High-Accuracy Surface-Perceiving Water Level Gauge With Self-Calibration for Hydrography

Zheng, Gan; Zong, Hongyan; Zhuan, Xiangtao; Wang, Lijuan

doi:10.1109/jsen.2010.2050138

Cited by 15 publications

(12 citation statements)

References 5 publications

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“…Different methods are used in redundancy in order to guarantee the availability and accuracy of measurements. Automatic water-level gauges are used for monitoring water-level by sensors that measure the level of water (i.e., float-type, pressure-type, ultrasonic-type and radar-type gauge) [4][5][6]. Moreover, video surveillance has become widely used for monitoring and measuring the system at hydro-power stations as a redundancy system [7].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…All-weather, real-time and automatic water-level monitoring is essential [4]. According to the principle of measurement, existing water-level gauges can be roughly divided into the float-type, the pressure-type, the ultrasonic-type and the radar-type [5,6,7]. However, float-type gauges have large cumulative measurement errors and need to be re-calibrated frequently.…”

Section: Introductionmentioning

confidence: 99%

Visual Measurement of Water Level under Complex Illumination Conditions

Zhang

Zhou

Liu

et al. 2019

Sensors

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Image-based water level measurement is a visual-sensing technique which automatically inspects the reading of the water line via image processing instead of the human eye. It can be realized easily on an existing video surveillance system and has advantages like low cost, non-contact, as well as results that are verifiable. It has the potential to be widely used in flood and waterlogging monitoring, while facing the challenge that water-line detection under complex natural or artificial illumination conditions is quite difficult in field applications. To handle this problem, a method is proposed assuming that the water line is generally located on the row with the largest local change of gray or edge features in the image of the water gauge. The water line is determined by coarse-to-fine detection of the position of the maximum mean difference (MMD) of the horizontal projections of gray and edge images. Image-based flow-level measurement systems were developed at two measurement sites. In situ comparative experiments were conducted with the float-type stage gauge and other image-based methods. The results show that the fusion of gray and edge features can overcome the shortcomings of single feature methods under complex illumination conditions such as dim light, glares, shadows and artificial night lighting. A coarse-to-fine strategy utilizes the periodicity of the surface pattern distribution of the standard bicolor water gauge, which improves the reliability of water-line detection. The resolution and accuracy of water-level measurement are 1 mm and 1 cm, respectively. In particular, the MMD value is efficient at identifying extremely unfavorable conditions and reducing gross errors.

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“…It is based on the measurement of hydrostatic pressure to deduce water height above the pressure detector/sensor.Float gauge. It is an old technique that is based on the movement of a floating object which is related to a mechanical system that deduces the position/height of the float, and thus the water surface level [5]. Fiber optic.…”

Section: Introductionmentioning

confidence: 99%

“…Float gauge. It is an old technique that is based on the movement of a floating object which is related to a mechanical system that deduces the position/height of the float, and thus the water surface level [5].…”

Section: Introductionmentioning

confidence: 99%

A New Water Level Measurement Method Combining Infrared Sensors and Floats for Applications on Laboratory Scale Channel under Unsteady Flow Regime

Majdalani

Chazarin

Moussa

2019

Sensors

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In this paper, we studied water transport under an unsteady flow regime in an experimental channel (4 m in length; 3 cm in width). Our experiments implicated some measuring requirements, specifically, a water level (WL) detection technique that is able to measure WL in a range of 2 cm with a precision of 1 mm. The existing WL detection techniques could not meet our measurement requirements. Therefore, we propose a new measurement method that combines two approaches: An “old” water contact technique (float) with a “new” remote non-contact technique (infrared sensor). We used an extruded polystyrene (XPS Foam) that needed some adequate treatment before using it as float in experimental measurements. The combination of IR-sensors with treated float foam lead to a sensitive measurement method that is able to detect flat and sharp flow signals, as well as highly dynamic variations of water surface level. Based on the experimental measurements of WL and outflow at the channel output, we deduced a loop rating curve that is suitable with a power law adjustment. The new measurement method could be extended to larger scale applications like rivers and more complicated cross section geometry of irregular shape.

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High-Accuracy Surface-Perceiving Water Level Gauge With Self-Calibration for Hydrography

Cited by 15 publications

References 5 publications

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

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

Visual Measurement of Water Level under Complex Illumination Conditions

A New Water Level Measurement Method Combining Infrared Sensors and Floats for Applications on Laboratory Scale Channel under Unsteady Flow Regime

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