Denoising of river surface photogrammetric DEMs using deep learning

2021 ◽  
Author(s):  
Radosław Szostak ◽  
Przemysław Wachniew ◽  
Mirosław Zimnoch ◽  
Paweł Ćwiąkała ◽  
Edyta Puniach ◽  
...  
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Water Level ◽  
Water Surface ◽  
Research University ◽  
Static Characteristic ◽  
Water Levels ◽  
Training Dataset ◽  
Observation Data ◽  
Characteristic Points

<p>Unmanned Aerial Vehicles (UAVs) can be an excellent tool for environmental measurements due to their ability to reach inaccessible places and fast data acquisition over large areas. In particular drones may have a potential application in hydrology, as they can be used to create photogrammetric digital elevation models (DEM) of the terrain allowing to obtain high resolution spatial distribution of water level in the river to be fed into hydrological models. Nevertheless, photogrammetric algorithms generate distortions on the DEM at the water bodies. This is due to light penetration below the water surface and the lack of static characteristic points on water surface that can be distinguished by the photogrammetric algorithm. The correction of these disturbances could be achieved by applying deep learning methods. For this purpose, it is necessary to build a training dataset containing DEMs before and after water surfaces denoising. A method has been developed to prepare such a dataset. It is divided into several stages. In the first step a photogrammetric surveys and geodetic water level measurements are performed. The second one includes generation of DEMs and orthomosaics using photogrammetric software. Finally in the last one the interpolation of the measured water levels is done to obtain a plane of the water surface and apply it to the DEMs to correct the distortion. The resulting dataset was used to train deep learning model based on convolutional neural networks. The proposed method has been validated on observation data representing part of Kocinka river catchment located in the central Poland.</p><p>This research has been partly supported by the Ministry of Science and Higher Education Project “Initiative for Excellence – Research University” and Ministry of Science and Higher Education subsidy, project no. 16.16.220.842-B02 / 16.16.150.545.</p>

scholarly journals Can GNSS-R Detect Abrupt Water Level Changes?

2020 ◽  
Vol 12 (21) ◽  
pp. 3614
Author(s):  
Sajad Tabibi ◽  
Olivier Francis
Keyword(s):  
Water Level ◽  
Vertical Velocity ◽  
Energy Demand ◽  
Water Surface ◽  
Spatial Scales ◽  
Satellite System ◽  
Water Levels ◽  
Level Gauge ◽  
Surface Reflection ◽  
Microwave Radar

Global navigation satellite system reflectometry (GNSS-R) uses signals of opportunity in a bi-static configuration of L-band microwave radar to retrieve environmental variables such as water level. The line-of-sight signal and its coherent surface reflection signal are not separate observables in geodetic GNSS-R. The temporally constructive and destructive oscillations in the recorded signal-to-noise ratio (SNR) observations can be used to retrieve water-surface levels at intermediate spatial scales that are proportional to the height of the GNSS antenna above the water surface. In this contribution, SNR observations are used to retrieve water levels at the Vianden Pumped Storage Plant (VPSP) in Luxembourg, where the water-surface level abruptly changes up to 17 m every 4-8 h to generate a peak current when the energy demand increases. The GNSS-R water level retrievals are corrected for the vertical velocity and acceleration of the water surface. The vertical velocity and acceleration corrections are important corrections that mitigate systematic errors in the estimated water level, especially for VPSP with such large water-surface changes. The root mean square error (RMSE) between the 10-min multi-GNSS water level time series and water level gauge records is 7.0 cm for a one-year period, with a 0.999 correlation coefficient. Our results demonstrate that GNSS-R can be used as a new complementary approach to study hurricanes or storm surges that cause abnormal rises of water levels.

scholarly journals Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera

2021 ◽  
Vol 11 (20) ◽  
pp. 9691
Author(s):  
Nur Atirah Muhadi ◽  
Ahmad Fikri Abdullah ◽  
Siti Khairunniza Bejo ◽  
Muhammad Razif Mahadi ◽  
Ana Mijic
Keyword(s):  
Deep Learning ◽  
Water Level ◽  
River Water ◽  
Correlation Coefficient ◽  
Rank Order ◽  
Measurement Data ◽  
Strong Relationship ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Surveillance Systems

The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions from the surveillance images. This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. Overall, both networks attained high accuracy when compared to the measurement data but the DeepLabv3+ network performed better than the SegNet network, achieving over 90% for overall accuracy and IoU metrics, and around 80% for boundary F1 score (BF score), respectively. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background but the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, the DeepLabv3+ network was used for practical application using a set of images captured at five consecutive days in the study area. The segmentation result and water level markers extracted from light detection and ranging (LiDAR) data were overlaid to estimate river water levels and observe the water fluctuation. River water levels were predicted based on the elevation from the predefined markers. The proposed water level framework was evaluated according to Spearman’s rank-order correlation coefficient. The correlation coefficient was 0.91, which indicates a strong relationship between the estimated water level and observed water level. Based on these findings, it can be concluded that the proposed approach has high potential as an alternative monitoring system that offers water region information and water level estimation for flood management and related activities.

