Supervised committee fuzzy logic model to assess groundwater intrinsic vulnerability in multiple aquifer systems

Groundwater vulnerability modeling is an alternative approach to evaluate groundwater contamination especially in areas affected by intensive anthropogenic activities. However, the DRASTIC model as a well-known method to assess groundwater vulnerability suffers from the inherent uncertainty associated with its seven essential parameters. In this study, three different fuzzy logic (FL) models (Sugeno fuzzy logic, Mamdani fuzzy logic, and Larsen fuzzy logic) are adopted to improve the DRASTIC system to be more realistic. The vulnerability map of groundwater from multiple aquifer systems (i.e., karstic, alluvium, and complex) in Basara basin, Iraq, was created using the FL models. Validation of the FL models results using NO3-N concentration obtained from wells and springs of the study area indicating that all of the three FL models are applicable for improving the DRASTIC model. However, each of the FL models has its own advantages for groundwater vulnerability estimation in different types of aquifer systems in the Basara basin. Therefore, this study proposes the supervised committee fuzzy logic (SCFL) as amultimodelmethod to combine the advantages of individual FL models. The SCFL method confirms that no water well with high NO3-N levels would be classified as low risk and vice versa. The study suggests that this approach has provided a convenient estimation of pollution risk in the study area and therefore, a more accurate prediction of the intrinsic vulnerability to pollution in the multiple aquifer system can be achieved through SCFL method.