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

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 l...

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Autores principales:	El Chaal Rachid, Aboutafail Mouley Othman
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/3402d404350743ea8812a3f74a304525
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spelling	oai:doaj.org-article:3402d404350743ea8812a3f74a3045252021-11-08T15:19:12ZDevelopment of Stochastic Mathematical Models for the Prediction of Heavy Metal Content in Surface Waters Using Artificial Neural Network and Multiple Linear Regression2267-124210.1051/e3sconf/202131402001https://doaj.org/article/3402d404350743ea8812a3f74a3045252021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/90/e3sconf_wmad2021_02001.pdfhttps://doaj.org/toc/2267-1242The 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.El Chaal RachidAboutafail Mouley OthmanEDP SciencesarticleEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 314, p 02001 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Environmental sciences GE1-350
spellingShingle	Environmental sciences GE1-350 El Chaal Rachid Aboutafail Mouley Othman Development of Stochastic Mathematical Models for the Prediction of Heavy Metal Content in Surface Waters Using Artificial Neural Network and Multiple Linear Regression
description	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.
format	article
author	El Chaal Rachid Aboutafail Mouley Othman
author_facet	El Chaal Rachid Aboutafail Mouley Othman
author_sort	El Chaal Rachid
title	Development of Stochastic Mathematical Models for the Prediction of Heavy Metal Content in Surface Waters Using Artificial Neural Network and Multiple Linear Regression
title_short	Development of Stochastic Mathematical Models for the Prediction of Heavy Metal Content in Surface Waters Using Artificial Neural Network and Multiple Linear Regression
title_full	Development of Stochastic Mathematical Models for the Prediction of Heavy Metal Content in Surface Waters Using Artificial Neural Network and Multiple Linear Regression
title_fullStr	Development of Stochastic Mathematical Models for the Prediction of Heavy Metal Content in Surface Waters Using Artificial Neural Network and Multiple Linear Regression
title_full_unstemmed	Development of Stochastic Mathematical Models for the Prediction of Heavy Metal Content in Surface Waters Using Artificial Neural Network and Multiple Linear Regression
title_sort	development of stochastic mathematical models for the prediction of heavy metal content in surface waters using artificial neural network and multiple linear regression
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/3402d404350743ea8812a3f74a304525
work_keys_str_mv	AT elchaalrachid developmentofstochasticmathematicalmodelsforthepredictionofheavymetalcontentinsurfacewatersusingartificialneuralnetworkandmultiplelinearregression AT aboutafailmouleyothman developmentofstochasticmathematicalmodelsforthepredictionofheavymetalcontentinsurfacewatersusingartificialneuralnetworkandmultiplelinearregression
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Development of Stochastic Mathematical Models for the Prediction of Heavy Metal Content in Surface Waters Using Artificial Neural Network and Multiple Linear Regression

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