# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 1 Introduction  
China has a vast territory, numerous rivers, and abundant water resources, which are important resources for human survival, and have greatly promoted the development of industry and society in our country. However, severe flood disasters have occurred many times in some areas, which not only seriously threatened the lives of residents, but also brought huge economic losses. Therefore, it is crucial to strengthen continuous monitoring of rivers to improve the accuracy and timeliness of flood warnings, as well as developing effective flood prevention strategies [1]. Although traditional techniques for measuring river flow velocity are capable of delivering precise results, they all belong to the intrusive contact measurement method category, and require a large amount of manpower and financial and material inputs. The current meter is the most widely used velocity measuring instrument at present. When water flows pass through the instrument, the rotor is driven to rotate, and the actual velocity can be calculated by recording the number of rotations of the rotor in a specified time. It requires complex deployment and depends on manual operation. Generally, it is only possible to measure and obtain data within a local range during the daytime. In addition, the Acoustic Doppler Current Profiler (ADCP) [2] is a contact measurement instrument that utilizes the acoustic doppler principle to calculate fluid velocity based on changes in echo frequency. These methods can become infeasible or even hazardous during extreme weather conditions and fail to satisfy the demands of continuous monitoring. In contrast, the development of non-contact flow measurement methods provides vital research value in this field [3], while also making up for the shortcomings of conventional contact methods. Non-contact techniques that utilize video image recognition have attracted widespread attention due to their advantages of high accuracy, low cost, safety, and strong real-time capabilities [4], and have gradually evolved into a mainstream technology and which has been successfully deployed in monitoring of river velocities and discharges. Particle image velocimetry (PIV) [5] involves dispersing tracer particles within the flow field and determining fluid velocity through cross-correlation analysis of their images. Large-scale particle image velocimetry (LSPIV) [6] extends PIV technology to measure velocities in large-scale flow fields for the first time, and uses cameras to capture the movement trajectory of natural floating objects such as foam, plant debris, and tiny ripples in the river. It eliminates the need for manual seeding of tracers, but can be limited by the availability and density of natural tracers and is prone to long computation times. Optical flow velocimetry (OFV) [7], based on the assumption of constant brightness between consecutive frames, obtains motion vectors by calculating the pixel displacement between adjacent frames. Space-time image velocimetry (STIV) [8] is a method to synthesize space-time images (STIs) by collecting information of the velocimetry line parallel to the river flow direction from river surface sequence images, and analyzes the main orientation of texture to calculate the velocity. [PERSON] et al. [9] determined the texture angle of space-time images using the Gradient Tensor Method (GTM); the principle is to divide the STI into several small windows of equal size. The texture angle of each small window is calculated according to the grayscale information of the image, and then the texture angle value of the whole image can be obtained. This method effectively addresses the limitations of LSPIV and greatly enhances computational efficiency, and the precision of the measured results is comparable to that of LSPIV. Further, [PERSON] et al.