Detection of Flood-Prone Areas Using Geospatial Data with Deep Learning Method Approach

Leni Novianti; Ade Silvia Handayani; Nyayu Latifah Husni; Darma Prabudi; Hetty Meileni; Marlina Sylvia

doi:10.2991/978-94-6463-118-0_47

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Detection of Flood-Prone Areas Using Geospatial Data with Deep Learning Method Approach

Authors

Leni Novianti¹^{, *}, Ade Silvia Handayani¹, Nyayu Latifah Husni¹, Darma Prabudi¹, Hetty Meileni¹, Marlina Sylvia²

¹Department of Informatics Management, Politeknik Negeri Sriwijaya, Palembang, Indonesia

²Public Works and Spatial Planning Office (PUPR), Palembang, South of Sumatera, Indonesia

^*Corresponding author. Email: leninovianti16@gmail.com

Corresponding Author

Leni Novianti

Available Online 26 June 2023.

DOI: 10.2991/978-94-6463-118-0_47 How to use a DOI?
Keywords: Flood Detection; Deep Learning; Computer Vision; GIS Application
Abstract: The flood problem that happens in Palembang City occurs due to seasonal tidal floods and flooding as a result of inundation caused by rainwater which results in the overflow of the Musi River. The Musi River that flows through the City of Palembang canon accommodates the increasing flow of water and high rainfall which can cause problems to many flood-prone areas along The Musi River. The flood that happens in Palembang City can reach 30cm and this motorbike riders can cause interfere with their travel. Flooding that occurs on the streets can cause damage, especially to vehicles, both two-wheeled and four-wheeled. In addition, flooding can also cause traffic congestion because vehicles have to slow down and this can cause long queues of vehicles on top of that flooding can affect roads and other infrastructure in Palembang City. To determine the volume of water flow we can use a CCTV camera at a strategic location and use Computer Vision alongside Deep Learning to Determine Water Level, and we can plot the data Into Geospatial Data that can be used to model Flood Hazard Areas.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 6th FIRST 2022 International Conference (FIRST-ESCSI-22)
Series: Atlantis Highlights in Engineering
Publication Date: 26 June 2023
ISBN: 10.2991/978-94-6463-118-0_47
ISSN: 2589-4943
DOI: 10.2991/978-94-6463-118-0_47 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Leni Novianti
AU  - Ade Silvia Handayani
AU  - Nyayu Latifah Husni
AU  - Darma Prabudi
AU  - Hetty Meileni
AU  - Marlina Sylvia
PY  - 2023
DA  - 2023/06/26
TI  - Detection of Flood-Prone Areas Using Geospatial Data with Deep Learning Method Approach
BT  - Proceedings of the 6th FIRST 2022 International Conference (FIRST-ESCSI-22)
PB  - Atlantis Press
SP  - 458
EP  - 465
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-118-0_47
DO  - 10.2991/978-94-6463-118-0_47
ID  - Novianti2023
ER  -

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