L'ottimizzazione delle Reti di Monitoraggio con Tecniche Fuzzy

Spatial and temporal behavior of hydrochemical parameters in groundwater can be studied using tools provided by geostatistics. The cross-variogram can be used to measure the spatial increments between observations at two given times as a function of distance (spatial structure). Taking into account the existence of such a spatial structure, two different data sets (sampled at two different times), representing concentrations of the same hydrochemical parameter can be analyzed by cokriging in order to reduce the uncertainty of the estimation. In particular, if one of the two data sets is a subset of the other (i.e. an under-sampled set), cokriging allows us to study the spatial distribution of the hydrochemical parameter at that time, while also considering the statistical characteristics of the full data set established at a different time. Cokriging Estimation Variance (CEV) is a useful tool in order to determine the influence of the spatial configuration of monitoring networks on the estimations. Then it can be useful for evaluating an optimal arrangement of the wells of a monitoring network for estimating the parameter values in a given number of target sites. Nevertheless, an optimal pattern of a monitoring wells suffers of the uncertainties on the variogram parameters caused by the variographer's choices. Using a fuzzy approach to represent these uncertainties, it is possible to describe the variogram parameters as fuzzy numbers and the parameter uncertainties by means of membership functions. Using such fuzzy variograms, the cokriging method produces membership functions of both the estimation and the CEV. Such optimization procedure has been tested on real cases.