The Experts below are selected from a list of 84156 Experts worldwide ranked by ideXlab platform
Nasreen Islam Khan - One of the best experts on this subject based on the ideXlab platform.
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simulating and predicting River Discharge time series using a wavelet neural network hybrid modelling approach
Hydrological Processes, 2012Co-Authors: Shouke Wei, Jinxi Song, Nasreen Islam KhanAbstract:Accurate simulation and prediction of the dynamic behaviour of a River Discharge over any time interval is essential for good watershed management. It is difficult to capture the high-frequency characteristics of a River Discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet-neural network (WNN) hybrid modelling approach for the predication of River Discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of River Discharge into sub-series with low (approximation) and high (details) frequency, and these sub-series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict River Discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction. Copyright © 2011 John Wiley & Sons, Ltd.
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Simulating and predicting River Discharge time series using a wavelet‐neural network hybrid modelling approach
Hydrological Processes, 2011Co-Authors: Shouke Wei, Jinxi Song, Nasreen Islam KhanAbstract:Accurate simulation and prediction of the dynamic behaviour of a River Discharge over any time interval is essential for good watershed management. It is difficult to capture the high-frequency characteristics of a River Discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet-neural network (WNN) hybrid modelling approach for the predication of River Discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of River Discharge into sub-series with low (approximation) and high (details) frequency, and these sub-series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict River Discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction. Copyright © 2011 John Wiley & Sons, Ltd.
N. Bercher - One of the best experts on this subject based on the ideXlab platform.
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Estimating River Discharge from earth observation measurements of River surface hydraulic variables
Hydrology and Earth System Sciences, 2011Co-Authors: Jonathan Negrel, Pascal Kosuth, N. BercherAbstract:River Discharge is a key variable for quantifying the water cycle, its fluxes and stocks at different scales. These scales range from a local scale for the efficient management of water resources to a global scale for the monitoring of climate change. Therefore, developing Earth observation (EO) techniques for the measurement or estimation of River Discharge poses a major challenge. A key question deals with the possibility of deriving River Discharge values from EO surface variables (width, level, slope, and velocity are the only such variables accessible through EO) without any in situ measurement. Based on a literature study and original investigations, this study explores the possibilities of estimating River Discharge from water surface variables.\nThe proposed method relies on limiting assumptions to simplify River flow equations to obtain the values of the hydraulic parameters at a given River station without using ground measurements. Once the hydraulic parameters are identified, the method allows the estimation of the River Discharge corresponding to a set of surface measurements of hydraulic variables.
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Estimating River Discharge from earth observation measurement of River surface hydraulic variables
Hydrology and Earth System Sciences Discussions, 2010Co-Authors: Jonathan Negrel, Pascal Kosuth, N. BercherAbstract:Abstract. River Discharge is a key variable to quantify the water cycle, its fluxes and stocks at different scales, from local scale for the efficient management of water resource to global scale for the monitoring of climate change. Therefore, developing Earth observation (EO) techniques for the measurement or estimation of River Discharge is a major challenge. A key question deals with the possibility of deriving River Discharge values from EO surface variables (width, level, slope, velocity the only one accessible through EO) without any in situ measurement. Based on a literature study and original developments, the possibilities of estimating water surface variables using remote-sensing techniques have been explored, mainly RADAR altimetry as well as across-track and along-track interferometry.
