Principles of artificial neural networks
Principles of artificial neural networks
Essence of Neural Networks
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Environmental Modelling & Software
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems
Mathematics and Computers in Simulation
Predictive modeling for wastewater applications: Linear and nonlinear approaches
Environmental Modelling & Software
Environmental Modelling & Software
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Support vector machines-kernel algorithms for the estimation of the water supply in Cyprus
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network
Environmental Modelling & Software
Reliable probabilistic classification with neural networks
Neurocomputing
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This is a preliminary attempt towards a wider use of Artificial Neural Networks in the management of mountainous water supplies. It proposes a model to be used effectively in the estimation of the average annual water supply, in each mountainous watershed of Cyprus. This is really a crucial task, especially during the long dry summer months of the island. On the other hand the evaluation of the potential torrential risk due to high volume of water flow in the winter season is also very important. Data (from 1965-1993) from 78 measuring stations located in the 70 distinct watersheds of Cyprus were used. This data volume was divided in the training subset comprising of 60 cases and in the testing subset containing 18 cases. The input parameters are the area of the watershed, the average annual and the average monthly rain-height, the altitude and the slope in the location of the measuring station. Consequently three structural and two dynamic factors are considered. After several and extended training-testing efforts a Modular Artificial Neural Network was determined to be the optimal one.