Quantification of salinity using artificial neural networks. Case study of the Isser River (Algeria)

K. Houari

Abstract


Abstract: In this study, we examine a very complex phenomenon in the salinity of rivers. The complexity of the mineralization of rivers makes it difficult to quantify by traditional statistical models. Artificial intelligence is an interesting and fully justified alternative for modeling non-linear phenomena. The present study aims to develop a model based on a multilayer perceptron neuron network (MLP), capable of explaining the salt concentration-liquid flow relationship. The method is based on observations recorded at the Oued Isser outlet at the Lakhdaria hydrometric. The obtained results indicate that the artificial neural network model (MLP) has the best performance compared to the empirical model, with an ''R2 '' value for regression analysis of training, testing and validation of 80%, 75% and 78%, respectively. Further, the «Nash» value for training, testing and validation are 79%, 72% and 75%, respectively.

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