A low-cost IoT-based deep learning method of water gauge measurement for flood monitoring

Abstract

Real-time and accurate measurement of the water level is a critical step in flood monitoring and management of water resources. In recent years, with the advent of the Internet of Things (IoTs) and cloud computing platforms and resources, the surveillance technology for water monitoring has been revolutionized due to the availability of high-resolution and portable cameras, robust image processing techniques, and cloud-enabled data fusion centers. However, despite the potential advantages of online water level monitoring of the rivers and lakes, some technical challenges need to be addressed before they can be fully utilized. Submersible sensor devices are frequently used for measuring water levels but are prone to damage from sediment deposition and many gauge detection techniques are inefficient at nighttime. In response, this paper presents a novel Internet of Things (IoT) based deep learning methodology that uses Mask-RCNN to accurately segment gauges from images even when there are distortions present. An automated and immediate water stage estimate is provided by this simple, low-cost method. The methodology’s applicability to water resource management systems and flood disaster prevention engineering opens up new possibilities for the deployment of intelligent IoT-based flood monitoring systems in the future.

Keywords

• Water level measurement
• remote sensing
• image processing
• deep learning
• low-cost surveillance system