In-situ water level measurement using NIR-imaging video camera

Zhang, Zhen; Zhou, Yang; Liu, Haiyun; Gao, Hongmin

doi:10.1016/j.flowmeasinst.2019.04.004

Cited by 63 publications

(48 citation statements)

References 19 publications

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“…However, the numerical solution as Θ is computationally expensive including assumptions for the hydrology process in flood. There is an enormous demand for parameter tuning of the numerical solution Θ once with high-resolution two-dimensional water level measurements mentioned in [36].…”

Section: Numerical Flood Simulation Model and Landlabmentioning

confidence: 99%

Hydrological Process Surrogate Modelling and Simulation with Neural Networks

Ruixi

Zen

Xing

et al. 2020

Advances in Knowledge Discovery and Data Mining

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Environmental sustainability is a major concern for urban and rural development. Actors and stakeholders need economic, effective and efficient simulations in order to predict and evaluate the impact of development on the environment and the constraints that the environment imposes on development. Numerical simulation models are usually computation expensive and require expert knowledge. We consider the problem of hydrological modelling and simulation. With a training set consisting of pairs of inputs and outputs from an off-the-shelves simulator, We show that a neural network can learn a surrogate model effectively and efficiently and thus can be used as a surrogate simulation model. Moreover, we argue that the neural network model, although trained on some example terrains, is generally capable of simulating terrains of different sizes and spatial characteristics.

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Section: Numerical Flood Simulation Model and Landlabmentioning

confidence: 99%

Hydrological Process Surrogate Modelling and Simulation with Neural Networks

Ruixi

Zen

Xing

et al. 2020

Advances in Knowledge Discovery and Data Mining

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“…A set of wind-solar hybrid power supply is adopted to keep the measurement system continuously running for at least 7 days. See Reference [36] for details.…”

Section: Measurement Site and Systemmentioning

confidence: 99%

“…Next the orthophoto correction image is binarized, and the gray value of each row of pixels is accumulated to obtain the horizontal projection curve. By searching the abrupt change value in the horizontal projection curve, the water line between water gauge and water surface is determined, and then the water level is obtained [36]. This idea of water-level measurement is a two-step process: one detecting the water level on a pixel space, and two translate these in real world coordinates.…”

Section: Water-level Measurementmentioning

confidence: 99%

“…When the gray distribution of the water gauge or water surface is uneven, the global threshold obtained by the Otsu method is not the local optimal segmentation threshold in the water line region, which results in large errors of water line segmentation. The order-statistic filtering (OSF) method [36] sets several sampling points in the water gauge and water-surface image, ascends through sorting with a bubble sort algorithm, then calculates the adaptive segmentation threshold. However, these methods based on single threshold may cause the gray value of a water gauge to be lower than the water surface under complex illumination conditions, which causes the measurement to be sensitive to the threshold.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The latter often involves the manual thresholding of several initial images, and the same approach can then be applied automatically to subsequent images. Optical noise can also be reduced using advanced mathematical algorithms (e.g., [71,72]). However, for NAPL-related investigations, this may cause information loss, especially for image areas with residual NAPL volumes (e.g., ganglia or blobs).…”

Section: Conceptualization Of a Basic Ipa Framework For Dnapl Releasementioning

confidence: 99%

Quantification of Uncertainties from Image Processing and Analysis in Laboratory-Scale DNAPL Release Studies Evaluated by Reflective Optical Imaging

Engelmann

Schmidt

Werth

et al. 2019

Water

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Subsurface DNAPL (dense non-aqueous phase liquid) contamination from (un-) intentional spilling typically leads to severe environmental hazards. A large number of studies have demonstrated the relevance of DNAPL source zone geometry for the determination of contaminant plume propagation in groundwater. Optical imaging represents a promising non-invasive method for identifying DNAPL saturation without disturbing multiphase flow dynamics. However, workflow and image analysis methodologies have not been sufficiently developed or described for general application to related experimental efforts. For example, the choice of dye(s) used for phase colorization affects image processing and can bias final estimations of DNAPL saturations. In this study, we perform a series of DNAPL migration and entrapment studies in transparent tanks that are filled with three different types of porous media. Different dyes are used and raw images are acquired. Subsequently, these are used to evaluate a suite of image processing and analysis approaches, which are organized into a workflow. Our approach allows for us to identify key image processing and analysis steps that introduce the most error. Applicable dye configurations led to uncertainties of up to 41% depending on the selection of processing steps. Based on these findings, it was possible to delineate a flexible framework for image processing and analysis that has the potential for transfer and application in other tank experiment setups.

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In-situ water level measurement using NIR-imaging video camera

Cited by 63 publications

References 19 publications

Hydrological Process Surrogate Modelling and Simulation with Neural Networks

Hydrological Process Surrogate Modelling and Simulation with Neural Networks

Visual Measurement of Water Level under Complex Illumination Conditions

Quantification of Uncertainties from Image Processing and Analysis in Laboratory-Scale DNAPL Release Studies Evaluated by Reflective Optical Imaging

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