Identifying most influencing input parameters for predicting chloride concentration in groundwater using an ANN approach

Assessment of groundwater quality at a specific location is an important step to provide adequate information about water management and sustainable development. Several variables affect groundwater salinity, expressed by chloride concentration, prediction; therefore, identification of the most significant parameters for accurate prediction is an important research area. In the present study, artificial neural network (ANN) models with various combinations of input parameters were developed to determine the most significant parameters that influence chloride concentration prediction. To achieve this, the variables affecting chloride concentration (recharge rate (RR), abstraction (A), abstraction average rate (AVR), lifetime (LT), groundwater level (GWL), aquifer thickness (AT), depth from the surface to well screen (DSWS), distance from sea shoreline (DSSL)) and climate parameters (total rainfall (R), relative humidity (RH), minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tavg), average wind speed (W), minimum wind speed (Wmin), and maximum wind speed (Wmax)), in addition to initial chloride concentration (ICC), were considered as input variables. The output variable was the final chloride concentration (FCC). 17 ANN models were developed by varying the identified input parameters. Additionally, the coefficient of determination (R-2) and root mean squared error (RMSE) were used to select the best predictive model. The results demonstrate that the ANN 5 model with the combinations of [ICC, RR, A, AVR, LT, GWL, DSWS, AT, DSSL, W] produced excellent estimation in predicting the value of final chloride concentration with reported values of 0.977 and 0.022 for R-2 and RMSE respectively. The proposed approach illustrates how the ANN modeling technique can be used to identify the key variables required for the most significant parameters affecting chloride concentration.