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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...
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...
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...
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 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...
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...
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...
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 ...
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.
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 ...
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 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...

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