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Assessing coastal sustainability: a Bayesian approach for modeling and estimating a global index for measuring risk

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Integrated Coastal Zone Management is an emerging research area. The aim is to provide a global view of different and heterogeneous aspects interacting in a geographical area. Decision Support Systems, integrating Computational Intelligence methods, can be successfully used to estimate useful anthropic and environmental indexes. Bayesian Networks have been widely used in the environmental science domain. In this paper a Bayesian model for estimating the Sustainable Coastal Index is presented. The designed Bayesian Network consists of 17 nodes, hierarchically organized in 4 layers. The first layer is initialized with the season and the physiographic region information. In the second layer, the first-order indexes, depending on raw data, of physiographic regions are computed. The third layer estimates the second-order indexes of the analyzed physiographic regions. In the fourth layer, the global Sustainable Coastal Index is inferred. Processed data refers to 13 physiographic regions in the Province of Trapani, western Sicily, Italy. Gathered data describes the environmental information, the agricultural, fisheries, and economical behaviors of the local population and land. The Bayesian Network was trained and tested using a real dataset acquired between 2000 and 2006. The developed system presents interesting results.
Rocznik
Tom
Strony
5--15
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
  • Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi, University of Palermo, Palermo, Italy
autor
  • Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica, University of Palermo, Palermo, Italy
autor
  • IAMC-CNR, National Research Council, Istituto per l’ambiente marino costiero, Mazara del Vallo (TP), Italy
autor
  • Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica, University of Palermo, Palermo, ICAR-CNR, National Research Council, Istituto di calcolo e reti ad alte prestazioni, Palermo, Italy
Bibliografia
  • [1] Y. Tyrinopoulos, E. Mitsakis, and A. Kortsari, “A decision support tool for the sustainable handling of seasonal variations of transport demand”, in Proc. 13th Int. IEEE Conf. Intell. Transport. Sys. ITSC 2010, Madeira, Portugal, 2010, pp. 724–729.
  • [2] C. Laudy and B. Goujon, “Soft data analysis within a decision support system”, in Proc. 12th IEEE Int. Conf. Infor. Fusion FUSION’09, Seattle, USA, 2009, pp. 1889–1896.
  • [3] N. Gooroochurn and G. Sugiyarto, “Competitiveness indicators in the travel and tourism industry”, Tourism Econom., vol. 11, no. 1, pp. 25–43, 2005.
  • [4] F. Ya-qin, Z. Qiu-wen, and W. Cheng, “Assessing the sustainability of cascade hydropower development based on complex ecology system”, in Proc. 2nd Int. Conf. Bioinform. Biomed. Engin. iCBBE 2008, Shanghai, China, 2008, pp. 4282–4285.
  • [5] R. B. Pollnac and R. S. Pomeroy, “Factors influencing the sustainability of integrated coastal management projects in the Philippines and Indonesia”, Ocean & coastal management, vol. 48, no. 3, pp. 233–251, 2005.
  • [6] R. E. Bowen and C. Riley, “Socio-economic indicators and integrated coastal management”, Ocean & Coastal Management, vol. 46, no. 3, pp. 299–312, 2003.
  • [7] R. K. Turner, “Integrating natural and socio-economic science in coastal management”, J. Marine Syst., vol. 25, no. 3, pp. 447–460, 2000.
  • [8] C. N. Ehler, “Indicators to measure governance performance in integrated coastal management”, Ocean & Coastal Manag., vol. 46, no. 3, pp. 335–345, 2003.
  • [9] C. Shi, S. Hutchinson, and S. Xu, “Evaluation of coastal zone sustainability: an integrated approach applied in Shanghai municipality and Chong Ming island”, J. Environ. Manag., vol. 71, no. 4, pp. 335–344, 2004.
  • [10] U. Brunelli, V. Piazza, L. Pignato, F. Sorbello, and S. Vitabile, “Three hours ahead prevision of SO2 pollutant concentration using an elman neural based forecaster”, Build. & Environ., vol. 43, no. 3, pp. 304–314, 2008.
  • [11] U. Brunelli, V. Piazza, L. Pignato, F. Sorbello, and S. Vitabile, “Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy”, Atmosph. Environ., vol. 41, no. 14, pp. 2967–2995, 2007.
  • [12] A. Farruggia, G. Lo Re, and M. Ortolani, “Probabilistic anomaly detection for wireless sensor networks”, in Proc. 12th Int. Conf. Artif. Intell. Around Man Beyond AIIA’11, LNAI 6934, Springer, 2011, pp. 438–444.
  • [13] M. Singh and M. Valtorta, “Construction of Bayesian Network structures from data: a brief survey and an efficient algorithm”, in Int. J. Approx. Reas., vol. 12, iss. 2, pp. 111–131, 1995.
  • [14] G. Pernice (Curatore), “Elaborazione di un modello di gestione integrata della zona costiera della Provincia di Trapani”, Rapporto finale, N.T.R. – I.R.M.A. Special Publication no. 11, IAMC-CNR – U.O.D. di Mazara del Vallo, 2007 (in Italian).
  • [15] Netica/Norsys Software Inc. website, July 2013 [Online]. Available: http://www.norsys.com/netica.html
  • [16] S. L. Lauritzen and D. J. Spiegelhalter, “Local computations with probabilities on graphical structures and their application to expert systems”, J. Royal Statis. Soc. Series B (Methodological), vol. 50, no. 2, pp. 157–224, 1988.
  • [17] F. V. Jensen, An Introduction to Bayesian Networks. New York: Springer, 1996.
  • [18] R. E. Neapolitan, Probabilistic Reasoning in Expert Systems: Theory and Algorithms. New York: Wiley, 1990.
  • [19] R. E. Neapolitan, Learning Bayesian Networks. New York: Prentice Hall, 2003.
  • [20] ArcGIS Desktop Software: Release 9, Environmental Systems Research Institute, Redlands, CA, USA, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6a6a1c17-c285-4525-b84a-3a414e2b6b9c
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