Artificial Intelligence-Integrated Water Level Monitoring System for Flood Detection Enhancement

Authors

  • Kien-Fei Lee Centre for Advanced Devices and Systems, Faculty of Engineering, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia
  • Zi-Neng Ng School of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia
  • Kar-Ban Tan Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • Ruthramurthy Balachandran Department of Electronics and Communication Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, P O Box 1888, Adama, Ethiopia
  • Abraham Shiau-Iun Chong KESPRO Consultants Sdn Bhd, Bandar Sunway, Subang Jaya, Selangor, 47500, Malaysia
  • Kah-Yoong Chan Centre for Advanced Devices and Systems, Faculty of Engineering, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia

Keywords:

Water level monitoring system, Internet of Things (IoT), ESP-32 Camera

Abstract

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.  

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References

Admad Fedzil Ismail, Mohammad Kamrul Hasan, Nor Bazilah Bopi, Suriza A. Zabidi, "Development of a River Basin Monitoring System for Malaysia," International Journal of u- and e- Service. Science and Technology, vol. 9, p. 1, 2016.

Ching Yun Chen, Baharudin, Mohd Ekhwan T, Lee Yook Heng, Maimon A, Salmijah, "Impact of Climate Change on Flood Risk in the River Basin of Malaysia," Disaster Advances, vol. 6, no. 10, p. 1, 2013.

Kodathala Sai Varun, Kandagadla Ashok, Vunnam Rakesh Chiowdary, C.S.K. Raju, "Water Level Management Using Ultrasonic Sensor (Automation)," International Journal of Computer Sciences and Engineering, vol. 6, no. 6, p. 801, 2018.

Fathin Izzaty Fahmi, Kah-Yoong Chan, Chu-Liang Lee, Wai-Leong Pang, Gregory Soon How Thien, Zi-Neng Ng, An Intelligent Waste Management System Enabled by IoT, International Journal of Intelligent Systems and Applications in Engineering (IJISAE) (2023) 11(8s), 401–405.

https://my.cytron.io/p-solar-panel-18v-555ma-10w.

https://my.cytron.io/p-38v-step-down-5v-5a-regulator.

https://www.instructables.com/DIY-Lithium-18650-Cells-Charger-by-Using-TP4056-Mo/.

https://my.cytron.io/p-3.7v-2000mah-li-ion-battery.

D. L. Baggio, "Mastering OpenCV with Practical Computer Vision Project," Packt Publishing Ltd, Birmingham, 2015.

Gary Bradski, Adrain Kaehler, "Learning OpenCV," Media, Inc, CA, 2008.

David Nettikadan, Subodh Raj M.S., "Smart Community Monitoring System using Thingspeak IoT Plaform," International Journal of Applied Engineering Research ISSN, vol. 13, no. 17, p. 13404, 2018.

H. R. Esmaeel, "Apply Android Studio (SDK) Tools," International Journal of Advanced Research in, vol. 5, no. 5, p. 89, 2015 .

Jun-Dong Chang, Shyr-Shen Yu, Hong-Hao Chen, and Chwei-Shyong Tsai, "HSV-based Color Texture Image Classification using Wavelet Transform," Journal of Computer, vol. 20, no. 4, p. 64, 2010.

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Published

24.03.2024

How to Cite

Lee, K.-F. ., Ng, Z.-N. ., Tan, K.-B. ., Balachandran, R. ., Chong, A. S.-I. ., & Chan, K.-Y. . (2024). Artificial Intelligence-Integrated Water Level Monitoring System for Flood Detection Enhancement. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 336–340. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5071

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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