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Development and Application of a Storage Model for River Flow Forecasting
Development Application Storage Model River Flow Forecasting
2009/10/28
A lumped sequential river flow forecasting model is outlined. It is shown to be
flexible in both temporal and spatial scales, thereby allowing simulations to be
undertaken for a wide range of practi...
Multivariate Transfer Function-Noise Model of River Flow for Hydropower Operation
Transfer Function-Noise Model River Flow Hydropower Operation
2009/10/27
The formulation of multivariate autoregressive moving average (ARMA) time
series models and their transfer function noise (TFN) form is described. Development
of a multivariate TFN model is difficul...
River Flow with Excessive Suspended Sediment Load
River Flow Excessive Suspended Sediment Load
2009/10/27
River flows with high volume concentrations (20-50 %) of silty sediments generally
imply that the mixture has non-Newtonian properties. In this study, the
rheological behaviour of mixtures with soli...
One of the most important consequences of future climate change may be an
alteration of the surface hydrological balance, including changes in flow regimes,
i.e. seasonal distribution of flow and es...
The Effect of Climate Change on River Flow and Snow Cover in the NOPEX Area Simulated by a Simple Water Balance Model
Climate Change River Flow Snow Cover NOPEX Area
2009/10/23
Within the next few decades, changes in global temperature and precipitation
patterns may appear, especially at high latitudes. A simple monthly water-balance
model of the NOPEX basins was developed...
Dynamics of River Flow Regimes Viewed through Attractors
Dynamics River Flow Regimes Attractors
2009/10/22
The hydrological system is extremely complex. To get insight into its behaviour
and possible future states it is important to assess not only its individual components
but also consider their intera...
Optimisation of LiDAR derived terrain models for river flow modelling
Optimisation LiDAR terrain models river flow modelling
2009/9/11
Airborne LiDAR (Light Detection And Ranging) combines cost efficiency, high degree of automation, high point density of typically 1–10 points per m2 and height accuracy of better than ±15 cm. For all ...
River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin
River flow forecasting artificial neural networks flow length travel time information
2009/9/11
This paper explores the use of flow length and travel time as a pre-processing step for incorporating spatial precipitation information into Artificial Neural Network (ANN) models used for river flow ...
Development of a high resolution grid-based river flow model for use with regional climate model output
regional climate model potential evaporation Probability-Distributed Model
2009/5/6
A grid-based approach to river flow modelling has been developed for regional assessments of the impact of environmental change on hydrologically sensitive systems. The approach also provides a means ...
Comparison of Artificial Intelligence Techniques for river flow forecasting
Artificial Neural Network Adaptive Neuro Fuzzy Inference System Generalized Regression Neural Networks
2009/4/28
The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro ...
A non-linear neural network technique for updating of river flow forecasts
Auto-Regressive Exogenous-input model neural network
2009/3/24
A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural netw...
Comparison of different forms of the Multi-layer Feed-Forward Neural Network method used for river flow forecasting
River flow forecast combination multi-layer feed-forward neural network
2009/3/17
The Multi-Layer Feed-Forward Neural Network (MLFFNN) is applied in the context of river flow forecast combination, where a number of rainfall-runoff models are used simultaneously to produce an overal...
Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments
data fusion fuzzy logic neural network hydrological modelling
2009/3/17
This paper evaluates six published data fusion strategies for hydrological forecasting based on two contrasting catchments: the River Ouse and the Upper River Wye. The input level and discharge estima...
Dependence between sea surge, river flow and precipitation in south and west Britain
Britain dependence
2009/3/6
Estuaries around Great Britain may be at heightened risk of flooding because of the simultaneous occurrence of extreme sea surge and river flow, both of which may be caused by mid-latitude cyclones. A...
Defining environmental river flow requirements – a review
environmental flow instream flow river habitat modelling
2009/3/6
Around the world, there is an increasing desire, supported by national and regional policies and legislation, to conserve or restore the ecological health and functioning of rivers and their associate...