USE OF ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL ANALYSIS FOR MODELING THE POLLUTION PRESSURE OF WATER RESOURCES IN THE SEYBOUSE VALLEY (NORTH-EASTERN ALGERIA)

The water supply environment in Seybouse Valley (North East of Algeria) is sensitive and fragile as the aquifer is highly vulnerable to various sources of pollution, must recognize the pollution sources and water quality integration. So, there is a need for a better knowledge and understanding of th...

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Autores principales:	Aissam GHRIEB, Fethi BAALI, Chemceddine FEHDI, Azzedine HANI, Hicham CHAFFAI, Larbi DJABRI
Formato:	article
Lenguaje:	EN
Publicado:	Stefan cel Mare University of Suceava 2021
Materias:	dpsir model ann multivariate techniques seybouse valley Food processing and manufacture TP368-456
Acceso en línea:	https://doaj.org/article/9b3809a9b58f4f15b0f749a19543608b
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spelling	oai:doaj.org-article:9b3809a9b58f4f15b0f749a19543608b2021-12-02T18:18:26ZUSE OF ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL ANALYSIS FOR MODELING THE POLLUTION PRESSURE OF WATER RESOURCES IN THE SEYBOUSE VALLEY (NORTH-EASTERN ALGERIA)2068-66092559-6381https://doaj.org/article/9b3809a9b58f4f15b0f749a19543608b2021-03-01T00:00:00Zhttp://fens.usv.ro/index.php/FENS/article/view/772/688https://doaj.org/toc/2068-6609https://doaj.org/toc/2559-6381The water supply environment in Seybouse Valley (North East of Algeria) is sensitive and fragile as the aquifer is highly vulnerable to various sources of pollution, must recognize the pollution sources and water quality integration. So, there is a need for a better knowledge and understanding of the water pollution determinants to meet the Domestic, agricultural and Industrial uses. The pollution of this ground water was determined by Total Dissolved Solids (TDS). This represents the salinity of freshwater and originate from natural sources, sewage, urban, runoff, industrial wastewater and chemicals. Based on cause-and-effect relationships, the Driver–Pressure–State–Impact–Response (DPSIR) plan was used to establish indicators for an integrated water resource management approach to water quality in the semi-arid Mediterranean region. The aim of this work is to determine the most pressing pollution source of Seybouse Valley. With this intention, the artificial neural network (ANN) models were used to model and predict the relationship between groundwater quality with point and diffuse pollution sources determinants. The selected variables were classified and organized using the multivariate techniques of Hierarchical cluster analysis (HCA), factor analysis (FA), principal components and classification analysis (PCCA). It was concluded that the industrial wastewater that is the most pressing pollution source followed by seawater intrusion.Aissam GHRIEBFethi BAALIChemceddine FEHDIAzzedine HANIHicham CHAFFAILarbi DJABRIStefan cel Mare University of Suceavaarticledpsir modelannmultivariate techniquesseybouse valleyFood processing and manufactureTP368-456ENFood and Environment Safety, Vol 20, Iss 1, Pp 68-80 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dpsir model ann multivariate techniques seybouse valley Food processing and manufacture TP368-456
spellingShingle	dpsir model ann multivariate techniques seybouse valley Food processing and manufacture TP368-456 Aissam GHRIEB Fethi BAALI Chemceddine FEHDI Azzedine HANI Hicham CHAFFAI Larbi DJABRI USE OF ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL ANALYSIS FOR MODELING THE POLLUTION PRESSURE OF WATER RESOURCES IN THE SEYBOUSE VALLEY (NORTH-EASTERN ALGERIA)
description	The water supply environment in Seybouse Valley (North East of Algeria) is sensitive and fragile as the aquifer is highly vulnerable to various sources of pollution, must recognize the pollution sources and water quality integration. So, there is a need for a better knowledge and understanding of the water pollution determinants to meet the Domestic, agricultural and Industrial uses. The pollution of this ground water was determined by Total Dissolved Solids (TDS). This represents the salinity of freshwater and originate from natural sources, sewage, urban, runoff, industrial wastewater and chemicals. Based on cause-and-effect relationships, the Driver–Pressure–State–Impact–Response (DPSIR) plan was used to establish indicators for an integrated water resource management approach to water quality in the semi-arid Mediterranean region. The aim of this work is to determine the most pressing pollution source of Seybouse Valley. With this intention, the artificial neural network (ANN) models were used to model and predict the relationship between groundwater quality with point and diffuse pollution sources determinants. The selected variables were classified and organized using the multivariate techniques of Hierarchical cluster analysis (HCA), factor analysis (FA), principal components and classification analysis (PCCA). It was concluded that the industrial wastewater that is the most pressing pollution source followed by seawater intrusion.
format	article
author	Aissam GHRIEB Fethi BAALI Chemceddine FEHDI Azzedine HANI Hicham CHAFFAI Larbi DJABRI
author_facet	Aissam GHRIEB Fethi BAALI Chemceddine FEHDI Azzedine HANI Hicham CHAFFAI Larbi DJABRI
author_sort	Aissam GHRIEB
title	USE OF ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL ANALYSIS FOR MODELING THE POLLUTION PRESSURE OF WATER RESOURCES IN THE SEYBOUSE VALLEY (NORTH-EASTERN ALGERIA)
title_short	USE OF ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL ANALYSIS FOR MODELING THE POLLUTION PRESSURE OF WATER RESOURCES IN THE SEYBOUSE VALLEY (NORTH-EASTERN ALGERIA)
title_full	USE OF ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL ANALYSIS FOR MODELING THE POLLUTION PRESSURE OF WATER RESOURCES IN THE SEYBOUSE VALLEY (NORTH-EASTERN ALGERIA)
title_fullStr	USE OF ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL ANALYSIS FOR MODELING THE POLLUTION PRESSURE OF WATER RESOURCES IN THE SEYBOUSE VALLEY (NORTH-EASTERN ALGERIA)
title_full_unstemmed	USE OF ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL ANALYSIS FOR MODELING THE POLLUTION PRESSURE OF WATER RESOURCES IN THE SEYBOUSE VALLEY (NORTH-EASTERN ALGERIA)
title_sort	use of artificial neural networks and multivariate statistical analysis for modeling the pollution pressure of water resources in the seybouse valley (north-eastern algeria)
publisher	Stefan cel Mare University of Suceava
publishDate	2021
url	https://doaj.org/article/9b3809a9b58f4f15b0f749a19543608b
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USE OF ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL ANALYSIS FOR MODELING THE POLLUTION PRESSURE OF WATER RESOURCES IN THE SEYBOUSE VALLEY (NORTH-EASTERN ALGERIA)

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