Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things

Obaid Rafiq Jan, Hudyjaya Siswoyo Jo, Riady Siswoyo Jo and Jonathan Kua ()
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Obaid Rafiq Jan: Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching 93350, Sarawak, Malaysia
Hudyjaya Siswoyo Jo: Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching 93350, Sarawak, Malaysia
Riady Siswoyo Jo: School of Engineering and Physical Sciences, Heriot-Watt University Malaysia Campus, Putrajaya 62200, Federal Territory of Putrajaya, Malaysia
Jonathan Kua: School of Information Technology, Deakin University, Geelong 3220, Victoria, Australia

Future Internet, 2022, vol. 14, issue 11, 1-19

Abstract: Natural disasters such as severe flooding can cause catastrophic losses to properties and human lives. Constant real-time water level monitoring prior to a flooding event can minimise damages and casualties. Many of the currently deployed water level monitoring systems typically use a combination of float-type or ultrasonic sensing, image processing and computer vision techniques. However, these systems incur high computing and hardware requirements, which hinder the deployment of such systems in resource-constrained and low-cost environments. The recent development of technologies empowered by the Internet of things (IoT) and edge computing have enabled real-time systems to be deployed at a significantly lower cost and a far more distributed manner. In this paper, we propose an architecture for flood monitoring using RGB-D cameras with stereoscopic capabilities to measure the water level in an open environment. Our system uses image preprocessing techniques to account for chromatic aberration due to overexposure, followed by postprocessing before the depth readings are extracted. Data processing and water level information extraction are entirely performed on an edge computing device, therefore greatly reducing the amount of data transmitted to the cloud server. We practically implemented and experimentally validated this system in the real world, under a wide range of weather and lighting conditions. Our results showed promising outcomes and demonstrated the applicability of our proposed system in a wider context.

Keywords: flood monitoring; computer vision; image processing; edge computing; Internet of things (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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