Abstract
Aim of the paper is the development of a Fuzzy Decision Support System (FDSS) for the Environmental Risk Assessment (ERA) of the deliberate release of genetically modified plants. The evaluation process permits identifying potential impacts that can achieve one or more receptors through a set of migration paths. ERA process is often performed in presence of incomplete and imprecise data and is generally yielded using the personal experience and knowledge of the human experts. Therefore the risk assessment in the FDSS is obtained by using a Fuzzy Inference System (FIS), performed using jFuzzyLogic library. The decisions derived by FDSS have been validated on real world cases by the human experts that are in charge of ERA. They have confirmed the reliability of the fuzzy support system decisions.
Chapter PDF
Similar content being viewed by others
Keywords
References
Chen, Y.-L., Weng, C.-H.: Mining fuzzy association rules from questionnaire data. Knowledge-Based Systems 22, 46–56 (2009)
Chen, Z., Zhao, L., Lee, K.: Environmental risk assessment of offshore produced water discharges using a hybrid fuzzy-stochastic modeling approach. Environmental Modelling & Software 25, 782–792 (2010)
Ciaramella, A., Tagliaferri, R., Pedrycz, W.: The genetic development of ordinal sums. Fuzzy Sets and Systems 151(2), 303–325 (2005)
Cingolani, P., Fdez, J.A.: jFuzzyLogic: A Robust and Flexible Fuzzy-Logic Inference System Language Implementation. In: Proceedings of IEEE World Congress on Computational Intelligence 2012 (2012)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. The MIT Press (2009)
Davidson, V.J., Ryks, J., Fazil, A.: Fuzzy risk assessment tool for microbial hazards in food systems. Fuzzy Sets and Systems 157, 1201–1210 (2006)
Guimara, A.C.F., Lapa, C.M.F.: Fuzzy inference to risk assessment on nuclear engineering systems. Applied Soft Computing 7, 17–28 (2007)
Kahraman, C., Kaya, I.: Fuzzy Process Accuracy Index to Evaluate Risk Assessment of Drought Effects in Turkey. Human and Ecological Risk Assessment 15, 789–810 (2009)
Karimi, I., Hullermeier, E.: Risk assessment system of natural hazards: A new approach based on fuzzy probability. Fuzzy Sets and Systems 158, 987–999 (2007)
International Electrotechnical Commission technical committee industrial process measurement and control2. IEC 61131 - Programmable Controllers. Part 7: Fuzzy Control Programming. IEC 2000 (2000)
Li, W., Zhou, J., Xie, K., Xiong, X.: Power System Risk Assessment Using a Hybrid Method of Fuzzy Set and Monte Carlo Simulation. IEEE Transactions on Power Systems 23(2) (2008)
Lin, C.-T., Lee, C.S.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice Hall (1996)
Ngai, E.W.T., Wat, F.K.T.: Design and development of a fuzzy expert system for hotel selection. Omega 31, 275–286 (2003)
Parr, T.J., Quong, R.W.: Software: Practice and Experience 25(7), 789–810 (1995)
Sadiqa, R., Husain, T.: A fuzzy-based methodology for an aggregative environmental risk assessment: A case study of drilling waste. Environmental Modelling & Software 20, 33–46 (2005)
Sorlini, C., Buiatti, M., Burgio, G., Cellini, F., Giovannelli, V., Lener, M., Massari, G., Perrino, P., Selva, E., Spagnoletti, A., Staiano, G.: La valutazione del rischio ambientale dell’ immissione deliberata nell’ ambiente di organismi geneticamente modificati. Tech. Report (2003) (in Italian), http://bch.minambiente.it/EN/Biosafety/propmet.asp
Wang, Y.-M., Elhag, T.M.S.: An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Systems with Applications 34, 3099–3106 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Camastra, F. et al. (2013). Environmental Risk Assessment of Genetically Modified Organisms by a Fuzzy Decision Support System. In: Petrosino, A., Maddalena, L., Pala, P. (eds) New Trends in Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41190-8_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-41190-8_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41189-2
Online ISBN: 978-3-642-41190-8
eBook Packages: Computer ScienceComputer Science (R0)