Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models

Abstract

Scarcity of water resources is becoming a threatening issue in arid regions like Gulf. Accurate prediction of quantities and quality of groundwater is the first step towards better management of water resources where groundwater is the major source of water supply. Groundwater modelling with respect to its quantity and quality has been performed in this paper using Artificial Neural Networks (ANNs), Adaptive Neurofuzzy Inference System (ANFIS), and hydraulic model MODFLOW. Five types of ANN models with various training functions have been investigated to find the most efficient training function for the prediction of quantity and quality of groundwater, which is an original contribution useful for engineering sector. The results of the hydraulic model, ANFIS, and ANN have been compared. Nash-Sutcliffe Model Efficiency and Mean Square Error have been used for assessing the performance of models. Taylor's Diagram has also been used to compare various models. The part of Saq Aquifer lying in the Qassim Region has been investigated as the study area. Modern tools, including Geographical Information System (GIS) and Digital Elevation Model (DEM) are applied to process the required data for modelling. Climatic, geographical, and quality of groundwater (contaminants) data are obtained from the Ministry of Environment, Water, and Agriculture, Jeddah/Riyadh. ANFIS model is found to be the most efficient for modelling both the quality and quantity of the aquifer. Sensitivity analysis was performed, and then various future scenarios were investigated for sustainable groundwater pumping. The results of the research will be useful for the community and experts working in the field of water resources engineering, planning, and management in arid regions.