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Estimating River Discharge from earth observation measurement of River surface hydraulic variables
2010Co-Authors: Jonathan Negrel, Pascal Kosuth, N. BercherAbstract:River Discharge is a key variable to quantify the water cycle, its fluxes and stocks at different scales, from local scale for the efficient management of water resources to global scale for the monitoring of climate change. However, gathering reliable, long term and consistent information on River Discharges worldwide or on large transboundary River basins is an extremely complex task, if ever achievable, as Hydrologic Services in different countries have heterogeneous acquisition strategies and data policies. Therefore, developing Earth Observation (EO) techniques for the measurement or estimation of River Discharge is a major challenge. A key question deals with the possibility of deriving River Discharge values from EO surface variables (width, level, slope, velocity, the only one accessible through EO) without any in situ measurement. Based on a literature study and original developments, the possibilities of estimating water surface variables using remote-sensing techniques have been explored, mainly radar altimetry as well as across-track and along-track interferometry. Then a method has been developed, based in a first phase on the equations of the uniform regime, in order to estimate River Discharge from these surface variables only. The River section is simplified assuming to have a rectangular cross-section represented by its mean bottom level and width. Another hypothesis is made on a constant coefficient linking water surface velocity to the River section mean velocity. Based on a set of surface variables measurement at different dates and hydrological regimes, the methods estimates the values of the mean bottom level and mean Manning coefficient. Therefore, to be applied, the method requires a reasonable number of measurements along the complete hydrological cycle. The method has been developed and tested on a dataset of measurements realized on several stations on the Amazon basin (HyBAM ANA-IRD Project). Surface velocities and surface width are provided through ADCP measurements while water level and longitudinal River slopes are provided by in situ monitoring of levelled gauging stations and relevant technique to derive the longitudinal profile and slope. This method has been tested on different stations of the Amazon basin and gives satisfactory results on some of them but discrepancies on others. It appears that time varying surface slope on Amazon stations is in contradiction with the uniform hypothesis; therefore the method has been adapted to a non uniform flow configuration. The new method give relevant results on simulated data and further development are on-going to increase robustness of this method to noisy data.
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Estimating River Discharge from Earth Observation measurement of River surface hydraulic variables
2009Co-Authors: Jonathan Negrel, Pascal Kosuth, N. BercherAbstract:River Discharge is a key variable to quantify the water cycle, its fluxes and stocks at different scales, from local for the efficient management of water resource to global for the monitoring of climate change. Therefore, developing EO techniques for the measurement or estimation of River Discharge is a major challenge. A key question deals with the possibility of deriving River Discharge values from EO surface variables (width, level, slope, velocity.: the only one accessible through EO) without any in situ measurement. Based on a literature study and original developments, the possibilities of estimating water surface variables using remote-sensing techniques have been explored, mainly radar altimetry as well as across-track and along-track interferometry. Then a method has been developed, based in a first phase on the equations of the uniform regime, in order to estimate River Discharge from these surface variables only. The River section is simplified assuming to have a rectangular cross-section represented by its mean bottom level and width. Based on a set of surface variables measurements at different dates and hydrological regimes, the method estimates the values of the mean bottom level and mean Manning coefficient. Therefore, to be applied, the method requires a reasonable number of measurements along the complete hydrological cycle. The method has been developed and tested on a dataset of measurements realized on several stations on the Amazon basin (HyBAM ANA-IRD Project). Surface velocities and surface width are provided through ADCP measurements while water level and longitudinal River slopes are provided by in situ monitoring of levelled gauging stations and relevant technique to derive the longitudinal profile and slope. This method has been tested on different stations of the Amazon basin and gives satisfactory results on some of them but discrepancies on others. At this stage it appears to give more robust results than the Bjerklie equations [2003-2005]. Further developments are on-going to adapt he method to a non uniform flow configuration.
Shouke Wei - One of the best experts on this subject based on the ideXlab platform.
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simulating and predicting River Discharge time series using a wavelet neural network hybrid modelling approach
Hydrological Processes, 2012Co-Authors: Shouke Wei, Jinxi Song, Nasreen Islam KhanAbstract:Accurate simulation and prediction of the dynamic behaviour of a River Discharge over any time interval is essential for good watershed management. It is difficult to capture the high-frequency characteristics of a River Discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet-neural network (WNN) hybrid modelling approach for the predication of River Discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of River Discharge into sub-series with low (approximation) and high (details) frequency, and these sub-series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict River Discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction. Copyright © 2011 John Wiley & Sons, Ltd.
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Simulating and predicting River Discharge time series using a wavelet‐neural network hybrid modelling approach
Hydrological Processes, 2011Co-Authors: Shouke Wei, Jinxi Song, Nasreen Islam KhanAbstract:Accurate simulation and prediction of the dynamic behaviour of a River Discharge over any time interval is essential for good watershed management. It is difficult to capture the high-frequency characteristics of a River Discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet-neural network (WNN) hybrid modelling approach for the predication of River Discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of River Discharge into sub-series with low (approximation) and high (details) frequency, and these sub-series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict River Discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction. Copyright © 2011 John Wiley & Sons, Ltd.
Jonathan Negrel - One of the best experts on this subject based on the ideXlab platform.
