Development of mathematical models for predicting the iron concentrations of Lake Oubeira waters (ne Algerian)

Abstract

Facing the increase of surface water samples contaminated by ETMs, usually from the geochemical background, the emergence of new human diseases is worrying. To solve this problem, we have developed several models based on different learning algorithms qualified by high performance, using different transfer functions. We have shown that all the Neural Models presented more or less important performance  compared to the one based on multiple linear regressions. The best revealed model ANN in the current work is a MLP type that uses the Levenberg-Marquardt algorithm as a learning algorithm, with Tansig and Purelin as transfer functions, respectively in the hidden layer and the output layer. This successful model can be considered as an important tool of great effectiveness in the context of environmental prediction and especially in anticipation of the iron contents of the Oubeira Lake water.

Keywords: Prediction, heavy metals, Linear multiple regression, artificial neural  networks, Oubéira Lake.