5D World Map System for Disaster-Resilience Monitoring from Global to Local: Environmental AI System for Leading SDG 9 and 11

Shiori Sasaki, Yasushi Kiyoki, Madhurima Sarkar-Swaisgood, Jinmika Wijitdechakul, Irene Erlyn Wina Rachmawan, Sanjay Srivastava, Rajib Shaw, Chalisa Veesommai

研究成果: Conference contribution

抜粋

This paper presents a 5D World Map System's application for disasterresilience monitoring as "Environmental AI System" of each player's implementation of United Nation's SDG 9 and DGS 11 from global-level to regional-level, country-level, sub-regional-level and city-level. In Asia-Pacific, disaster risk is outpacing disaster resilience. The gap between risk and resiliencebuilding is growing in those countries with the least capacity to prepare for and respond to disasters. Using the Sensing-Processing-Actuation (SPA) functions of 5D World Map System, a disaster risk analysis can be conducted in multiple contexts, including regional, national, and sub-national. At the regional, national and subnational levels, the analysis will focus on identifying disaster risk hotspots through incorporating existing multi-hazard disaster risk and socio-economic risk information. The system will further be used to assess future risks through integration of global climate scenarios downscaled to the region as well as countries. This paper presents the design of two new actuation functions of 5D World Map System: (1) Short-term warning with prediction and push alert and (2) Long-term warning with context-dependent multidimensional visualization, and examines the applicability of these functions by indicating that (1) will support both resident and those who are working at the operational level by being customized to disaster risk analysis for each target region/country/area, and (2) assist both policy-makers and sectoral ministries in target countries to use the analysis for evidence-based policy formulation, planning and investment towards building disaster-resilient society.

元の言語English
ホスト出版物のタイトルInformation Modelling and Knowledge Bases XXXI
編集者Ajantha Dahanayake, Janne Huiskonen, Yasushi Kiyoki, Bernhard Thalheim, Hannu Jaakkola, Naofumi Yoshida
出版者IOS Press
ページ306-323
ページ数18
ISBN(電子版)9781643680446
DOI
出版物ステータスPublished - 2019 12 13
イベント29th International Conference on Information Modeling and Knowledge Bases, EJC 2019 - Lappeenranta, Finland
継続期間: 2019 6 32019 6 7

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
321
ISSN(印刷物)0922-6389

Conference

Conference29th International Conference on Information Modeling and Knowledge Bases, EJC 2019
Finland
Lappeenranta
期間19/6/319/6/7

ASJC Scopus subject areas

  • Artificial Intelligence

フィンガープリント 5D World Map System for Disaster-Resilience Monitoring from Global to Local: Environmental AI System for Leading SDG 9 and 11' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Sasaki, S., Kiyoki, Y., Sarkar-Swaisgood, M., Wijitdechakul, J., Rachmawan, I. E. W., Srivastava, S., Shaw, R., & Veesommai, C. (2019). 5D World Map System for Disaster-Resilience Monitoring from Global to Local: Environmental AI System for Leading SDG 9 and 11. : A. Dahanayake, J. Huiskonen, Y. Kiyoki, B. Thalheim, H. Jaakkola, & N. Yoshida (版), Information Modelling and Knowledge Bases XXXI (pp. 306-323). (Frontiers in Artificial Intelligence and Applications; 巻数 321). IOS Press. https://doi.org/10.3233/FAIA200022