Deep Learning-Based Unmanned Surveillance Systems for Observing Water Levels

Abstract

Traditional surveillance systems for observing water levels are often complex, costly, and time-consuming. In this paper, we developed a low-cost unmanned surveillance system consisting of remote measuring stations and a monitoring center. The system uses a map-based Web service, as well as video cameras, water level analyzers, and wireless communication routers necessary to display real-time water level measurements of rivers and reservoirs on a Web platform. With the aid of a wireless communication router, the water level information is transmitted to a server connected to the Internet via a cellular network. By combining complex water level information of different river basins, the proposed system can be used to forecast and prevent flood disasters. In order to evaluate the proposed system, we conduct experiments using three feasible methods, including the difference method, dictionary learning, and deep learning. The experimental results show that the deep learning-based method performs best in terms of accuracy and stability.

Keywords

• Water level surveillance
• internet of things (IoT)
• dictionary learning
• deep learning
• convolutional neural network (CNN)