Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Development of a Targeted Recommendation Model for Earthquake Risk Prevention in the Whole Disaster Chain

  • Su, Xiaohui (School of Information Science and Technology, Beijing Forestry University) ;
  • Ming, Keyu (School of Information Science and Technology, Beijing Forestry University) ;
  • Zhang, Xiaodong (College of Land Science and Technology, China Agricultural University) ;
  • Liu, Junming (College of Land Science and Technology, China Agricultural University) ;
  • Lei, Da (School of Information Science and Technology, Beijing Forestry University)
  • Received : 2020.09.04
  • Accepted : 2020.12.29
  • Published : 2021.02.28
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Abstract

Strong earthquakes have caused substantial losses in recent years, and earthquake risk prevention has aroused a significant amount of attention. Earthquake risk prevention products can help improve the self and mutual-rescue abilities of people, and can create convenient conditions for earthquake relief and reconstruction work. At present, it is difficult for earthquake risk prevention information systems to meet the information requirements of multiple scenarios, as they are highly specialized. Aiming at mitigating this shortcoming, this study investigates and analyzes four user roles (government users, public users, social force users, insurance market users), and summarizes their requirements for earthquake risk prevention products in the whole disaster chain, which comprises three scenarios (pre-quake preparedness, in-quake warning, and post-quake relief). A targeted recommendation rule base is then constructed based on the case analysis method. Considering the user's location, the earthquake magnitude, and the time that has passed since the earthquake occurred, a targeted recommendation model is built. Finally, an Android APP is implemented to realize the developed model. The APP can recommend multi-form earthquake risk prevention products to users according to their requirements under the three scenarios. Taking the 2019 Lushan earthquake as an example, the APP exhibits that the model can transfer real-time information to everyone to reduce the damage caused by an earthquake.

Keywords

Acknowledgement

This paper is funded by the National Key Research and Development Program of China (No. 2018YFC1508901) and the Fundamental Research Funds for the Central Universities (No. BLX2013034).

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