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The present study focuses on the unexplored area of application of artificial neural network in groundwater hydrology. Three models, each based on artificial neural networks, are applied for predictio...
This paper examines and compares the capability of an artificial neural network (ANN) with five different backpropagation (BP) algorithms, namely Gradient descent with momentum (GDM), Gradient descent...
In interpolation of groundwater properties such as transmissivity, due to the unknown distributed values of the variables and heterogenity, the best and the unbiased aspects are frequently difficult t...
The present study focuses on the unexplored area of application of artificial neural network in groundwater hydrology. Three models, each based on artificial neural networks, are applied for predictio...
This paper examines and compares the capability of an artificial neural network (ANN) with five different backpropagation (BP) algorithms, namely Gradient descent with momentum (GDM), Gradient descent...
In interpolation of groundwater properties such as transmissivity, due to the unknown distributed values of the variables and heterogenity, the best and the unbiased aspects are frequently difficult t...
This paper proposes a change detection analysis method based on multitemporal LANDSAT satellite data, presenting a study performed on the Lama San Giorgio (Bari, Italy) river basin area. Based o...
Artificial neural networks (ANN) have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models....
Data-based methods of flow forecasting are becoming increasingly popular due to their rapid development times, minimum information requirements, and ease of real-time implementation, with transfer fun...
Most neural network hydrological modelling has used split-sample validation to ensure good out-of-sample generalisation and thus safeguard each potential solution against the danger of overfitting. Ho...
A step that should be considered when developing artificial neural network (ANN) models for water resources applications is the selection of an appropriate transformation of the data. In general, the ...
The inversion of snow water equivalent, SWE, from passive microwave remote sensing measurements may be accomplished by using a neural network trained with a dense media multiple scattering model. Br...
Flood forecasting is always a challenge in Taiwan, which has a subtropical climate and high mountains. This paper develops a fuzzy clustering neural network (FCNN), and implements this novel structu...
Spatial Analysis Neural Network (SANN) is a specified neural network for conducting the spatial analysis of any type of variable. It provides a nonparametric mean estimator and also estimators of high...
Various studies have been carried out for the evaluation of non-point source pollution using physically-based distributed hydrological and water quality models. A number of modeling interactions have ...

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