Evolution of a hybrid approach for groundwater vulnerability assessment using hierarchical fuzzy-DRASTIC models in the Cuddalore Region, India

Uncertainty in the supply and demand and a lack of available freshwater resources require a better management plan for sustainable development in agriculturally dependent communities. Therefore, scarce freshwater resources are protected and monitored to prevent contamination. Groundwater is the largest freshwater reservoir, and groundwater zones prone to contamination need to be identified. A precise model that enables the simplification and validation of the assessment process was developed by applying a fuzzy logic technique. A hierarchical fuzzy inference model (HFIM) was developed to better handle the input. The application of the developed model was then compared with the conventional index-based DRASTIC model in the Cuddalore District. The parameters that were found to influence the degree of vulnerability, including the depth of the water table (D), net recharge to the aquifer (R), aquifer media (A), soil properties (S), topography of the area (T), impact of the vadose zone (I), and hydraulic conductivity of the aquifer (C), were considered in the model development. A geographical information system (GIS) framework was utilized to synthesize the DRASTIC model and MATLAB was employed to develop the hierarchical fuzzy inference model. The results obtained from the GIS-DRASTIC model and HFIM were classified into five and seven categories based on their index values, respectively. The models were validated using nitrate concentration (mg/l) data obtained from 40 sampling points in and around the study area. A sensitivity analysis was performed on the models by varying the input from their minimum to maximum values for a selected hydrogeological setting. The results revealed that the HFIM was better at determining groundwater vulnerability levels in the Cuddalore District. It could cope with the uncertainty and nonlinearity of the datasets; the output showed a continuous response to modifications of the input data, which contrasts the DRASTIC model.