Image-Based River Water Level Estimation for Redundancy Information Using Deep Neural Network

Abstract

Monitoring and management of water levels has become an essential task in obtaining hydroelectric power. Activities such as water resources planning, supply basin management and flood forecasting are mediated and defined through its monitoring. Measurements, performed by sensors installed on the river facilities, are used for precisely information about water level estimations. Since weather conditions influence the results obtained by these sensors, it is necessary to have redundant approaches in order to maintain the high accuracy of the measured values. Staff gauge monitored by conventional cameras is a common redundancy method to keep track of the measurements. However, this method has low accuracy and is not reliable once it is monitored by human eyes. This work proposes to automate this process by using image processing methods of the staff gauge to measure and deep neural network to estimate the water level. To that end, three models of neural networks were compared: the residual networks (ResNet50), a MobileNetV2 and a proposed model of convolutional neural network (CNN). The results showed that ResNet50 and MobileNetV2 present inferior results compared to the proposed CNN.

Evaluation of deep learning computer vision for water level measurements in rivers

“…The existing literature on the application of deep learning computer vision to water level analysis has predominantly focused on comparing the differences among various deep learning methods [ 23 , 27 , 43 , 44 ] or comparing the water level analyzed by deep learning with the measured water level [ 17 , 45 , 46 ]. However, traditional image processing methods have not been discussed or compared, and only a few studies have examined the effect of image datasets on the analysis results during deep learning network training.…”

A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology

“…Furthermore, since CNNs perform well in object detection, especially in handwritten digit recognition, many convolutional neural network models have also been applied for gauging character location and recognition tasks, such as Yolov5s [64], FCOS [65] and CornerNet [66]. Fleury et al [67] used the character recognition and counting method to make training set. Thereafter, the CNN was trained to estimate the water level end-to-end.…”

Design of a Groundwater Level Monitoring System Based on Internet of Things and Image Recognition

“…Based on Kalman filter, Hwung et al [11] proposed a predictive correction method to estimate the position of horizontal plane. Fleury et al [12] positioned the pre-installed calibration line and water level line by subtracting the two adjacent frames of the video image, and calculated the water level value according to their relative position. Hu and Li [13] held that the energy of flowing water area is relatively low in the high frequency area of the continuous video frames, and calculated the water level by spectral transformation of the video image.…”