Mapping of groundwater salinization and modelling using meta-heuristic algorithms for the coastal aquifer of eastern Saudi Arabia

The growing increase in groundwater (GW) salinization in the coastal aquifers has reached an alarming socio-economic menace in Saudi Arabia and various places globally due to several natural and anthropogenic activities. Hence, evaluating the GW salinization is paramount to safeguarding the water resources planning and management. This study presents three different scenarios viz.: real field investigation, experimental laboratory analysis (using ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS), etc.), and artificial intelligence (AI) based metaheuristic optimization (MO) algorithms in Saudi Arabia. The main purpose of this study is to validate the obtained experimental-based analysis using hybrid MO techniques comprising of adaptive neuro-fuzzy inference system (ANFIS) hybridized with genetic algorithm (GA), particle swarm optimization (PSO), and biogeography -based optimization (BBO) for identification of GW salinization in the coastal region of eastern Saudi Arabia. Addition-ally, ArcGIS 10.3 software generates the prediction map based on ANFIS-GA, ANFIS-PSO, and ANFIS-BBO. Feature selection was assessed using the PSO algorithm, and four indices evaluated the estimated models, namely, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard devi-ation (SD). The simulated results are based on three variable input combinations, which showed that the ANFIS-PSO (MAE = 0.00439) algorithm had the highest accuracy (99 %), followed by the ANFIS-GA (MAE = 0.00767) and ANFIS-BBO (MAE = 0.0132) algorithms. Besides, Ca2+, Na+, Mg2+, and Cl- were the most influential parameters.