A fuzzy expert approach for comparing alternative end uses for requalification of contaminated sites
An important aspect in the issue of contaminated sites is their use after remediation. The choice of a certain use has got necessarily socio-economic implications that make it more or less suitable for that site. For this reason it is important to insert socio-economic analysis within the studies on contaminated site remediation and to implement approaches that calibrate extent and modality of the remediation on the basis of potential uses of the site after it. In particular it is important to provide decision-makers with tools that offer them the possibility to consider also this aspect, in order to support their decisions. The specific aim of the socio-economic module is providing the decision makers with a tool that makes possible to compare the different use destinations, outlining possible scenarios linked to alternative uses of the considered site, on the basis of socio-economic considerations (often founded on theories and methods of the spatial analysis) and of local characteristics. Comparing these scenarios it aims at giving indications on which use is more suitable and why. The final objective is just to establish which is the “best” use for that site. The term “best” indicates that it is possible to rank the whole different use destinations. This is a typical multicriteria decision making problem: there is not a natural order in a multidimensional space, so it is necessary to find a device to do it. A method to rank them is to define a function from the attribute space in the real line and so obtain a total order induced by the real number one. The scientific literature is reach of several ways to approach this problem. Here we propose a method typical of Artificial Intelligence: a Fuzzy Expert Systems (FES). The final product is a software prototype for the remediation of contaminated sites: DESYRE (DEcision Support sYstem for the REqualification of contaminated sites, www.r3environmental.co.uk/dstdemo/), that represents a useful tool for decision-makers. Entering data,relative to the considered site, the software is able to elaborate them to give back one numerical index for each possible use destination (UD). These indexes represent synthetic and comparable expressions of the socio-economic implications deriving from each UD. In this way the decision-makers can compare the opportunities coming from the different uses and have a synthetic indicator, without losing the whole information. The software works in a very transparent way, so that it is possible to highlight which factors determine the high or low index value.
Key wordsEnvironmental analysis contaminate sites socio-economic variables fuzzy expert systems
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- Bandermer H., Gottwald S. (1996) Fuzzy sets, fuzzy logic fuzzy methods. John Wiley & Sons New York.Google Scholar
- Desfor G. e Keil R, Contested and Polluted Terrain, in Local Environment, vol. 4, a 3, 1999.Google Scholar
- Facchinetti G.: (2001). “Fuzzy Expert Systems: Economic and Financial Applications” In Advanced Computer System J. Soldek and J Pejas eds, 3–26. Kluwer Academic Publishers.Google Scholar
- Facchinetti G.-Mastroleo G.-Paba S.: (2000) “A fuzzy approach to the geography of industrial districts”; “Proceedings of the 2000 ACM Symposium on Applied Computing” Carrol J., Damiani E., Haddam FL, Oppenheim D. Editors. ISBN: 1-58113-239-5, Vol.1 514–518.Google Scholar
- Facchinetti G.-Bordoni S.-Mastroleo G.: (2000) “Bank Creditworthiness using Fuzzy Systems: A Comparison with a Classical Analysis Technique” Risk Assesment and Management in Technology, Environment and Finance. Da Ruan, Fedrizzi M. e Kacprzyk J. Editors. Springer Verlag Press. Pubblicato nella sezione Fuzzy Applications and Library del sito web http:/www.fuzzytech.com.Google Scholar
- Facchinetti G.-Magni C-Mastroleo M-Vignola V. “Valuing strategic investment with a fuzzy expert system: an italian case” on proceedings of International Fuzzy System Association Fuzzyness And Soft Computing In The New Millenium (IFSA 2001), July 25–28 Vancouver Canada.Google Scholar
- Kasabov N.K. (1996) Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. MTT Press.Google Scholar
- Kosko, B., 1992. Fuzzy Systems as Universal Approximators. Proc. IEEE Int. Conf. On Fuzzy Systems, 1153–1162.Google Scholar
- Meyer P.B., Williams R.H. e Yount K.R., Contaminated Land: reclamation, redevelopment and reuse in USA and EU, Aldershot, Gower, 1995.Google Scholar
- Soriani S. (1996) “The Venice Port and Industrial Area in a Context of Regional Change”, in Cityports, Coastal Zones and Regional Change, B.S. Hoyle ed., Chichester, Wiley, pp. 235–248.Google Scholar
- von Altrock C. (1997). “Fuzzy Logic and neurofuzzy applications in business and finance.” Prentice Hall.Google Scholar
- Wang, L., 1992. Fuzzy systems are universal approximators. Proc. Of Int. Conf. On Fuzzy Systems.Google Scholar