMI-N2 model: Application to the 2011 Tohoku-Oki earthquake combined with source inversions,” Geophys. Res. Lett., Vol.42, pp. 1083-1091, 2015.
  4. [4] A. Musa, H. Matsuoka, O. Watanabe, Y. Murashima, S. Koshimura, R. Hino, Y. Ohta, and H. Kobayashi, “A Real-Time Tsunami Inundation Forecast System for Tsunami Disaster Prevention and Mitigation,” The International Conference for High Performance Computing, Networking, Strage and Analysis (SC15), Austin, Texas, Nov. 2015.
  5. [5] Y. Narita and S. Koshimura, “Classification of Tsunami Fragility Curves Based on Regional Characteristics of Tsunami Damage,” Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering), Vol.71, No.2, pp. I_331-I_336, 2015.
  6. [6] Y. Kawasaki, Y. Hara, T. Mitani, and M. Kuwahara, “Real-time Simulation of Dynamic Traffic Flow with Traffic Data Assimilation Approach,” Journal of Disaster Research, Vol.11, No.2, pp. 246-254, 2016.
  7. [7] Y. Kawasaki, M. Kuwahara, Y. Hara, T. Mitani, A. Takenouchi, T. Iryo, and J. Urata, “Investigation of Traffic and Evacuation Aspects at Kumamoto Earthquake and the Future Issues,” Journal of Disaster Research, Vol.12, No.2, 2017.
  8. [8] W. Liu, F. Yamazaki, and T. Sasagawa, “Monitoring of the recovery process of the Fukushima Daiichi nuclear power plant from VHR SAR images,” Journal of Disaster Research, Vol.11, No.2, pp. 236-245, 2016.
  9. [9] H. Gokon, S. Koshimura, and M. Matsuoka, “Object-based method for esimating tsunami-induced damage using TerraSAR-X data,” Journal of Disaster Research, Vol.11, No.2, pp. 225-235, 2016.
  10. [10] W. Liu and F. Yamazaki, “Extraction of collapsed buildings due to the 2016 Kumamoto earthquake based on multi-temporal PALSAR-2 data,” Journal of Disaster Research, Vol.12, No.2, 2017.
  11. [11] H. Gokon, S. Koshimura, and K. Meguro, “Verification of a method for estimating building damage in extensive tsunami affected areas using L-band SAR data,” Journal of Disaster Research, Vol.12, No.2, 2017.
  12. [12] Y. Bai, B. Adriano, E. Mas, H. Gokon, and S. Koshimura, “Developing an object-based building damage assessment methodology using only post event ALOS-2/PALSAR-2 dual polarimetric SAR intensity images,” Journal of Disaster Research, Vol.12, No.2, 2017.
  13. [13] N. Hirokawa and T. Osaragi, “Earthquake disaster simulation system – Structural damage, Trafic hindrance and Extensive fire,” Journal of Disaster Research, Vol.11, No.2, pp. 175-187, 2016.
  14. [14] T. Oki and T. Osaragi, “Modeling Human Behavior of Local Residents in the Aftermath of a Large Earthquake – Wide-area Evacuation, Rescue and Firefighting in Densely Built-up Wooden Residential Areas,” Journal of Disaster Research, Vol.11, No.2, pp. 188-197, 2016.
  15. [15] T. Osaragi and T. Oki, “Wide-area Evacuation Simulation Incorporating Rescue and Firefighting by Local Residents,” Journal of Disaster Research, Vol.12, No.2, 2017.
  16. [16] Osaragi, T. and N. Hirokawa, “Simulation Analysis of Fire Brigade Action Strategy at the Time of Simultaneous Multiple Fires,” Journal of Disaster Research, Vol.12, No.2, 2017.
  17. [17] K. Sezaki, S. Konomi, and M. Ito, “User Participatory Sensing for Disaster Detection and Mitigation,” Journal of Disaster Research, Vol.11, No.2, pp. 207-216, 2016.
  18. [18] H. Mori, M. Ito, and K. Sezaki, “Early Fire Alert System in an Evacuation Situation with Mobile Sensing Technology,” Journal of Disaster Research, Vol.12, No.2, 2017.
  19. [19] Y. Sekimoto, R. Shibasaki, H. Kanasugi, T. Usui, and Y. Shimazaki, “PFLOW: Reconstruction of people flow recycling large-scale social survey data,” IEEE Pervasive Computing, Vol.10, No.4, pp. 27-35, Oct.-Dec. 2011.
  20. [20] A. Sudo, T. Kashiyama, T. Yabe, H. Kanasugi, and Y. Sekimoto, “People Distribution Estimation Method on Massive Earthquake using Filtering Approach,” Journal of Disaster Research, Vol.11, No.2, pp. 217-224, 2016.
  21. [21] T. Yabe, Y. Sekimoto, A. Sudo, and K. Tsubouchi, “Predicting Delay of Commuting Activities following Frequently Occurring Disasters using Location Data from Smartphones,” Journal of Disaster Research, Vol.12, No.2, 2017.
  22. [22] J. Zhao, K. Sugiura, Y. Wang, and Y. Ishikawa, “Simulation Data Warehouse for Integration and Analysis of Disaster Information,” Journal of Disaster Research, Vol.11, No.2, pp. 255-264, 2016.
  23. [23] J. Zhao, Y. Ishikawa, Y. Wakita, and K. Sugiura, “Difference Operators in Simulation Data Warehouses,” Journal of Disaster Research, Vol.12, No.2, 2017.
  24. [24] T. Maeda and H. Fujiwara, “Seismic Hazard Visualization from Big Simulation Data: Construction of a Parallel Distributed Processing System for Ground Motion Simulation Data,” Journal of Disaster Research, Vol.11, No.2, pp. 265-271, 2016.
  25. [25] T. Maeda and H. Fujiwara, “Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground Motion Simulation Data,” Journal of Disaster Research, Vol.12, No.2, 2017.

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Last updated on Apr. 19, 2024