Phreatic Water Surface Profiles along Ice Jams – An Experimental Study

1996 ◽  
Vol 27 (3) ◽  
pp. 185-201 ◽  
Author(s):  
Raafat G. Saadé ◽  
Semaan Sarraf
Keyword(s):  
Water Level ◽  
Water Surface ◽  
Surface Profile ◽  
Water Levels ◽  
Ice Jams ◽  
Ice Jam ◽  
Phreatic Water ◽  
Northern Regions ◽  
Water Surface Profile ◽  
Ice Floes

In Northern Regions, the formation of ice jams along many rivers is a common phenomena. These ice jams may occur during the freeze-up and more importantly during the spring break-up period. Ice jams in general have considerable effects on the water levels because they alter the water surface profile for stretches of tens of kilometers along the rivers. As a consequence, water levels increase significantly upstream of the ice jam and result in the flooding of towns situated along the river banks. Knowledge of the water levels within an ice jam can be used to estimate many parameters that are difficult to measure and observe. Examples of such parameters are the local and global ice jam resistance to the flow, and forces acting within an ice jam. While ice jams are notorious causes of serious problems in hydraulic engineering, very little engineering methodology exists to deal with such problems. In this paper, the results of a laboratory study aimed at investigating the development of the water surface profile along an ice jam that is lodged in place, are analyzed and presented. A rectangular flume with a horizontal bed was used for the experiments. Twelve experiments carried out under different geometrical, hydrodynamic and ice conditions, were analysed. A simulated floating ice cover was used to arrest the downstream transport of the ice floes, forming the ice jams. The experiments indicate two types of ice jams, those that are floating and others that are lodged at one or more locations along their length. The phreatic water level along a floating ice jam is up to 0.92 the ice jam thickness. This is not true when an ice jam is lodged in place. Different experiments have shown that the water surface profile along a lodged ice jam follows similar tendencies regardless of the geometry, ice floe size distribution and hydrodynamic conditions. It was found that the phreatic water level varies linearly from the trailing edge of the ice jam up to approximately 90% of its length downstream. Towards the remaining part of the jam's length the water level follows a cubic polynomial line.

scholarly journals AN ORIGINAL PROCESSING METHOD OF SATELLITE ALTIMETRY FOR ESTIMATING WATER LEVELS AND VOLUME FLUCTUATIONS IN A SERIES OF SMALL LAKES OF THE PANTANAL WETLAND COMPLEX IN BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 327-334 ◽  
Author(s):  
Paulo Henrique Costa ◽  
Eric Oliveira Pereira ◽  
Philippe Maillard
Keyword(s):  
Water Level ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Small Lakes ◽  
Water Surface Area ◽  
Volume Fluctuations ◽  
Calm Water ◽  
Gauging Station

Satellite altimetry is becoming a major tool for measuring water levels in rivers and lakes offering accuracies compatible with many hydrological applications, especially in uninhabited regions of difficult access. The Pantanal is considered the largest tropical wetland in the world and the sparsity of <i>in situ</i> gauging station make remote methods of water level measurements an attractive alternative. This article describes how satellites altimetry data from Envisat and Saral was used to determine water level in two small lakes in the Pantanal. By combining the water level with the water surface area extracted from satellite imagery, water volume fluctuations were also estimated for a few periods. The available algorithms (retrackers) that compute a range solution from the raw waveforms do not always produce reliable measurements in small lakes. This is because the return signal gets often “contaminated” by the surrounding land. To try to solve this, we created a “lake” retracker that rejects waveforms that cannot be attributed to “calm water” and convert them to altitude. Elevation data are stored in a database along with the water surface area to compute the volume fluctuations. Satellite water level time series were also produced and compared with the only nearby <i>in situ</i> gauging station. Although the “lake” retracker worked well with calm water, the presence of waves and other factors was such that the standard “ice1” retracker performed better on the overall. We estimate our water level accuracy to be around 75 cm. Although the return time of both satellites is only 35 days, the next few years promise to bring new altimetry satellite missions that will significantly increase this frequency.

