Development of a Neural Network approach for Predicting nitrate and sulfate concentration in three lakes: Ifrah, Iffer and Afourgagh, Middle Atlas Morocco

H. Ousmana, A. El Hmaidi, M. Berrada, B. Damnati, I. Etabaai, A. Essahlaoui


Neural networks are mathematical and computer models to power nonlinear data that play a very important role in various scientific fields. They are specially used for automatic resolution of environmental problems.

This study focuses on the prediction of nitrate (NO3-) and sulfate (SO42-) of lake water in the Moroccan Middle Atlas. Ifrah, Iffer and Afourgagh are taken as case studies by using a number of parameters physic-chemical of water. Two methods were used: Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) Multilayer Perceptron Model (MLP).

In order to choose the best neural network architecture, several statistical tests were used in conjunction with some robustness tests: Mean Square Error (MSE), mean absolute error (MAE) and correlation coefficient (R).

The results showed that the models established by artificial neural network Multilayer Perceptron type (ANN-MLP) of configuration [17-8-2] are more efficient compared to those determined by the conventional method based on multiple linear regression.

This performance demonstrates the existence of a nonlinear relationship between the physic-chemical characteristics of both nitrates and sulfates in the lakes waters studied that are under investigation.


Neural Networks, Multiple linear regression, prediction, Middle Atlas, Physic-Chemical, lake waters

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