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River flow: New machine learning methods could improve environmental predictions
river flow improve environmental predictions algorithm environmental prediction
2021/8/5
Machine learning algorithms do a lot for us every day -- send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to watc...
INFLUENCE OF RIVER BED ELEVATION SURVEY CONFIGURATIONS AND INTERPOLATION METHODS ON THE ACCURACY OF LIDAR DTM-BASED RIVER FLOW SIMULATIONS
LiDAR Digital Terrain Model River Bed Survey Configuration Interpolation Integration Flow Simulation
2016/10/14
In this paper, we investigated how survey configuration and the type of interpolation method can affect the accuracy of river flow simulations that utilize LIDAR DTM integrated with interpolated river...
Adequate knowledge of the nature of river flow process is crucial for proper planning and management of our water resources and environment. This study attempts to detect the salient characteristics o...
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...
Daily River Flow Forecasting Using Artificial Neural Networks and Auto-Regressive Models
Streamflow forecasting Neural networks Auto-regressive models
2009/10/10
Estimating the flows of rivers can have a signi cant economic impact, as this can help in agricultural water management and in providing protection from water shortages and possible flood damage. This...
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 ...
An Experiment with Escherichia Coli T Bacteriophage as Tracer in River Flow Studies
Escherichia Coli T Bacteriophage River Flow Studies
2009/6/18
An Experiment with Escherichia Coli T Bacteriophage as Tracer in River Flow Studies.
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 ...