On improving data fitting procedure in reservoir operation using artificial neural networks

It is an attempt to overcome the problem of not knowing at what least count to reduce the size of the steps taken in weight space and by how much in artificial neural network approach. The parameter estimation phase in conventional statistical models is equivalent to the process of optimizing the connection weights, which is known as 'learning'. Consequently the theory of nonlinear optimization is applicable to the training of feed forward networks. Multilayer Feed forward (BPM & BPLM) and Recurrent Neural network (RNN) models as intra and intra neuronal architectures are formed. The aim is to find a near global solution to what is typically a highly non-linear optimization problem like reservoir operation. The reservoir operation policy derivation has been developed as a case study on application of neural networks. A better management of its allocation and management of water among the users and resources of the system is very much needed. The training and testing sets in the ANN model consisted of data from water year 1969-1994. The water year 1994-1997 data were used in validation of the model performance as learning progressed. Results obtained by BPLM are more satisfactory as compared to BPM. In addition the performance by RNN models when applied to the problem of reservoir operation have proved to be the fastest method in speed and produced satisfactory results among all artificial neural network models.