Evaluation of geostatistical techniques and their hybrid in modelling of groundwater quality index in the Marand Plain in Iran

In many parts of the world, groundwater is considered as one of the main sources of urban and rural drinking water. Over the past three decades, the qualitative and quantitative characteristics of aquifers have been negatively affected by different factors such as excessive use of chemical fertilizers in agriculture, indiscreet, and over-exploitation use of groundwater. Therefore, finding the effective method for mapping the water quality index (WQI) is important for locating suitable and non-suitable areas for urban and rural drinking waters. In the present paper, the best method to estimate the spatial distribution of WQI was assessed using the inverse distance weighted, kriging, cokriging, geographically weighted regression (GWR), and hybrid models. Creating hybrid models can increase modeling capabilities. Hybrid methods make use of a combination of estimated model capabilities. In addition, to improve the results of cokriging, GWR, and hybrid methods, the auxiliary parameters of land slope, groundwater table, and groundwater transmissibility were used. In order to assess the proposed methodology, 11 qualitative parameters obtained from 63 observation wells in Marand Plain (Iran) were utilized. Four statistical measures, namely the root mean square error (RMSE), the mean absolute error (MAE), the Akaike coefficient (AIC), and the correlation coefficient (R²) along with the Taylor diagram, have been done. Classification of the WQI index showed that the quality of a number of 1, 27, 18, and 17 wells was, respectively, in excellent, good, moderate, and poor grades. The results of modeling the WQI index based on IDW, kriging, cokriging, GWR, and hybrid methods showed that the best estimate of WQI was obtained by using hybrid GWR-kriging method with three input parameters of land slope, groundwater table, and groundwater transmissibility. Therefore, hybrid kriging and GWR methods have been fairly well able to simulate the WQI index.