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Estimating River Discharge from earth observation measurements of River surface hydraulic variables
Hydrology and Earth System Sciences, 2011Co-Authors: Jonathan Negrel, Pascal Kosuth, N. BercherAbstract:River Discharge is a key variable for quantifying the water cycle, its fluxes and stocks at different scales. These scales range from a local scale for the efficient management of water resources to a global scale for the monitoring of climate change. Therefore, developing Earth observation (EO) techniques for the measurement or estimation of River Discharge poses a major challenge. A key question deals with the possibility of deriving River Discharge values from EO surface variables (width, level, slope, and velocity are the only such variables accessible through EO) without any in situ measurement. Based on a literature study and original investigations, this study explores the possibilities of estimating River Discharge from water surface variables.\nThe proposed method relies on limiting assumptions to simplify River flow equations to obtain the values of the hydraulic parameters at a given River station without using ground measurements. Once the hydraulic parameters are identified, the method allows the estimation of the River Discharge corresponding to a set of surface measurements of hydraulic variables.
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Estimating River Discharge from earth observation measurement of River surface hydraulic variables
Hydrology and Earth System Sciences Discussions, 2010Co-Authors: Jonathan Negrel, Pascal Kosuth, N. BercherAbstract:Abstract. River Discharge is a key variable to quantify the water cycle, its fluxes and stocks at different scales, from local scale for the efficient management of water resource to global scale for the monitoring of climate change. Therefore, developing Earth observation (EO) techniques for the measurement or estimation of River Discharge is a major challenge. A key question deals with the possibility of deriving River Discharge values from EO surface variables (width, level, slope, velocity the only one accessible through EO) without any in situ measurement. Based on a literature study and original developments, the possibilities of estimating water surface variables using remote-sensing techniques have been explored, mainly RADAR altimetry as well as across-track and along-track interferometry.
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Estimating River Discharge from earth observation measurement of River surface hydraulic variables
2010Co-Authors: Jonathan Negrel, Pascal Kosuth, N. BercherAbstract:River Discharge is a key variable to quantify the water cycle, its fluxes and stocks at different scales, from local scale for the efficient management of water resources to global scale for the monitoring of climate change. However, gathering reliable, long term and consistent information on River Discharges worldwide or on large transboundary River basins is an extremely complex task, if ever achievable, as Hydrologic Services in different countries have heterogeneous acquisition strategies and data policies. Therefore, developing Earth Observation (EO) techniques for the measurement or estimation of River Discharge is a major challenge. A key question deals with the possibility of deriving River Discharge values from EO surface variables (width, level, slope, velocity, the only one accessible through EO) without any in situ measurement. Based on a literature study and original developments, the possibilities of estimating water surface variables using remote-sensing techniques have been explored, mainly radar altimetry as well as across-track and along-track interferometry. Then a method has been developed, based in a first phase on the equations of the uniform regime, in order to estimate River Discharge from these surface variables only. The River section is simplified assuming to have a rectangular cross-section represented by its mean bottom level and width. Another hypothesis is made on a constant coefficient linking water surface velocity to the River section mean velocity. Based on a set of surface variables measurement at different dates and hydrological regimes, the methods estimates the values of the mean bottom level and mean Manning coefficient. Therefore, to be applied, the method requires a reasonable number of measurements along the complete hydrological cycle. The method has been developed and tested on a dataset of measurements realized on several stations on the Amazon basin (HyBAM ANA-IRD Project). Surface velocities and surface width are provided through ADCP measurements while water level and longitudinal River slopes are provided by in situ monitoring of levelled gauging stations and relevant technique to derive the longitudinal profile and slope. This method has been tested on different stations of the Amazon basin and gives satisfactory results on some of them but discrepancies on others. It appears that time varying surface slope on Amazon stations is in contradiction with the uniform hypothesis; therefore the method has been adapted to a non uniform flow configuration. The new method give relevant results on simulated data and further development are on-going to increase robustness of this method to noisy data.
