Measurement of Water Level in Urban Streams under Bad Weather Conditions

Abstract

Flood control and water resources management require monitoring the water level in rivers and streams. Water level measurement techniques increasingly consider image processing procedures. Most of the systems use a staff gauge to support the waterline detection. However, these techniques can fail when applied to urban stream channels due to water undulation, debris on the water surface, and traces of rain captured by the camera, and other adverse effects on images can be quite dramatic on the results. The importance of considering these effects is that they are usually associated with the variation in the water level with the occurrence of rain. The technique proposed in this work uses a larger detection zone to minimize the effects that tend to obstruct the waterline. The developed system uses an infrared camera to operate during the day and night. Images acquired in different weather conditions helped to evaluate the proposed technique. The water level measurement accuracy was about 1.8 cm for images taken during the day and 2.8 cm for images taken at night. During short periods of heavy rain, the accuracy was 2.6 cm for the daytime and 3.4 cm for the nighttime. Infrared lighting can improve detection accuracy at night. The developed technique provides good accuracy under different weather conditions by combining information from various detection positions to deal with waterline detection issues.

Noncontact Automatic Water-Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep Learning

“…Regarding the contactless methods, although ultrasonic sensors are easy to install, they have been shown to be problematic when examining turbulent water [6], which usually happens during flooding events in volcanic islands' water channels [3]. As a result, image-based sensors were found to be suitable for monitoring the water channels in these locations, presenting a low installation and maintenance cost [7]. However, satellite images lack enough spatial and temporal resolutions, particularly for narrow water streams [8], while the use of unmanned vehicles is likely not to be suitable for small urban areas [9].…”

Field performance of the GaugeCam image-based water level measurement system

“…The latter is particularly important in cases or areas where establishing a stage-discharge rating curve is difficult, when possible at all. Research reported on image-based hydrological monitoring tends to either focus on Large Scale Particle Image Velocimetry (LS-PIV) to compute discharge [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], on discharge measurements using machine learning [29], on flood detection from surveillance cameras [30][31][32][33], or, on the stage measurement itself [2,[34][35][36][37][38][39][40][41][42][43] from which discharge is often calculated.…”

Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry

“…Zhang et al. (2019) and Azevedo and Brás (2021) developed monitoring systems using a single infrared camera to mitigate the visibility issue and image quality. The proposed method by Zhang et al.…”

“…The proposed method by Zhang et al (2019) provided comparable accuracy to the existing float-type gauges. Average accuracy of 1.8 cm for the daytime and 2.8 cm for the nighttime measurements were determined by Azevedo and Brás (2021). Marker points and recognizable structures such as buildings, concrete walls, rocks, ground control points and even the temporal texture of the changing water surface are alternative to staff gauge for detecting and obtaining water line around the monitoring area (Eltner et al, 2018;Leduc et al, 2018;Young et al, 2015).…”

“…The technique is subject to poor visibility due to weather condition, ambient noise and image distortions (e.g., Chen et al, 2021;Kuo & Tai, 2022;Lin et al, 2018). Zhang et al (2019) and Azevedo and Brás (2021) developed monitoring systems using a single infrared camera to mitigate the visibility issue and image quality. The proposed method by Zhang et al (2019) provided comparable accuracy to the existing float-type gauges.…”