Publication Date



GIS, Spatial Modeling, Remote Sensing, Fuzzy Logic, Neural Networks


There is a need to develop new modeling techniques that assess ground water vulnerability with less expensive data and which are robust when data are uncertain and incomplete. Incorporation of Geographic Information Systems (GIS) with a modeling approach that is robust has the potential for creating a successful modeling tool. The specific objective of this study was to develop a model using Neuro-fuzzy techniques in a GIS to predict ground water vulnerability. The Neuro-fuzzy model was developed in JAVA using four plausible parameters deemed critical in transporting contaminants in and through the soil profile. These parameters include soil hydrologic group, depth of the soil profile, soil structure (pedality points) of the soil A horizon and landuse. The model was validated using nitrate-N concentration data. The majority of the highly vulnerable areas predicted by the model coincided with agricultural landuse, moderately deep to deep soils, soil hydrologic group C (moderately low Ksat) and high pedality points (high water transmitting properties of the soil structure). The proposed methodology has potential for facilitating ground water vulnerability modeling at a regional scale and can be used for other regions, but would require incorporation of appropriate input parameters suitable for the region. This study is the first step toward incorporation of Neuro- fuzzy techniques, GIS, GPS and remote sensing in the assessment of ground water vulnerability from non-point source contaminants.