Artificial intelligence-integrated water level monitoring system for flood detection enhancement
Flash floods are increasingly becoming a common disaster in Malaysia, triggered by a combination of natural and human induced factors. The natural factors include climate changes, landforms due to the environmental impacts, while the human-induced factors are associated with the negligence in river...
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| Format: | Article |
| Language: | English |
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Ismail Saritas
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/116367/ http://psasir.upm.edu.my/id/eprint/116367/1/116367.pdf |
| _version_ | 1848866987005968384 |
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| author | Tan, Kar Ban Kien, Fei Lee Ng, Zi Neng Balachandran, Ruthramurthy Chong, Abraham Shiau Lun Chan, Kah Yoong |
| author_facet | Tan, Kar Ban Kien, Fei Lee Ng, Zi Neng Balachandran, Ruthramurthy Chong, Abraham Shiau Lun Chan, Kah Yoong |
| author_sort | Tan, Kar Ban |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Flash floods are increasingly becoming a common disaster in Malaysia, triggered by a combination of natural and human induced factors. The natural factors include climate changes, landforms due to the environmental impacts, while the human-induced factors are associated with the negligence in river conservation, clogged drainage, and polluted water retention systems due to industrial and domestic wastes. These factors affect the water levels in rivers and drainage systems, leading to potential flash floods once the danger mark is exceeded. Flash floods could result in severe property damage and even loss of lives. Considering the devastating impact of flash floods, it is imperative to develop an early warning system that facilitates timely remedial measures. This system could monitor the water levels in rivers and other water retention areas. Herein, this study aims to design a water level monitoring system using a cost-effective camera module powered by the Internet of Things (IoT). The system, which includes an ESP32-Camera module powered by a solar panel, captures the water level data using OpenCV at one-minute intervals. Then, the data are made available on IoT platforms like ThingSpeak, enabling the authorized parties to keep track of the critical water levels in water retention areas. |
| first_indexed | 2025-11-15T14:29:19Z |
| format | Article |
| id | upm-116367 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:29:19Z |
| publishDate | 2024 |
| publisher | Ismail Saritas |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1163672025-03-27T06:21:00Z http://psasir.upm.edu.my/id/eprint/116367/ Artificial intelligence-integrated water level monitoring system for flood detection enhancement Tan, Kar Ban Kien, Fei Lee Ng, Zi Neng Balachandran, Ruthramurthy Chong, Abraham Shiau Lun Chan, Kah Yoong Flash floods are increasingly becoming a common disaster in Malaysia, triggered by a combination of natural and human induced factors. The natural factors include climate changes, landforms due to the environmental impacts, while the human-induced factors are associated with the negligence in river conservation, clogged drainage, and polluted water retention systems due to industrial and domestic wastes. These factors affect the water levels in rivers and drainage systems, leading to potential flash floods once the danger mark is exceeded. Flash floods could result in severe property damage and even loss of lives. Considering the devastating impact of flash floods, it is imperative to develop an early warning system that facilitates timely remedial measures. This system could monitor the water levels in rivers and other water retention areas. Herein, this study aims to design a water level monitoring system using a cost-effective camera module powered by the Internet of Things (IoT). The system, which includes an ESP32-Camera module powered by a solar panel, captures the water level data using OpenCV at one-minute intervals. Then, the data are made available on IoT platforms like ThingSpeak, enabling the authorized parties to keep track of the critical water levels in water retention areas. Ismail Saritas 2024-03-24 Article PeerReviewed text en cc_by_sa_4 http://psasir.upm.edu.my/id/eprint/116367/1/116367.pdf Tan, Kar Ban and Kien, Fei Lee and Ng, Zi Neng and Balachandran, Ruthramurthy and Chong, Abraham Shiau Lun and Chan, Kah Yoong (2024) Artificial intelligence-integrated water level monitoring system for flood detection enhancement. International Journal of Intelligent Systems and Applications in Engineering, 12 (19 spec.). pp. 336-340. ISSN 2147-6799 https://www.ijisae.org/index.php/IJISAE/article/view/5071 |
| spellingShingle | Tan, Kar Ban Kien, Fei Lee Ng, Zi Neng Balachandran, Ruthramurthy Chong, Abraham Shiau Lun Chan, Kah Yoong Artificial intelligence-integrated water level monitoring system for flood detection enhancement |
| title | Artificial intelligence-integrated water level monitoring system for flood detection enhancement |
| title_full | Artificial intelligence-integrated water level monitoring system for flood detection enhancement |
| title_fullStr | Artificial intelligence-integrated water level monitoring system for flood detection enhancement |
| title_full_unstemmed | Artificial intelligence-integrated water level monitoring system for flood detection enhancement |
| title_short | Artificial intelligence-integrated water level monitoring system for flood detection enhancement |
| title_sort | artificial intelligence-integrated water level monitoring system for flood detection enhancement |
| url | http://psasir.upm.edu.my/id/eprint/116367/ http://psasir.upm.edu.my/id/eprint/116367/ http://psasir.upm.edu.my/id/eprint/116367/1/116367.pdf |