EventsOHOW 2022 – The 1st International Symposium on One Health, One World
Published
with-doi10.3390/ohow2022-13656 (registering DOI)
This submission belongs to the session S1. Urban Safety and Disaster Mitigation of the event OHOW 2022 – The 1st International Symposium on One Health, One World
Published date
17 Nov, 2022
Academic Editor
author-avatarWataru Takeuchi
Citation
Akira Kodaka, Natt Leelawat, Jing Tang, Naohiko Kohtake, PILOT ANALYSIS TO ASSESS BUSINESS CONTINUITY OF INDUSTRIAL COMPLEXES IN THAILAND BY CHANGES IN NITROGEN DIOXIDE CONCENTRATION, in Proceedings of OHOW 2022 – The 1st International Symposium on One Health, One World, Amari Pattaya Hotel, 8 December–10 December 2022, MDPI: Basel, Switzerland, doi: 10.3390/ohow2022-13656
Share
Email
Facebook
Twitter
LinkedIn

PILOT ANALYSIS TO ASSESS BUSINESS CONTINUITY OF INDUSTRIAL COMPLEXES IN THAILAND BY CHANGES IN NITROGEN DIOXIDE CONCENTRATION

Naohiko Kohtake 3
1. Keio University, Japan
2. Chulalongkorn University
3. Keio University
Abstract

The 2011 Flood in Thailand triggered an acceleration of business continuity planning in industrial complexes. In these circumstances, the development of an objective indicator to measure the effectiveness of business continuity planning is necessary for future resilient business. There are studies conducted on understanding the heat emissions of industrial complexes [1], but there is limited scientific knowledge to understand the activities of industrial complexes from the perspective of business continuity. Therefore, this study set the research question of whether it is possible to objectively assess the status of activities in industrial complexes from indirect data and conducted a preliminary study of the effective feasibility of this approach was conducted.

Keywords
Business Continuity
nitrogen dioxide
principal component analysis
COVID-19
Thailand
TROPICAL PEATLANDS CANAL SEGMENTATION FROM HIGH RESOLUTION OPTICAL IMAGE USING U-NET ARCHITECTURE
Basic Study on Urgency Classification Model for Sewage Pipe Using Machine Learning