Abstract
Image-based gauging stations offer the potential for substantial enhancement in the monitoring networks of river water levels. Nonetheless, the majority of camera gauges fall short in delivering reliable and precise measurements because of the fluctuating appearance of water in the rivers over the course of the year. In this study, we introduce a method for measuring water levels in rivers using both the traditional continuous image subtraction (CIS) approach and a SegNet neural network based on deep learning computer vision. The historical images collected from on-site investigations were employed to train three neural networks (SegNet, U-Net, and FCN) in order to evaluate their effectiveness, overall performance, and reliability. The research findings demonstrated that the SegNet neural network outperformed the CIS method in accurately measuring water levels. The root mean square error (RMSE) between the water level measurements obtained by the SegNet neural network and the gauge station's readings ranged from 0.013 m to 0.066 m, with a high correlation coefficient of 0.998. Furthermore, the study revealed that the performance of the SegNet neural network in analyzing water levels in rivers improved with the inclusion of a larger number of images, diverse image categories, and higher image resolutions in the training dataset. These promising results emphasize the potential of deep learning computer vision technology, particularly the SegNet neural network, to enhance water level measurement in rivers. Notably, the quality and diversity of the training dataset play a crucial role in optimizing the network's performance. Overall, the application of this advanced technology holds great promise for advancing water level monitoring and management in river systems.