Alessandro Mazzella

The Datacrossing DSS: A Data-GRID Based Decision Support System for Groundwater Management

The Datacrossing DSS (http://datacrossing.crs4.it) is a basin-scale groundwater model that relies on a geographically distributed GIS to support decision makers, through a user-friendly Web interface, in the field of sustainable water resources management. The portal, for the general user, exposes hydrological applications based on complex models that make use of large volumes of distributed data available in a GRID infrastructure. Free software and in-house technologies are combined to transparently and automatically deploy the applications. Our objective is to build a development platform that by introducing the computational and data-sharing advantages of the GRID infrastructure promotes joint initiatives and encourages cooperation among multidisciplinary teams operating in environmental sciences. To illustrate the potential of our data-grid DSS, we present its application to a Sardinian case history where a coastal aquifer is threatened by the leakage of highly toxic inorganic residuals from the Portoscuso industrial settlements.

Use of Kriging Technique to Study Roundabout Performance

Road intersections are dangerous places because of the many conflicting points between motorized and nonmotorized vehicles. In the case of defined traffic volume, several research groups have proved that roundabouts reduced the number of injuries and fatal accident cases. In recent years, many countries have adopted roundabouts as a standard design solution for both urban and rural roads. Several recent studies have investigated the performance of roundabouts, including some with models that calculated the entering flow (Qe) as a function of the circulating flow (Qc). Most existing models have been constructed with the use of linear or exponential statistical regression. The interpolative techniques in classical statistics are based on the use of canonical forms (linear or polynomial) that completely ignore the correlation law between collected data. As such, the determined interpolation stems from the assumption that the data represent a random sample. In the research reported in this paper, a geostatistical approach was considered: the relationship Qe versus Qc is supposed to be a regionalized phenomenon. According to that supposition, collected data do not represent a random sample of values but are supposed to be related to each other with a defined law. This recognition allows the realization of interpolation on the basis of the real law of the phenomenon. This paper discusses the fundamental theories, the applied operating procedures, and the first results obtained in modeling the Qe versus Qc relationship with the application of geostatistics.

The Importance of the Model Choice for Experimental Semivariogram Modeling and Its Consequence in Evaluation Process

Geostatistics was created during the second half of 20th century by Georges Matheron, on the basis of Danie Krige’s and Herbert Sichel’s theories. The purpose of this new science was to achieve an optimal evaluation of mining ore bodies. The interest in geostatistical tools has grown, and nowadays its techniques are applied in many branches of engineering where data analysis, interpolation, and evaluation are necessary. This paper presents an overview of the geostatistics approach in data analysis and describes each operative step from experimental semivariogram calculation to kriging interpolation, focusing and underlining the experimental semivariogram modeling step. To help any data analysts during geostatistical analysis process, an innovative geostatistical software was created. This new software, named “Kriging Assistant” (KA) and developed within the Department of Geoengineering and Environmental Technologies University of Cagliari, is able, with a marginal support of the user, to produce 2D and 3D grids and contour maps of sampled data. A comparison between kriging results obtained by KA and two of the most common data analysis softwares (Golden Software Surfer and ESRI Geostatistical Analyst for ArcMap) is presented in this paper. Reported data showed that KA minimizes interpolation errors and, for this reason, provides better interpolation results.

Artificial Neural Networks and Kriging Method for Slope Geomechanical Characterization

In this work, Multi Layer Perceptron Artificial Neural Networks (MLP ANNs) and Kriging method are applied for slope stability analysis. Both methods have been applied in order to evaluate detrital layer depth within a test site located in countryside of Capoterra (South Sardinia, Italy). Test site consists in a large area subjected to flooding and great magnitude debris flow events. Identified area stability and strength have been analysed by building a local geodatabase that allowed to perform a correlation analysis between depth of the detrital layers and respective geotechnical, geo-mechanical, hydraulic characteristics. Some other features regarding morphological, geological, structural, physiographic and vegetation settings have been considered. The comparison between the results obtained with the MLP ANNs and kriging method shows that the two methods can be applied to implement a realistic and accurate representation of the depth and geomechanical properties of incoherent deposits.