Parallel Session 1.3: Experience with demand-based risk assessment in Korea (Kyunghee Uni)

Introduction
A risk assessment for climate change is an important basis for setting up an adaptation policy. Admitting the fact that current national climate change adaptation policy has been established from the top down risk assessment, there is a need to incorporate demand-based bottom up approach to reflect diverse needs for climate change adaptation. Advanced technologies such as big data, data mining, deep learning, GIS, etc. could be a very useful tool to identify complicated climate change risks. In this session, we seek to find good case studies of climate change risk assessments using artificial intelligence technologies.

Objectives

• To give case studies for data-driven risk assessment using artificial intelligence technologies;
• To explore an applicable methodology to assess demand-based climate change risks;
• To share knowledge and experiences between academia and related business sector.

Expected outcomes

This session is expected to:

• Enhance information on Korea situation of climate change risk assessment;
• Find a new methodology to assess climate change risks using artificial intelligence technologies;
• Suggest a future research topic considering current state of knowledge.

Draft agenda

• Opening (2 min)
• Congratulatory message (3 min)
• Presentation (65 min):
  • P1) Research Topic Introduction of the Specialized Graduate School for Climate Change in Kyung Hee University (Prof. Gayoung Yoo)
  • P2) Case Study for Urban Climate Change Adaptation Using GIS and RS (Prof. Donghyung Seo)
  • P3) Methodology for Climate Change Risk Assessment Using Text Mining (Dr. Young-eun Kang)
  • P4) Study for Smart Big Board System Development and Management (Team leader. Minsoo Go)
• Discussion (20 min):
  • Prof. Geunyoung Yoon (Kyung-Hee University)
  • Dr. Jiyoung Sin (KEI)