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We present an experiment on fifty multilayer perceptrons trained for streamflow forecasting on three watersheds using bootstrapped input series. This type of neural network is common in hydrology and ...
Time series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute...
In this paper neural networks have been studied as a tool to realise a single-input single-output nonlinear dynamic system simulating rainfall-runoff transformation in a urban hydrological basin. The ...
Scour at culvert outlets is a phenomenon encountered world-wide. Research into the problem has mainly been of an experimental nature, with equations being derived for particular circumstances. These t...
The paper compares potentials and achievements of artificial neural networks and genetic algorithms in terms of forecasting and understanding of algal blooms in Lake Kasumigaura (Japan). Despite the c...
Flow over a side weir is one of the more complex flows to simulate in one-dimensional unsteady flow analysis. Various experiments have been applied, but no agreement is apparent in the literature abou...
The last decade has seen increasing interest in the application of Artificial Neural Networks (ANNs) for the modelling of the relationship between rainfall and streamflow. Since multi-layer, feed-forw...
In this paper, Multi-Layer Perceptron and Radial-Basis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountai...
We have applied three models, a neural network, a conceptual model and a combination of these two, a hybrid model, to model thc backwatcr effect of ice in a river. The neural network is a black-box ...
Radiation is a variable that governs many hydrological and phenological processes, but its measurements are not made routinely. To overcome this problem, continuous hydrological models that include ...
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 ...
This paper presents the application of a modular approach for real-time streamflow forecasting that uses different system-theoretic rainfall-runoff models according to the situation characterising the...
In this study, a methodology has been developed to emulate a time consuming Monte Carlo (MC) simulation by using an Artificial Neural Network (ANN) for the assessment of model parametric uncertainty. ...
The increasing incidence of groundwater pollution has led to recognition of a need to develop objective techniques for designing reniediation schemes. This paper outlines one such possibility for dete...
This paper compares the performance of two artificial neural network (ANN) models the multi layer perceptron (MLP) and the radial basis function network (RBF) – with a stepwise multiple linear regre...

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