Water Level Prediction of Community Secondary Water Supply Tank Based on Deep Learning

2019 ◽  
Author(s):  
Han Wu ◽  
Zhenwu Lei ◽  
Yuntao Shi ◽  
Xiaogen Li
Keyword(s):  
Deep Learning ◽  
Water Supply ◽  
Water Level ◽  
Supply Tank ◽  
Water Level Prediction

Urban Water Flow and Water Level Prediction Based on Deep Learning

2017 ◽  
pp. 317-329 ◽  
Author(s):  
Haytham Assem ◽  
Salem Ghariba ◽  
Gabor Makrai ◽  
Paul Johnston ◽  
Laurence Gill ◽  
...  
Keyword(s):  
Deep Learning ◽  
Water Level ◽  
Water Flow ◽  
Urban Water ◽  
Water Level Prediction

scholarly journals Physical Modelling of Flow and Head along with Dead-end and Looped Manifolds

2021 ◽  
Vol 29 (4) ◽  
Author(s):  
Abdullah Amer ◽  
Thamer Ahmad Mohammad ◽  
Wissam Hameed Alawee ◽  
Nadhir Al-Ansari
Keyword(s):  
Experimental Data ◽  
Water Supply ◽  
Water Level ◽  
Pressure Sensors ◽  
Physical Modelling ◽  
Physical Models ◽  
Supply Tank ◽  
Hydraulic Behaviour ◽  
Dead End ◽  
Pump Pressure

In this study, physical models were designed and fabricated to investigate the hydraulic behaviour of dead-end and looped PVC manifolds. The physical models consisted of a water supply tank with overflow, PVC manifolds, steel supports, collection tank, pump, pressure sensors and valves to allow flow control. Throughout the study, the water level in the supply tank was kept constant. The hydraulic behaviour of dead-end manifolds was investigated using different spacing, S between outlets (S= 3m, S=2.5m, S=2m, S=1.5m, and S=0.75m). The hydraulic behaviour of looped manifolds was investigated using a single outlet spacing of 1.5m. The comparison between the hydraulic behaviour of looped and dead-end manifolds was carried out using the data of the 1.5m outlet spacing. The value of uniformity, U for dead-end and looped manifolds was 82% and 92%, respectively. The value of friction ratio, fn/f1, was found to be 33 and 0.18 for dead-end and looped manifolds, respectively. The experimental data of this study were used to validate selected formulae for estimation of the friction correction factor (G Factor). The results showed that the equation proposed by Alazba et al. (2012) yielded the most satisfactory estimation. The performance of the selected formulae was tested using two statistical indices.

scholarly journals Optimal Location of Energy Dissipation Box in Long Distance and High Drop Gravitational Water Supply System

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 461
Author(s):  
Weixiang Ni ◽  
Jian Zhang ◽  
Sheng Chen
Keyword(s):  
Energy Dissipation ◽  
Water Supply ◽  
Water Level ◽  
Pressure Head ◽  
Optimal Location ◽  
Water Supply Systems ◽  
Protective Effects ◽  
Long Distance ◽  
Optimal Position ◽  
Gravitational Flow

In the long-distance and high-drop gravitational water supply systems, the water level difference between the upstream and downstream is large. Thus, it is necessary to ensure energy dissipation and pressure head reduction to reduce the pipeline pressure head. The energy dissipation box is a new type of energy dissipation and pressure head reduction device, which is widely used in the gravitational flow transition systems. At present, there is still a dearth of systematic knowledge about the performance of energy dissipation boxes. In this paper, a relationship between the location of the energy dissipation box and the pressure head amplitude is established, a theoretical optimal location equation of the energy dissipation box is derived, and numerical simulations using an engineering example are carried out for verification. The protective effects of an energy dissipation box placed at the theoretical optimal location and an upstream location are compared. The results indicate that for the same valve action time, the optimal position allows effectively reducing the total volume of energy dissipation box. The oscillation amplitudes of the water level in the box and the pressure head behind the box are markedly reduced. Under the condition that the water level oscillation of the energy dissipation box is almost the same, the optimal location offers better pressure head reduction protection performance than the upstream location.

EXAMINATION OF SHORT-TERM WATER LEVEL PREDICTION MODEL IN LOW-LYING LAKES USING MACHINE LEARNING

2020 ◽  
Vol 76 (2) ◽  
pp. I_439-I_444
Author(s):  
Masaomi KIMURA ◽  
Takahiro ISHIKAWA ◽  
Naoto OKUMURA ◽  
Issaku AZECHI ◽  
Toshiaki IIDA
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Water Level ◽  
Short Term ◽  
Water Level Prediction

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 Inter-annual change of the drought limit water level of a reservoir based on system dynamics

Water Policy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 91-107 ◽  
Author(s):  
Fawen Li ◽  
Dong Yu ◽  
Yong Zhao ◽  
Runxiang Cao
Keyword(s):  
Water Supply ◽  
System Dynamics ◽  
Water Level ◽  
Water Levels ◽  
Disaster Mitigation ◽  
Structural Diagram ◽  
Yuqiao Reservoir ◽  
Engineering Measure ◽  
Drought Early Warning ◽  
Limit Water Level

Abstract Drought is one of the major natural disasters affecting the development of economies and society. Drought early warning is the primary step and most important non-engineering measure for drought relief. This paper took Yuqiao Reservoir in Tianjin as a case study and analysed inter-annual changes of the drought limit water level. First, the causality between variables in the water supply–demand system was analysed, and a structural diagram of water sources allocation was drawn. Coupled with the parameters and a structural diagram, a system dynamics (SD) model of the water supply volume was established. Secondly, simulation results were tested to ensure that the model was valid. The water supply volume from 2003 to 2020 was simulated by using the model. Finally, based on the inflow process and the water supply volume, the drought limit water level was calculated. The results showed the water supply volume of Yuqiao Reservoir has changed remarkably. The drought limit water levels in 2003–2012 and in 2016–2020 were 16.70 m and 16.30 m, respectively: a difference of 0.40 m. The regulation curve of guarantee for water supply during 2016–2020 is significantly lower than that of 2003–2012. This research is of great significance for drought resistance, disaster mitigation and reservoir management.

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