A fuzzy logic based dynamic wave model inversion algorithm for canal regulation

2009 ◽  
Vol 23 (12) ◽  
pp. 1739-1752 ◽  
Author(s):  
R. Gopakumar ◽  
P. P. Mujumdar
Keyword(s):  
Fuzzy Logic ◽  
Wave Model ◽  
Inversion Algorithm ◽  
Model Inversion ◽  
Dynamic Wave

scholarly journals FRF WAVE TEST BED AND BATHYMETRY INVERSION

2017 ◽  
pp. 22 ◽  
Author(s):  
Jane McKee Smith ◽  
Spicer Bak ◽  
Tyler Hesser ◽  
Mary A. Bryant ◽  
Chris Massey
Keyword(s):  
Numerical Models ◽  
Field Research ◽  
Wave Model ◽  
Army Corps ◽  
Army Corps Of Engineers ◽  
Test Bed ◽  
Inversion Algorithm ◽  
Us Army ◽  
Spectral Wave Model ◽  
Limited Application

An automated Coastal Model Test Bed has been built for the US Army Corps of Engineers Field Research Facility to evaluate coastal numerical models. In October of 2015, the test bed was expanded during a multi-investigator experiment, called BathyDuck, to evaluate two bathymetry sources: traditional survey data and bathymetry generated through the cBathy inversion algorithm using Argus video measurements. Comparisons were made between simulations using the spectral wave model STWAVE with half-hourly cBathy bathymetry and the more temporally sparse surveyed bathymetry. The simulation results using cBathy bathymetry were relatively close to those using the surveyed bathymetry. The largest differences were at the shallowest gauges within 250 m of the coast, where wave model normalized root-mean-square was approximately twice are large using the cBathy bathymetry. The nearshore errors using the cBathy input were greatest during events with wave height greater than 2 m. For this limited application, the Argus cBathy algorithm proved to be a suitable bathymetry input for nearshore wave modeling. cBathy bathymetry was easily incorporated into the modeling test bed and had the advantage of being updated on approximately the same temporal scale as the other model input conditions. cBathy has great potential for modeling applications where traditional surveys are sparse (seasonal or yearly).

Modèle de prévision pour la gestion en temps réel : application aux réseaux d'assainissement

2000 ◽  
Vol 27 (2) ◽  
pp. 327-337
Author(s):  
Abderrahman Assabbane ◽  
Saad Bennis
Keyword(s):  
Kalman Filter ◽  
Transfer Function ◽  
Real Time ◽  
Time Management ◽  
Wave Model ◽  
Transfer Function Model ◽  
Function Model ◽  
Proposed Model ◽  
Kalman Filter Algorithm ◽  
Dynamic Wave

The work presented here aims at developing a flow forecast model dedicated to real-time management. The proposed model is based on the notion of a transfer function for a linear system identified through the Kalman filter algorithm. In a first step, the transfer function model is linked to the Muskingum semi-empirical model; then it is modified to eliminate the autoregressive component. The Kalman filter algorithm allows the parameters of the proposed model to be updated upon the reception of each new measure with respect to the forecast errors observed in real time. To analyze the performance of the proposed model, its results are compared with those obtained using the dynamic wave model and the simplified kinematic wave model. Because of the absence of measured downstream flow values corresponding to the input hydrograph, the results from the dynamic wave model are used as reference values to evaluate the performance of the other models. These results are also used with the addition of noises to simulate measured values and feed, in "real-time," the identification algorithm of the transfer function in order to adjust, a posteriori, its parameters according to its differences in the flow prediction. The results obtained by the transfer function model agree with those obtained by the dynamic model following the three performance criteria employed. The Nash coefficient and the ratio between the peak flows are close to unity in all of the cases. Also, the lag between the peak flows estimated by the two models is negligible.Key words: waste water networks, real-time management, flow propagation models, forecast, transfer function, Kalman filter.[Journal Translation]

scholarly journals ESTIMATING THE 2011 LARGEST FLOOD DISCHARGE AT THE KUMANO RIVER USING THE 2D DYNAMIC WAVE MODEL AND PARTICLE FILTERS

2013 ◽  
Vol 69 (4) ◽  
pp. I_163-I_168 ◽  
Author(s):  
Yeonsu KIM ◽  
Yasuto TACHIKAWA ◽  
Sunmin KIM ◽  
Michiharu SHIIBA ◽  
Seong Jin NOH ◽  
...  
Keyword(s):  
Particle Filters ◽  
Wave Model ◽  
Flood Discharge ◽  
Dynamic Wave

scholarly journals Application of newtonian nudging data assimilation method in hydrodynamic model of flood flow in the lower Biebrza basin

2012 ◽  
Vol 34 (2) ◽  
pp. 91-105 ◽  
Author(s):  
Dorota Mirosław-Świątek
Keyword(s):  
Boundary Condition ◽  
Data Assimilation ◽  
Water Table ◽  
Flow Model ◽  
Wave Model ◽  
Spring Flood ◽  
One Dimensional ◽  
Using Data ◽  
Flood Flow ◽  
Dynamic Wave

Abstract The paper presents a 1D hydrodynamic flood flow model employing a data assimilation procedure based on Newtonian nudging. Data assimilation was used to determine correctly the upstream boundary condition defined as a discharge hydrograph. In the model developed, “nudging to individual observations” method was used. The data chosen for assimilation were water table levels recorded by a D-Diver automatic sensor installed in the river channel c. 1.5 km below a computational cross-section opening the analysed stretch of the river and the adjacent valley. This hydrological model of flood flow containing the data assimilation procedure is based on a one-dimensional Saint-Venant system of equations (dynamic wave model). The calculations were performed for the 2010 spring flood event at a 20-km stretch of the river and the floodplain in the upper part of the Lower Biebrza Basin. Modifying the boundary condition by using data assimilation has dramatically improved the accuracy of water table predictions during floods in the area of the Lower Biebrza Basin.

Rainfall Runoff Hydrograph Prediction Using Dynamic Wave Based Instantaneous Unit Hydrograph

2020 ◽  
Author(s):  
Minyeob Jeong ◽  
Jongho Kim ◽  
Dae-Hong Kim
Keyword(s):  
Rainfall Intensity ◽  
Wave Model ◽  
Test Results ◽  
Rainfall Runoff ◽  
Unit Hydrograph ◽  
Time To Peak ◽  
Instantaneous Unit Hydrograph ◽  
Runoff Hydrographs ◽  
Dynamic Wave ◽  
Peak Value

<p>A method to predict runoff based on the instantaneous unit hydrograph and dynamic wave approximation is proposed. The method is capable of generating IUH of a watershed without the need of observed rainfall and runoff data, and only topography and surface roughness of a watershed are needed. IUHs were generated using a dynamic wave model and S-hydrograph method, and IUH generated was a function of both watershed and rainfall properties. The ordinate of IUH depends on the rainfall intensities, and the peak value of IUH was proportional to the rainfall intensity while the time to peak of the IUH was inversely proportional to the rainfall intensity.  Corresponding IUHs for different rainfall intensities were used to generate runoff hydrographs. Since the IUH is generated using a dynamic wave model, it can be a tool to physically simulate the rainfall-runoff processes. Also, nonlinear rainfall-runoff relationship can be taken into account by expressing IUH as a function of rainfall excess intensity. Several test results in ideal basins and in a real watershed show that the proposed method has a good capability in predicting runoff, while several limitations remain.</p><p>Keywords: rainfall-runoff, instantaneous unit hydrograph, dynamic wave model</p>

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