Practical Application Of Neural Networks To
Predict DO Concentration
Author(s)
J.A. Garcia, V. Arroyo, L. Sanchez & J.A. Pino
Abstract
Practical application of neural networks to
predict DO concentration
J.A. Garcia, V. Arroyo, L. Sanchez, J.A. Pino
Information Technologies Group (GTI)
Departament of Applied Mathematics and Computational Sciences
University ofCantabria
Email:jgarcia @ mace, unican.es
Abstract
This paper presents a different alternative based on neural networks to
predict dissolved oxygen concentration (DO) in water masses. This method allows
the solution to mathematical models to be obtained more quickly, thus avoiding
excessive computing times. With neural networks, non-linear systems can be
modelled quite effectively, and time series prediction tasks can be carried out
without the need for any excessively complicated calculations. In this paper, the first
step has been to look for a suitable neural network model, the solution being found
in feedforward backpropagation multilayer perceptron networks. Subsequently, the
NevProp network simulator has been used to train and valida
Keywords
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