Assessment of sodium adsorption ratio (SAR) in groundwater: Integrating experimental data with cutting-edge swarm intelligence approaches

In developing countries, evaluating irrigation water quality using conventional methods can be costly and time-consuming. To overcome these challenges, this study explores the potential of utilizing physical parameters and artificial intelligence (AI) models for predicting and evaluating the quality indicators of irrigation water in aquifer systems. To achieve this goal, novel hybrid methods, namely the Whale Optimization Algorithm (WOA) and Wind-Driven Optimization (WDO), are employed in conjunction with Artificial Neural Network (ANN) models. The specific objective of this study is to forecast the Sodium Adsorption Ratio (SAR) by considering independent variables such as Na+, Mg2+, Ca2+, Na percent, K+, SO42−, Cl−, pH, and HCO3−. A dataset of 540 samples from the Shiraz plain, collected over a statistical period of 16 years (2002–2018), is used to estimate the groundwater quality variables. A pre-processing technique is applied in the AI approach to enhance the model's efficiency. The results indicate that the WDO-ANN model exhibits higher accuracy (R2 = 0.9983 and RMSE = 0.10618) than the WOA-ANN model (R2 = 0.9957 and RMSE = 0.16957). The optimization of computational parameters and comparison of AI model structures demonstrate that the WDO-ANN model outperforms the WOA-ANN model in predictive ability. In general, using AI models as a tool for low-cost and timely prediction of underground water quality using physical parameters as input variables has a high potential.