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Estimating River Discharge using AlongTrack Interferometry techniques
2009Co-Authors: Jonathan Negrel, Pascal Kosuth, J.f. Nouvel, P. Dubois Fernandez, Y. LasneAbstract:River Discharge monitoring using Earth Observation techniques is a major challenge for hydrologists for the water resources management as well as the surveillance of climate changes. In northern regions, for instance, Rivers are heavily impacted by climate changes. The melting of permafrost raises the income of water in River and consequently flooding and land erosion caused by the increase of River Discharge. Water current measurement is a main variable for the calculation of River Discharge. These researches aim at estimating this water surface current using radar Along Track Interferometry (ATI) techniques. The Along Track Interferometry technique is based on the comparison of the phase of two SAR images acquired with two antennas separated with a short distance in the along-track direction. The two images are acquired with the same geometrical configuration separated by a short time lag. The phase difference between the two images comes from the short time lag between the two acquisitions and the movement of the target (water particles) during this time lag. Therefore, the target radial velocity, toward or away from the radar, is estimated from this phase difference. Images have been acquired on the Rhone River (France) with the RAMSES SAR sensor (ONERA) operating at X band. The ATI techniques applied to the images acquired during the campaign allowed the extraction of a surface velocity map. The analysis of this map shows that the ATI techniques are very promising and are consistent with ground measurements acquired simultaneously (using ADCP) to the radar acquisition. Surface current profile, across the River section, extracted from the map matches the current profile extracted from ADCP measurements. A radar backscattering model using a specific water surface roughness model is under investigation. The roughness model is based on the study of the movements of water particles in wind induced short waves and the velocity of these waves on water surface under different wind stress conditions. The comparison between simulated data using this radar backscattering model and real data will allow the development of a reverse model of the radar signal to estimate water surface current and thereby the accuracy of water current measurement using EO techniques.
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Estimating River Discharge from Earth Observation measurement of River surface hydraulic variables
2009Co-Authors: Jonathan Negrel, Pascal Kosuth, N. BercherAbstract:River Discharge is a key variable to quantify the water cycle, its fluxes and stocks at different scales, from local for the efficient management of water resource to global for the monitoring of climate change. Therefore, developing EO techniques for the measurement or estimation of River Discharge is a major challenge. A key question deals with the possibility of deriving River Discharge values from EO surface variables (width, level, slope, velocity.: the only one accessible through EO) without any in situ measurement. Based on a literature study and original developments, the possibilities of estimating water surface variables using remote-sensing techniques have been explored, mainly radar altimetry as well as across-track and along-track interferometry. Then a method has been developed, based in a first phase on the equations of the uniform regime, in order to estimate River Discharge from these surface variables only. The River section is simplified assuming to have a rectangular cross-section represented by its mean bottom level and width. Based on a set of surface variables measurements at different dates and hydrological regimes, the method estimates the values of the mean bottom level and mean Manning coefficient. Therefore, to be applied, the method requires a reasonable number of measurements along the complete hydrological cycle. The method has been developed and tested on a dataset of measurements realized on several stations on the Amazon basin (HyBAM ANA-IRD Project). Surface velocities and surface width are provided through ADCP measurements while water level and longitudinal River slopes are provided by in situ monitoring of levelled gauging stations and relevant technique to derive the longitudinal profile and slope. This method has been tested on different stations of the Amazon basin and gives satisfactory results on some of them but discrepancies on others. At this stage it appears to give more robust results than the Bjerklie equations [2003-2005]. Further developments are on-going to adapt he method to a non uniform flow configuration.
Jinxi Song - One of the best experts on this subject based on the ideXlab platform.
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simulating and predicting River Discharge time series using a wavelet neural network hybrid modelling approach
Hydrological Processes, 2012Co-Authors: Shouke Wei, Jinxi Song, Nasreen Islam KhanAbstract:Accurate simulation and prediction of the dynamic behaviour of a River Discharge over any time interval is essential for good watershed management. It is difficult to capture the high-frequency characteristics of a River Discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet-neural network (WNN) hybrid modelling approach for the predication of River Discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of River Discharge into sub-series with low (approximation) and high (details) frequency, and these sub-series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict River Discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction. Copyright © 2011 John Wiley & Sons, Ltd.
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Simulating and predicting River Discharge time series using a wavelet‐neural network hybrid modelling approach
Hydrological Processes, 2011Co-Authors: Shouke Wei, Jinxi Song, Nasreen Islam KhanAbstract:Accurate simulation and prediction of the dynamic behaviour of a River Discharge over any time interval is essential for good watershed management. It is difficult to capture the high-frequency characteristics of a River Discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet-neural network (WNN) hybrid modelling approach for the predication of River Discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of River Discharge into sub-series with low (approximation) and high (details) frequency, and these sub-series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict River Discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction. Copyright © 2011 John Wiley & Sons, Ltd.
