A comparison of different rule-based statistical models for modeling geogenic groundwater contamination

There is an increasing interest in modeling groundwater contamination, particularly geogenic contaminant, on a large scale both from the researcher’s as well as policy maker’s point of view. However, modeling large scale groundwater contamination is very challenging due to the incomplete understanding of geochemical and hydrological processes in the aquifer. Despite the incomplete understanding, existing knowledge provides sufficient hints to develop predictive models of geogenic contamination. In this study we used a global database of fluoride measurements (>60,000 entities), as well as global-scale information relevant to soil, geology, elevation, climate, and hydrology to evaluate several hybrid methods. The hybrid methods were developed by combining two classification techniques including classification and regression tree (CART) and “knowledge based clustering” (KBC) and three predictive techniques including multiple linear regression (MLR), adoptive neuro-fuzzy inference system (ANFIS) and logistic regression (LR). The results indicated that combination of classification techniques and nonlinear predictive method (ANFIS and LR) were more reliable than others and provided a better prediction capability. Among the different hybrid procedures, combination of KBC-ANFIS and also CART-ANFIS resulted in larger true positive rates and smaller false negative rates for both training and test data sets. However, as the CART classifier is very unstable and very sensitive to resampling, the combination of KBC and ANFIS is preferred as it not only is more robust but also is flexible enough to account for geohydrological conditions.