SCHÜTZ, F. C. A.; http://lattes.cnpq.br/3997654537105134; SCHÜTZ, Fabiana Costa de Araujo.
Resumo:
The present study was to develop two models in Artificial
Neural Networks (ANN) in order to estimate the dissolved oxygen and model
the depuration of the Alegria River , located in the municipality of Medianeira in
the State of Paraná. Both models were developed based on data of water
quality of the river and model responsible for the prediction of self-purification of
the river also used data from the effluent that is built into the river over the
range studied With 22 samples in 6 seasons: the training and validation of
models 132 sets of data were generated . To simulate the concentration of
dissolved oxygen in the river water five different artificial neural networks have
been developed. The input variables in these networks were the water quality
parameters except the Dissolved Oxygen (DO), who set up in all the networks
as output. To predict the self-purification of the river was a model developed in
artificial neural networks based on water quality data at a point upstream of the
release of effluent , this effluent and data , from these data, the model provides
values of dissolved oxygen (DO ) and biochemical oxygen demand ( BOD ) at
points downstream , ie, predicts the potential self-purification river . Among the
models developed to simulate the dissolved oxygen concentration was the best
result achieved an average error between the OD values estimated by the
artificial neural network 11.4%. In the model tested to predict the selfpurification
of the river, several architectures were tested and showed the best
result was setup with a hidden layer of 14 neurons 14:14:1 and 2000 seasons.
The best result of average error between the known values of BOD and the
adjusted valueswas 2.17 % and the BOD generated an average error between
the known values and the adjusted values of 11.26%.