JDR Vol.12 No.2 pp. 335-346
doi: 10.20965/jdr.2017.p0335

Survey Report:

Online Information as Real-Time Big Data About Heavy Rain Disasters and its Limitations: Case Study of Miyagi Prefecture, Japan, During Typhoons 17 and 18 in 2015

Shosuke Sato*,†, Shuichi Kure**, Shuji Moriguchi*, Keiko Udo*, and Fumihiko Imamura*

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

Corresponding author

**Department of Environmental Engineering, Toyama Prefectural University, Toyama, Japan

October 4, 2016
January 22, 2017
Online released:
March 16, 2017
March 20, 2017
online information, public information, disaster response, situation awareness, heavy rain
The role of public online information in helping to reduce disaster damage is expected to become increasingly important since it can be used for decision making about disaster response. This paper aims to discuss the effectiveness and limitations of real-time online information about heavy rainfall based on an analysis of data on the disaster caused by Typhoons 17 and 18 in 2015 in Miyagi prefecture, Japan, and on a focus group interview survey with four experts on natural disasters. The results from the interviews showed the following: (1) Landslide alert information is reliable for prediction purposes. However, many people did not monitor it because it was released around midnight. (2) Areas of landslide occurrence and river flooding correspond to areas with heavy cumulative rainfall. Yet cumulative rainfall data are not available on the web. (3) The available radar-rainfall data can be used to predict the situation one hour from the present as long as the person has expert knowledge. (4) It is possible to monitor river water levels at many points. Yet, about half of the observation points have no established “flood danger water level.” (5) Local governments released a great amount of disaster information through social media before flooding occurred on some rivers. However, one must monitor multiple social media accounts and not just the account of one’s hometown.
Cite this article as:
S. Sato, S. Kure, S. Moriguchi, K. Udo, and F. Imamura, “Online Information as Real-Time Big Data About Heavy Rain Disasters and its Limitations: Case Study of Miyagi Prefecture, Japan, During Typhoons 17 and 18 in 2015,” J. Disaster Res., Vol.12 No.2, pp. 335-346, 2017.
Data files:
  1. [1] Cabinet Office, Application situation of Disaster Relied Law, [accessed Oct. 2, 2016]
  2. [2] Japan Metological Agency, Landslide warning information, [accessed Oct. 2, 2016]
  3. [3] M. Ushiyama, F. Imamura. T. Katada, and K. Yoshida, “Investigation of people’s behavior at heavy rainfall disaster in the highly flood disaster information age – A case study on the typhoon No. 0206 July, 2002 –,” Journal of Japan Society of Hydrogy and Water Resources, Vol. 17, pp. 150-158, 2004.
  4. [4] M. Ushiyama, “Characteristics of Human Damage by the Typhoon No. 0423 from October 20 to 21, 2004,” Natural Disaster Science, Vol. 24, pp. 257-265, 2005.
  5. [5] Disaster Management Headquarters of Fire and Disaster Managemtn Agency, Damage Situation Report of Typoon 18, No.37, 2015, [accessed Oct. 2, 2016]
  6. [6] Japan Metological Agency, High-resolution Precipitation Nowcasts, [accessed Oct. 2, 2016]
  7. [7] Japan Metological Agency, Analysis and forecast of precipitation / Shour term rain forecas / Japan, [accessed Oct. 2, 2016]
  8. [8] Ministry of Land, Infrastructure, Transport and Tourism, River Disaster Prevention Information (Kawa-no-bosai-joho), [accessed Oct. 2, 2016]
  9. [9] Japan Metological Agency, Lnadslide warning information, [accessed Oct. 2, 2016]
  10. [10] Ministry of Land, Infrastructure, Transport and Tourism,Renewal of River Disaster Prevention Information (Kawa-no-bosai-joho), [accessed Oct. 2, 2016]
  11. [11] The Real-time Landslide Risk Map, [accessed Oct. 2, 2016]
  12. [12] S. Kure, A. Hayashi, S. Moriguchi, T. Horiai, and H. Tanaka, “Evaluation of Probable Maximum Hydrodynamic Force of Flood Inundation at the Shibui River in September 2015,” Advances in River Engineering, Vol.22, pp. 297-302, 2016.
  13. [13] D. Komori, Draft Survey Report of Heavy Rainfall Disaster at the Nanakita River in September 2015 (1st report), [accessed Oct. 2, 2016]
  14. [14] T. Matsumura, Operational Intelligence: Theory of Operational Intelligence for Decision Making, The Nikkei, p. 220, 2006.
  15. [15] Kahoiku Shnpo, Miyagi pref have newly additional designated four rivers as subjects of constant monitoring, 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024