Artificial neural networks for defining the water quality determinants of groundwater abstraction in coastal aquifer

• Main Authors: Lallahem, S., Hani, A.
• Format: Conference Proceeding
• Language: English
• Subjects: Agricultural management; Aquifers; Artificial neural networks; Back propagation; Coastal waters; Correlation coefficients; Determinants; Groundwater; Groundwater quality; Mathematical models; Multilayer perceptrons; Neural networks; Parameters; Quality assessment; River basins; Water quality; Water resources management
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/1.4976232

Water sustainability in the lower Seybouse River basin, eastern Algeria, must take into account the importance of water quantity and quality integration. So, there is a need for a better knowledge and understanding of the water quality determinants of groundwater abstraction to meet the municipal and agricultural uses. In this paper, the artificial neural network (ANN) models were used to model and predict the relationship between groundwater abstraction and water quality determinants in the lower Seybouse River basin. The study area chosen is the lower Seybouse River basin and real data were collected from forty five wells for reference year 2006. Results indicate that the feed-forward multilayer perceptron models with back-propagation are useful tools to define and prioritize the important water quality parameters of groundwater abstraction and use. The model evaluation shows that the correlation coefficients are more than 95% for training, verification and testing data. The model aims to link the water quantity and quality with the objective to strengthen the Integrated Water Resources Management approach. It assists water planners and managers to better assess the water quality parameters and progress towards the provision of appropriate quantities of water of suitable quality.