Water Level Measurement Method Based on Temporal Variation of Water Surface Pixel Arrangement in Successive Images

Coastal hazards, such as a tsunamis and storm surges, are a critical threat to coastal communities that lead to significant loss of lives and properties. To mitigate their impact, event-driven water level changes should be properly monitored. A tide gauge is one of the conventional water level measurement sensors. Still, alternative measurement systems can be needed to compensate for the role of tide gauge for contingency (e.g., broken and absence, etc.). Global Navigation Satellite System (GNSS) is an emerging water level measurement sensor that processes multipath signals reflected by the water surface that is referred to as GNSS-Reflectometry (GNSS-R). In this study, we adopted the GNSS-R technique to monitor tsunamis and storm surges by analyzing event-driven water level changes. To detect the extreme change of water level, enhanced GNSS-R data processing methods were applied which included the utilization of multi-band GNSS signals, determination of optimal processing window, and Kalman filtering for height rate determination. The impact of coastal hazards on water level retrievals was assessed by computing the confidence level of retrieval (CLR) that was computed based on probability of dominant peak representing the roughness of the water surface. The proposed approach was validated by two tsunami events, induced by 2012 Haida Gwaii earthquake and 2015 Chile earthquake, and two storm surge events, induced by 2017 Hurricane Harvey and occurred in Alaska in 2019. The proposed method successfully retrieved the water levels during the storm surge in both cases with the high correlation coefficients with the nearby tide gauge, 0.944, 0.933, 0.987, and 0.957, respectively. In addition, CLRs of four events are distinctive to the type of coastal events. It is confirmed that the tsunami causes the CLR deduction, while for the storm surges, GNSS-R keep high CLR during the event. These results are possibly used as an indicator of each event in terms of storm surge level and tsunami arrival time. This study shows that the proposed approach of GNSS-R based water level retrieval is feasible to monitor coastal hazards that are tsunamis and storm surges, and it can be a promising tool for investigating the coastal hazards to mitigate their impact and for a better decision making.

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Comparative study of PID controlled modes on automatic water level measurement system

2011 IEEE 7th International Colloquium on Signal Processing and its Applications ◽

10.1109/cspa.2011.5759879 ◽

2011 ◽

Cited By ~ 9

Author(s):

I.S Isa ◽

B.C.C. Meng ◽

Z. Saad ◽

N.A. Fauzi

Keyword(s):

Comparative Study ◽

Water Level ◽

Measurement System ◽

Level Measurement

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Denoising of river surface photogrammetric DEMs using deep learning

10.5194/egusphere-egu21-10266 ◽

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 &#8220;Initiative for Excellence &#8211; Research University&#8221; and Ministry of Science and Higher Education subsidy, project no. 16.16.220.842-B02 / 16.16.150.545.</p>

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Replacement of NOAA's National Tide and Water Level Measurement System

10.1109/oceans.1981.1151566 ◽

1981 ◽

Cited By ~ 1

Author(s):

T. Bartholemew ◽

D. Spillman

Keyword(s):

Water Level ◽

Measurement System ◽

Level Measurement

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Prototipe Perangkat untuk Pemantauan dan Pengendalian Berbasis Web Diiintegrasikan ke Smarthome System

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.58316 ◽

2020 ◽

Vol 10 (2) ◽

pp. 167

Author(s):

Achmad Faris Nasyarudin ◽

Ritzkal Ritzkal ◽

Arief Goeritno

Keyword(s):

Performance Measurement ◽

Water Level ◽

Measurement System ◽

Electronic Devices ◽

Water Tank ◽

Monitoring And Control ◽

Validation Test ◽

Level Measurement ◽

And Performance ◽

And Control

The design and construction of a device prototype for a water level measurement system in a tank and controlling a number of garden light analogies has been carried-out and the prototype can be integrated into smarthome system. Three topics are discussed in this paper, including the manufacture, programming, and performance measurement of device prototypes. The formation of prototype of the device is done through wiring integration between electronic devices, in order to obtain the hardware handshacking. Programming the prototype of device is done through the creation of algorithms and preparation of syntax, in order to obtain the software handshacking. The performance of the prototype of device is measured when integrated into the Smarthome system, in order to obtain the hardware and software handshacking. The performance of prototype of the device when monitoring in the form of information about the water level in the water tank with 3 (three) conditions, namely the criteria of "empty", "medium", and "full", while the control in the form of information about the operation of ON/OFF of the LED as an analogy to the lamp garden are done for 3 (three) positions, namely position #1, #2, and #3. The manufactured subsystem prototype can be integrated into the smarthome system when a validation test is performed. Prototype of the device for monitoring and control based-on web that can be integrated into the smarthome system.

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