Hydraulic head and groundwater 111Cd content interpolations using empirical Bayesian kriging (EBK) and geo-adaptive neuro-fuzzy inference system (geo-ANFIS)

Abstract

In this study, hydraulic head and ¹¹¹Cd interpolations based on the geo-adaptive neuro-fuzzy inference system (Geo-ANFIS) and empirical Bayesian kriging (EBK) were performed for the alluvium unit of Karabağlar Polje in Muğla, Turkey. Hydraulic head measurements and ¹¹¹Cd analyses were done for 42 water wells during a snapshot campaign in April 2013. The main objective of this study was to compare Geo-ANFIS and EBK to interpolate hydraulic head and ¹¹¹Cd content of groundwater. Both models were applied on the same case study: alluvium of Karabağlar Polje, which covers an area of 25 km² in Muğla basin, in the southwest of Turkey. The ANFIS method (called ANFIS_XY) uses two reduced centred pre-processed inputs, which are cartesian coordinates (XY). Geo-ANFIS is tested on a 100-random-data subset of 8 data among 42, with the remaining data used to train and validate the models. ANFISXY and EBK were then used to interpolate hydraulic head and heavy metal distribution, on a 50 m² grid covering the study area for ANFIS_XY, while a 100 m² grid was used for EBK. Both EBK- and ANFIS_XY-simulated hydraulic head and ¹¹¹Cd distributions exhibit realistic patterns, with RMSE < 9 m and RMSE < 8 μg/L, respectively. In conclusion, EBK can be considered as a better interpolation method than ANFIS_XY for both parameters.

Keywords: ANFIS, EBK, interpolation, hydraulic head, metal, ¹¹¹Cd, alluvium, Muğla