Uncertain spatial reasoning of environmental risks in GIS using genetic learning algorithms

Environmental Monitoring and Assessment

Volumn 184, Issue 10, 2012, Pages 6307-6323

  Shad, Rouzbeh ^a,b   Shad, Arefeh ^a,b  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

^b FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

Fuzzy; Genetic; GIS; Learning; Reasoning

Indexed keywords

ENVIRONMENTAL RISKS; EXPERT JUDGMENT; FUZZY; GENETIC; GENETIC FUZZY SYSTEMS; GENETIC LEARNING; GENETIC LEARNING ALGORITHMS; GENETIC-FUZZY; LANDSAT THEMATIC MAPPER; LEARNING; LEARNING CAPABILITIES; LEARNING METHODS; PERSIAN GULF; REASONING; SATELLITE IMAGES; SPATIAL BEHAVIORS; SPATIAL DATABASE; SPATIAL KNOWLEDGE; SPATIAL REASONING; TOPOLOGICAL RULES; TRAINING DATA; TUNING RULES;

AIR POLLUTION; GEOGRAPHIC INFORMATION SYSTEMS; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; OIL WELL FIRES;

IMAGE PROCESSING;

ATMOSPHERIC POLLUTION; ENVIRONMENTAL RISK; FUZZY MATHEMATICS; GENETIC ALGORITHM; GIS; SPATIAL ANALYSIS; UNCERTAINTY ANALYSIS;

ACCURACY; AIR POLLUTION; ARTICLE; CONTROLLED STUDY; DATA BASE; ENVIRONMENTAL MONITORING; FIRE; FUZZY SYSTEM; INTERMETHOD COMPARISON; LEARNING ALGORITHM; OIL FIELD; RISK ASSESSMENT; WAR;

AIR POLLUTANTS; AIR POLLUTION; ALGORITHMS; ENVIRONMENTAL MONITORING; FUZZY LOGIC; GEOGRAPHIC INFORMATION SYSTEMS; MODELS, CHEMICAL; OIL AND GAS FIELDS; PETROLEUM POLLUTION; RISK ASSESSMENT;

EID: 84866543780     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-011-2421-z     Document Type: Article

References (31)