scholarly journals Measurement of Wastewater Discharge in Sewer Pipes Using Image Analysis

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1771
Author(s):  
Hyon Ji ◽  
Sung Yoo ◽  
Bong-Jae Lee ◽  
Dan Koo ◽  
Jeong-Hee Kang
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Water Level ◽  
Low Cost ◽  
Water Levels ◽  
Boundary Line ◽  
Sewer Pipe ◽  
Flow Meters ◽  
Submerged Area ◽  
Boundary Lines

Generally, the amount of wastewater in sewerage pipes is measured using sensor-based devices such as submerged area velocity flow meters or non-contact flow meters. However, these flow meters do not provide accurate measurements because of impurities, corrosion, and measurement instability due to high turbidity. However, cameras have advantages such as their low cost, easy service, and convenient operation compared to the sensors. Therefore, in this study, we examined the following three methods for measuring the flow rate by capturing images inside of a sewer pipe using a camera and analyzing the images to calculate the water level: direct visual inspection and recording, image processing, and deep learning. The MATLAB image processing toolbox was used for analysis. The image processing found the boundary line by adjusting the contrast of the image or removing noise; a network to find the boundary line between wastewater and sewer pipe was created after training the image segmentation results and placing them into three categories using deep learning. From the recognized water levels, geometrical features were used to identify the boundary lines, and flow velocities and flow rates were calculated from Manning’s equation. Using direct inspection and image-processing techniques, boundary lines in images were detected at rates of 12% and 53%, respectively. Although the deep-learning model required training, it demonstrated 100% water-level detection, thereby proving to be the most advantageous method. Moreover, there is enough potential to increase the accuracy of deep learning, and it can be a possible replacement for existing flow measurement sensors.

Web-Based Surface Level Measuring System Employing Ultrasonic Sensors and GSM/GPRS-Based Communication

2015 ◽  
Vol 771 ◽  
pp. 92-95 ◽  
Author(s):  
Muhammad Miftahul Munir ◽  
Rahmat Awaludin Salam ◽  
Eko Widiatmoko ◽  
Yundi Supriadani ◽  
Andri Rahmadhani ◽  
...  
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Water Surface ◽  
Warning System ◽  
Water Levels ◽  
Measuring System ◽  
Web Based ◽  
Surface Level

Water surface level should get special attention as water can cause disasters such as flood when its surface exceeds a certain level. A real time early warning system to monitor water surface level is necessary for avoiding severe effects of flood to human life. A web-based water level measuring system using an ultrasonic sensor can be an alternative choice for developing the early warning system. It is known that the system has advantages in the installation and maintenance compared to other systems. This paper discusses the design of a water level measuring system integrated with an internet web server. Ultrasonic sensors are used to measure the water surface level. A GSM / GPRS-based communication system is applied for sending measured water levels to a web server. The results indicate that the measurement data are in accordance with the water levels manually obtained. The results also show that the system works real time.

scholarly journals Wave-attenuation characteristics of combined-vegetation wave break forests for big rivers with large flood water level changes

2021 ◽  
Vol 83 (4) ◽  
pp. 831-840
Author(s):  
Jie Ren ◽  
Zengchuan Dong ◽  
Dawei Jin ◽  
Yue Zhou ◽  
Wei Xu ◽  
...  
Keyword(s):  
Water Level ◽  
Water Depth ◽  
Water Surface ◽  
Wave Attenuation ◽  
Water Levels ◽  
Large Rivers ◽  
Attenuation Effect ◽  
Practical Applications ◽  
Different Types ◽  
Wave Break

Abstract For large rivers with a compound cross section, the downstream channel has a very wide water surface during the flood season. A wide water surface, high water level, and larger wind speed will cause higher waves, increasing the threat of flooding to the dike. The design of a combined-vegetation wave break forest was put forward to achieve better wave attenuation effect. The main idea of this concept is to plant different types of vegetation at different locations in front of the dike. Three single-vegetation and four combined-vegetation forest schemes were tested under seven different water depth conditions. Both physical experiments and wave numerical simulations were carried out for each scheme to study the wave attenuation effect. The results showed that the wave attenuation effect of the single-vegetation wave break forest was significantly different under different water depth conditions, and the overall effect of the combined-vegetation of wave forest was better. Combined-vegetation wave break forests combine the advantages of different types of vegetation in different water levels, which makes it more economical and reasonable to plant by rivers with large water level variation. The proposed design ideas and methods could provide theoretical support for ecological revetment engineering of large rivers and insights for practical applications.

