Groundwater quality modeling using neuro-particle swarm optimization and neuro-differential evolution techniques

Recently, the capabilities of artificial neural networks (ANNs) in simulating dynamic systems have been proven. However, the common training algorithms of ANNs (e.g., back-propagation and gradient algorithms) are featured with specific drawbacks in terms of slow convergence and probable entrapment in local minima. Alternatively, novel training techniques, e.g., particle swarm optimization (PSO) and differential evolution (DE) algorithms might be employed for conquering these shortcomings. In this paper, ANN-PSO and ANN-DE models were applied for modeling groundwater qualitative parameters, i.e., SO4 and sodium adsorption ratio (SAR). Three statistical parameters including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) were used for assessing the models' capabilities. The results showed that the ANN-DE presents more accurate results than ANN-PSO in modeling SAR and electrical conductivity (EC).