# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
###### 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. flood monitoring; computer vision; image processing; edge computing; Internet of things +
Footnote †: journal: Computer vision  
existing surveillance cameras infrastructure can also be used to perform flood monitoring, which is beneficial in a smart city. In this paper, we propose and present a camera-based real-time flood monitoring system using computer vision techniques underlined by edge computing and IoT system approaches. We explore, design and deploy the proposed system in a real-world water level monitoring facility in a rural location. Our system leverages the advantages of camera-based system over the typical specialised noncamera systems. However, in a camera-based system, the raw information of images needs to be processed to extract essential information (e.g., water level). The image processing of these digital images is often performed on a computing device. Depending on the system setup, different computing paradigms are classified based on the location where the computational process is performed. In a cloud-computing setup, the raw image data are sent across the network to be processed on the computer located at a distant facility commonly known as a server. The process of transmitting raw data to the server becomes a challenge especially when the sensor node is located in a remote location where network connectivity is limited [4]. In contrast, in an edge-computing setup, the image data are processed in situ using the computing device located close to the sensor node which then extracts and transmits the important parameters to the server. With the advancement in computing technology, the edge computing device is becoming increasingly compact, powerful and power-efficient [5; 6]. These features increase the viability of implementing edge-based processing tasks in areas where network bandwidth and power resources are scarce. The edge computing setup in the context of the Internet of things (IoT) enhances the capabilities of a real-time monitoring system in rural areas when extracting and processing important data are concerned [7; 8; 9]  
This paper aims to investigate the viability of utilising red, green, blue and depth (RGB-D) image sensor technology coupled with edge-based processing for the application of flood monitoring. The system setup is deployed and tested in a real-life scenario to further reinforce the feasibility of the implementation.