A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer

Abstract

Groundwater salinity in an aquifer system is typically measured through field studies (e.g., groundwater sampling, and direct current resistivity method). The field-based measurements are costly and time-consuming when they are applied over a large domain. In this study, a methodology was developed and evaluated based on available hydrogeology and hydrometeorology data and statistical and machine learning techniques to map the groundwater salinity in the southern coastal aquifer of the Caspian Sea. First, variables affecting groundwater salinity (aquifer transmissivity, distance from the sea, the mean annual precipitation, the mean annual evaporation, elevation, and the depth to the water table) were determined, and the dataset was randomly divided into three subsets of training, testing, and verification. Next, the relationship between groundwater salinity and its controlling factors was established using three methods namely extreme gradient boosting (EGB), deep neural network (DNN), and multiple linear regression (MLR), and the models were evaluated by comparing the measured values and the predicted values using the statistical criteria (R-squared, Nash–Sutcliffe efficiency (NSE), and normalized root-mean-square deviation (NRMSD)). Finally, the optimum model was applied to the set of known input variables to map the spatial variation of the groundwater salinity across the entire southern coastal plain of the Caspian Sea, and the final map was verified using the verification subset. Results showed that consideration should be given to the EGB method, considering its higher performance on the testing subset (R-squared 0.89, NSE 0.87, NRMSD 0.45). In-depth analysis of the variables showed that the aquifer transmissivity is the most crucial parameter affecting groundwater salinity in the region. The adopted approach could potentially be used for groundwater management purposes in the study area and similar settings elsewhere.