Hydraulic conductivity estimation via fuzzy analysis of grain size data

A measure of hydraulic conductivity is arguably the most important variable to practicing hydrogeologists. However, the amount of readily available hydraulic conductivity data at any site is generally small, given the resources required to adequately sample a spatial domain. However, other hydrogeologic data, such as grain size distributions and soil descriptions, are often rather easy to obtain. A fuzzy reasoning algorithm is used to define a relationship between soil grain size and hydraulic conductivity. By introducing soil grain distributions and qualitative borehole log descriptions into this fuzzy inference system, hydraulic conductivity can be estimated. The theory is defined, and an application to data from a Superfund site is provided, where the inference procedure produces accurate hydraulic conductivity estimates.