Development of Stochastic Mathematical Models for the Prediction of Heavy Metal Content in Surface Waters Using Artificial Neural Network and Multiple Linear Regression

Data Analysis, Mathematical Modeling, and Optimization Team, Department of Computer Science, Logistics, and Mathematics, Ibn Tofail University, National School of Applied Sciences ENSA, Kenitra 14 000, Morocco

^* Corresponding author: rachid.elchaal@uit.ac.ma

Abstract

The principal purpose of this study is to build stochastic neuronal models, for the prediction of heavy metal, contents in the surface waters of the Oued Inaouen catchment area of the TAZA region, according to their Physico-chemical parameters; we have carried out a comparative study: the multiple linear regression (MLR) method and the artificial neural network (ANN) approach. The following statistical indicators were used to evaluate the performance of the stochastic models developed by neural network and MLR: The sum of the quadratic errors (SSE) and the determination coefficient (R²), also through the study of fit graphs. The results show that the predictive modelling using artificial neural networks is very effective. This performance shows a non-linear relation between the studied Physico-chemical characteristics and the heavy metal contents in the surface waters of the Oued Inaouen catchment area.