Deriving groundwater major ions from electrical conductivity using artificial neural networks supported by analytical hydrochemical solutions

Abstract

Tracing groundwater chemistry requires broad and continuous sampling campaigns to keep track of groundwater deterioration. The need is pressing in coastal aquifers due to high water demand to meet rapid population growth. This paper uses artificial intelligence (AI) to develop an algorithm capable of predicting seven major chemical ions in water (Ca2+, Mg2+, Na+, K+, HCO3−, Cl− and SO42−) from electrical conductivity (EC), a single input that can be easily acquired manually or via automated monitoring programs. The algorithm mainly relied on artificial neural network (ANN) to tackle complex and nonlinear relationships. It included a set of procedures among others, Levenberg-Marquardt back propagation multilayer perceptron (MLP), conjugate gradient back propagation with Fletcher-Reeves updates MLP, and support vector regression. The low accuracy of ANN models in predicting Ca2+ and HCO3− concentrations justified their calculation via advanced hydrochemical analytical solutions that were embedded in one AI scheme. The full scheme was trained and tested over an EC20 range of 400–3000 μS/cm, and then validated for new measurements elsewhere. It showed <15% discrepancy for the four ions that constitute the majority of groundwater chemical composition (Cl−, Na+, HCO3− and Ca2+). Higher uncertainties were associated with concentrations derived for EC values outside the training limits. The developed algorithm can save time and money in return of labor and laboratory work. It is an open source code publicly available for further improvements by training it on additional data covering a wider EC range.