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

Abstract

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.