research-article

Urban Flood Management: Bangkok Survey

Authors:

Narongrit Waraporn,

Vajirasak Vanijja,

Montri Supattatham,

Olarn Rojanapornpun,

Nannapat Termsak,

Piraporn SirisawatvatanaAuthors Info & Claims

IAIT '21: Proceedings of the 12th International Conference on Advances in Information Technology

Article No.: 10, Pages 1 - 8

https://doi.org/10.1145/3468784.3468888

Published: 20 July 2021 Publication History

Abstract

Completeness of flood management by the city authority has been limited. The requirement from the city, residents, and city staffs must be integrated with the digital technology disruption and the global climate change in order to design the flood management platform with its comprehensiveness. The survey from experts of flood management team was conducted. The results of the survey incorporated with the modern flood prediction models such as rain model, canal and sewer model, water over flow model, water gate and pump simulation and warning system were proposed. In order to gain the social responsibility of community, we proposed the city community platform for flood events. It included the resident enable system and city staff enable system.

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Cited By

Azizi BWaraporn N(2021)Spatiotemporal Data of Vegetation Images for Convolutional Neural Network: Okra Case Study2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON53757.2021.9666664(0504-0508)Online publication date: 1-Dec-2021
https://doi.org/10.1109/UEMCON53757.2021.9666664

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Urban Flood Management: Bangkok Survey

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IAIT '21: Proceedings of the 12th International Conference on Advances in Information Technology

June 2021

281 pages

ISBN:9781450390125

DOI:10.1145/3468784

Copyright © 2021 ACM.

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Publication History

Published: 20 July 2021

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IAIT2021

IAIT2021: The 12th International Conference on Advances in Information Technology

June 29 - July 1, 2021

Bangkok, Thailand

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Overall Acceptance Rate 20 of 47 submissions, 43%

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Cited By

Azizi BWaraporn N(2021)Spatiotemporal Data of Vegetation Images for Convolutional Neural Network: Okra Case Study2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON53757.2021.9666664(0504-0508)Online publication date: 1-Dec-2021
https://doi.org/10.1109/UEMCON53757.2021.9666664

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