Regional disaster prevention activities must be evaluated in terms of their effectiveness and suitability, and then improved on the basis of this evaluation. Those who can evaluate such activities are required to have abundant on-site experience in and extensive knowledge on disaster prevention. However, there is a shortage of such talent, and the training and nurturing thereof requires considerable resources. To address these issues, machine learning was introduced in our previous study to automate the evaluation of such activities. In the present study, we propose the automatic generation of the evaluation model of such activities using the responses of a self-evaluation questionnaire as the input variables. The output variables are the results of a review committee consisting of experts on disaster prevention. This paper describes the application of the model to the fourth Disaster Prevention Map Contest, examines the predicted results, and discusses the application conditions and issues to be resolved.

1. [1] N. Okada, “Perspective of Comprehensive Disaster Prevention Science,” Y. Hagihara, N. Okada, and H. Tadano eds., Kyoto University Press, pp. 9-54, 2006 (in Japanese).
2. [2] T. Nagasaka and S. Ikeda, “Strategy and methodology for implementing disaster risk governance,” Japanese J. of Risk Analysis, Vol.17, No.3, pp. 13-23, 2008 (in Japanese with English abstract).
3. [3] T. Nagasaka, H. Tsubokawa, Y. Usuda, S. Nagamatsu, S. Miura, and S. Ikeda, “Participatory Risk Communication Method for Risk Governance Using Disaster Risk Scenarios,” J. Disaster Res., Vol.3, No.6, pp. 442-456, 2009.
4. [4] H. Kawabata, “Development of a Management System for Disaster Mitigation Activities in Local Community,” J. Archit. Plann., AIJ, Vol.73, No.631, pp. 1899-1906, 2008 (in Japanese with English abstract).
5. [5] S. Nagamatsu, T. Nagasaka, Y. Usuda, and S. Ikeda, “How can the ‘Coping Capacity of the Local Community Against Disasters’ be Evaluated?,” Report of the National Research Institute for Earth Science and Disaster Prevention, No.74, 2009 (in Japanese with English abstract).
6. [6] Ministry of Internal Affairs and Communications, “Survey on Approval Affairs Status of Territorial Organization,” 2003 (in Japanese), http://www.soumu.go.jp/main_content/000472604.pdf [accessed December 30, 2018]
7. [7] Q. Cui, K. Shimazaki, T. Yi, and Y. Usuda, “Automatic Performing Method of Regional Disaster Prevention Activities Evaluation,” J. of Social Safety Science, No.31, pp. 271-277, 2017 (in Japanese with English abstract).
8. [8] T. Y. Yi, H. Taguchi, Y. Usuda, T. Nagasaka, and H. Tsubokawa, “Effect of Risk Communication Method on Earthquake Disaster Prevention Activity A case study in Tsukuba City,” J. of JAEE, Vol.17, No.1, pp. 63-76, 2017 (in Japanese with English abstract).
9. [9] Disaster Prevention Contest Web, http://bosai-contest.jp/ [accessed December 30, 2018]
10. [10] Q. Cui, T. Y. Yi, K. Shimazaki, H. Taguchi, Y. Usuda, and H. Tsubokawa, “Role and Introduction Effect of Intermediate Support Function in Regional Disaster Prevention Activity,” J. of Japan Society for Natural Disaster Science, Vol.36, pp.53-67, 2017 (in Japanese with English abstract).
11. [11] NIED, http://www.bosai.go.jp/ [accessed December 30, 2018]
12. [12] T. Yasuda and H. Mase, “Real-time Prediction of Tsunami by Using Offshore Observation Data:Inverse Method and Artificial Neural Network Method,” J. of Coastal Zone Studies, Vol.22, No.4, pp.51-61, 2010 (in Japanese with English abstract).
13. [13] Y. Bai, B. Adriano, E. Mas, and S. Koshimura, “Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake,” J. Disaster Res., Vol.12, No.sp, pp. 646-655, 2017.
14. [14] J. Y. Choe, “A Fuzzy-Neural Network Model for Travel Demand on Evacuee-Trip Production at the beginning of the Great Hanshin-Awaji Earthquake,” J. of Social Safety Science, No.4, pp. 31-40, 2002 (in Japanese with English abstract).
15. [15] T. Yabe, Y. Sekimoto, A. Sudo, and K. Tsubouchi, “Predicting Delay of Commuting Activities Following Frequently Occurring Disasters Using Location Data from Smartphones,” J. Disaster Res., Vol.12, No.2, pp. 287-295, 2017.
16. [16] A. Miyamoto, K. Kawamura, H. Nakamura, and H. Yamamoto, “Development of Concrete Bridge Rating Expert System by Using Hierarchical Neural Networks,” J. of JSCE, No.644/VI-46, pp. 67-86, 2000 (in Japanese with English abstract).
17. [17] H. Teruhi, Y. Yamaguchi, and J. Akahani, “Water Leakage Detection System for Underground Pipes by Using Wireless Sensors and Machine Learning,” J. Disaster Res., Vol.12, No.3, pp. 557-568, 2017.
18. [18] S. Raschka, “Machine Learning and Deep Learning with Python: Data scientist Theory and Practice,” IMPRESS, 2016 (in Japanese).
19. [19] M. J. A. Berry and G.S. Linoff, “Data Mining Techniques,” KAIBUNDO, 1999.