scholarly journals Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 55
Author(s):  
Tsumugu Kusudo ◽  
Atsushi Yamamoto ◽  
Masaomi Kimura ◽  
Yutaka Matsuno
Keyword(s):  
Water Management ◽  
Deep Learning ◽  
Water Level ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Water Levels ◽  
Short Term ◽  
Term Memory ◽  
Deep Learning Algorithm ◽  
Long Short Term Memory

In this study, we aimed to develop and assess a hydrological model using a deep learning algorithm for improved water management. Single-output long short-term memory (LSTM SO) and encoder-decoder long short-term memory (LSTM ED) models were developed, and their performances were compared using different input variables. We used water-level and rainfall data from 2018 to 2020 in the Takayama Reservoir (Nara Prefecture, Japan) to train, test, and assess both models. The root-mean-squared error and Nash–Sutcliffe efficiency were estimated to compare the model performances. The results showed that the LSTM ED model had better accuracy. Analysis of water levels and water-level changes presented better results than the analysis of water levels. However, the accuracy of the model was significantly lower when predicting water levels outside the range of the training datasets. Within this range, the developed model could be used for water management to reduce the risk of downstream flooding, while ensuring sufficient water storage for irrigation, because of its ability to determine an appropriate amount of water for release from the reservoir before rainfall events.

scholarly journals The Effect of Climate Changes on The Fluctuation of The Water Level of Al- Razzaza Lake, Iraq

2021 ◽  
pp. 4464-4474
Author(s):  
Shahad A. Al-Qaraghuli ◽  
Azhar A. Hassan ◽  
Rafa A. Albaldawi ◽  
Omnia K. Abd
Keyword(s):  
Water Level ◽  
Surface Area ◽  
Water Surface ◽  
Climate Changes ◽  
Spectral Band ◽  
Water Levels ◽  
Sharp Change ◽  
Significant Information ◽  
Water Index ◽  
Water Surface Area

      One of the serious environmental challenges that Iraq faces is climate changes and impacts of changing weather patterns and extreme global weather events. This paper addresses changes in the temporal and spatial characteristics of water levels of Razzaza Lake and response to climatic changes using archived series of Multispectral satellite images Landsat. TM, ETM+ and OLI images acquired on 1990, 2000 and of 2016. In order to extract, mapping the water surface area of the Razzaza Lake, Multispectral spectral band rationing the Normalized Difference Water Index (NDWI) technique was adopted, and the climatic elements data for the period (1990-2016) were analyzed which provide significant information of surface water. The results show that Razzaza Lake has a particularly sharp change rate in the water level and there are significant   fluctuations on lake level and water surface area over the time.

scholarly journals AN ORIGINAL PROCESSING METHOD OF SATELLITE ALTIMETRY FOR ESTIMATING WATER LEVELS AND VOLUME FLUCTUATIONS IN A SERIES OF SMALL LAKES OF THE PANTANAL WETLAND COMPLEX IN BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 327-334
Author(s):  
Paulo Henrique Costa ◽  
Eric Oliveira Pereira ◽  
Philippe Maillard
Keyword(s):  
Water Level ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Small Lakes ◽  
Water Surface Area ◽  
Volume Fluctuations ◽  
Calm Water ◽  
Gauging Station

Satellite altimetry is becoming a major tool for measuring water levels in rivers and lakes offering accuracies compatible with many hydrological applications, especially in uninhabited regions of difficult access. The Pantanal is considered the largest tropical wetland in the world and the sparsity of <i>in situ</i> gauging station make remote methods of water level measurements an attractive alternative. This article describes how satellites altimetry data from Envisat and Saral was used to determine water level in two small lakes in the Pantanal. By combining the water level with the water surface area extracted from satellite imagery, water volume fluctuations were also estimated for a few periods. The available algorithms (retrackers) that compute a range solution from the raw waveforms do not always produce reliable measurements in small lakes. This is because the return signal gets often “contaminated” by the surrounding land. To try to solve this, we created a “lake” retracker that rejects waveforms that cannot be attributed to “calm water” and convert them to altitude. Elevation data are stored in a database along with the water surface area to compute the volume fluctuations. Satellite water level time series were also produced and compared with the only nearby <i>in situ</i> gauging station. Although the “lake” retracker worked well with calm water, the presence of waves and other factors was such that the standard “ice1” retracker performed better on the overall. We estimate our water level accuracy to be around 75 cm. Although the return time of both satellites is only 35 days, the next few years promise to bring new altimetry satellite missions that will significantly increase this frequency.

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