1. Bojórquez-Tapia, L. A., Juárez, L., & Cruz-Bello, G. (2002). Integrating fuzzy logic, optimization, and GIS for ecological impact assessments. Environmental Management, 30 (3), 418-433.
2. Brink, A. B., & Eva, H. D. (2009). Monitoring 25 years of land cover change dynamics in Africa: a sample based remote sensing approach. Applied Geography, 29, 501-512.
3. Cano, J. R., Herrera, F., & Lozano, M. (2006). On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. Applied Soft Computing, 6, 323-332.
4. Cordón, O., Herrera, F., & Peregrín, A. (1997). Applicability of the fuzzy operators in the design of fuzzy logic controllers. Fuzzy Sets and Systems, 86(1), 15-41.
5. Cordón, O., Herrera, F., Villar, P., Hoffmann, F., & Magdalena, L. (2000). Genetic fuzzy systems-evolutionary tuning and learning of fuzzy knowledge bases. Singapore: World Scientific Co.
6. Cordón, O., Herrera, F., Hoffmann, F., & Magdalena, L. (2001). Genetic fuzzy systems. Evolutionary tuning and learning of fuzzy knowledge bases. River Edge: World Scientific.
7. Cordón, O., Herrera, F., & Villar, P. (2001). Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Transaction of Fuzzy Systems, 9(4), 667-674.
8. Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., & Magdalena, L. (2004). Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems, 41, 5-31.
9. Darbra, R. M., Eljarat, E., & Bracelo, D. (2008). How to measure uncertainties in environmental risk assessment. Trends in Analytical Chemistry, 27, 377-385.
10. Ferson, S. (2003). RAMAS Risk Calc 4.0 Software: risk assessment with uncertain numbers. Boca Raton: Lewis Publishers.
11. Freitas, A. A. (2002). Data mining and knowledge discovery with evolutionary algorithms. Berlin: Springer.
12. Homaifar, A., & McCormick, E. (1995). Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Transaction of Fuzzy Systems, 3(9), 129-139.
13. Ishibuchi, H., & Yamamoto, T. (2004). Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems, 141, 59-88.
14. Kaiser, M. F. (2009). Environmental changes, remote sensing, and infrastructure development: the case of Egypt's East Port Said harbour. Applied Geography, 29, 280-288.
15. Karr, C. L., & Gentry, E. J. (1993). Fuzzy control of ph using genetic algorithms. IEEE Transaction of Fuzzy Systems, 1, 46-53.
16. Kentel, E., & Aral, M. M. (2005). 2D Monte Carlo versus 2D fuzzy Monte Carlo health risk assessment. Stochastic Environmental Research and Risk Assessment, 19(1), 86- 96.
17. Konar, A. (2005). Computational intelligence: principles, techniques and applications. Berlin: Springer.
18. Kortenkamp, D., Bonasso, R. P., & Murphy, R. (1998). Artificial intelligence and mobile robots: case studies of successful robot systems. Cambridge: AAAI Press.
19. Lavrac, N., Cestnik, B., Gamberger, D., & Flach, P. (2004). Decision support through subgroup discovery: three case studies and the lessons learned. Machine Learning, 57(1- 2), 115-143.
20. Li, H. L., Huang, G. H., & Zou, Y. (2008). An integrated fuzzystochastic modeling approach for assessing health-impact risk from air pollution. Stochastic Environmental Research and Risk Assessment, 22(6), 789-803.
21. Lin, C. J. (2004). A GA-based neural fuzzy system for temperature control. Fuzzy Sets and Systems, 143, 311- 333.
22. Lin, C. J., & Xu, Y.-J. (2006). A novel genetic reinforcement learning for nonlinear fuzzy control problems. Neurocomputing, 69, 2078-2089.
23. Makkeasorn, A., Chang, N.-B., & Li, J. (2009). Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed. Journal of Environmental Management, 90, 1069-1080.
24. Mikut, R., Jäkel, J., & Gröll, L. (2005). Interpretability issues in data-based learning of fuzzy systems. Fuzzy Sets and Systems, 150, 179-197.
25. Price, A. R. G. (1993). The Gulf: human impacts and management initiatives. Marine Pollution Bulletin, 27, 17-27.
26. Sánchez, L., Couso, I. (2005). Advocating the use of imprecisely observed data in genetic fuzzy systems. In Proc. of the First Int. Workshop on Genetic Fuzzy Systems (GFS 2005), Granada, Spain. pp. 124-129.
27. Sánchez, L., Casillas, J., Cordón, O., & del Jesus, M. J. (2001). Some relationships between fuzzy and random classifiers and models. International Journal of Approximate Reasoning, 29, 175-213.
28. Setzkorn, C., & Paton, R. C. (2005). On the use of multiobjective evolutionary algorithms for the induction of fuzzy classification rule systems. Bio Systems, 81, 101- 112.
29. TØmmervik, H., Johansen, M. E., Pedersen, J. P., & Guneriussen, T. (1998). Integration of remote sensed and IN-SITU data in an analysis of the air pollution effects on terrestrial ecosystems in the border areas between Norway and Russia. Environmental Monitoring and Assessment, 49, 51-85.
30. Triolo, L., Binazzi, A., Cagnetti, P., Carconi, P., Correnti, A., De Luca, E., et al. (2008). Air pollution impact assessment on agroecosystem and human health characterisation in the area surrounding the industrial settlement of Milazzo (Italy): a multidisciplinary approach. Environmental Monitoring and Assessment, 140, 191-209.
31. Wang, H., Kwong, S., Jin, Y., Wei, W., & Man, K. F. (2005). Multiobjective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets and Systems, 149, 149-186.