CBR respond and preparedness system development for environmental emergency

Civil Engineering and Environmental Systems

Volumn 28, Issue 4, 2011, Pages 301-323

  Liao, Zhenliang ^a   Mao, Xuewei ^a   Liu, Yanhui ^a   Xu, Zuxin ^a   Hannam, Phillip M ^b  

^a Tongji University ^*  (China)

^b TONGJI UNIVERSITY   (China)

Author keywords

Case based reasoning; Emergency preparedness; Environmental emergency; Oil spill

Indexed keywords

EMERGENCY SERVICES; HIERARCHICAL SYSTEMS; OIL SPILLS;

CASE REPRESENTATION; CASEBASED REASONINGS (CBR); EMERGENCY PREPAREDNESS; ENVIRONMENTAL EMERGENCIES; HIERARCHICAL MODEL; SIMILARITY FUNCTIONS; SYSTEM ARCHITECTURES; SYSTEM DEVELOPMENT;

CASE BASED REASONING;

DISASTER MANAGEMENT; HAZARD ASSESSMENT; METHODOLOGY; NATURAL HAZARD; NUMERICAL MODEL; OIL SPILL; PLANNING PROCESS; UNCERTAINTY ANALYSIS;

EID: 84862969239     PISSN: 10286608     EISSN: 10290249     Source Type: Journal    
DOI: 10.1080/10286608.2011.604416     Document Type: Article

References (47)

1. Aamodt, A. and Plaza, E., 1994. Case-Based Reasoning: foundational issues, methodological variations, and system approaches. AI Communications, 7 (1), 39-59.
2. Assaf, H. and Saadeh, M., 2008. Assessing water quality management options in the Upper Litani Basin, Lebanon, using an integrated GIS-based decision support system. Environmental Modelling & Software, 23 (10-11), 1327-1337.
3. Bajo, J., et al., 2009. Integrating case-based planning and RPTW neural networks to construct an intelligent environment for health care. Expert Systems with Applications, 36 (3), 5844-5858.
4. Bansiya, J. and Davis, C.G., 2002. A hierarchical model for object-oriented design quality assessment. The IEEE Transactions on Software Engineering, 28 (1), 4-17.
5. Barletta, R., 1991. An introduction to case-based reasoning. AI Expert, 6 (8), 43-49.
6. Bianconi, R., Galmarini, S., and Bellasio, R., 2004. Web-based system for decision supportin caseof emergency: ensemble modeling of long-range atmospheric dispersion of radionuclides. Environmental Modelling & Software, 19 (4), 401-411.
7. Bodis, L., Ross, A., and Pretsch, E., 2007. A novel spectra similarity measure. Chemometrics and Intelligent Laboratory Systems, 85 (1), 1-8.
8. Chang, N.B., et al., 1997. The design of a GIS-based decision support system for chemical emergency preparedness and response in an urban environment. Computers, Environment and Urban Systems, 21 (1), 67-94.
9. Chen, H. and Wang, K., 2006. Hazard chemical accident disposal and emergency preparedness. Beijing: China Petrochemical Press (in Chinese).
10. Chen, W.T., et al., 2010. Developing a CBR-based adjudication system for fatal construction industry occupational accidents. Part I: Building the system framework. Expert Systems with Applications, 37 (7), 4867-4880.
11. Chinese Central Government, 2006. Chinese National Preparedness for Environmental Emergencies (in Chinese).
12. Desimone, R.V. and Agosta, J.M., 1994. Oil-spill response simulation: the application of artificial intelligence planning technology. In: Proceedings of 1994 Simulation Multiconference, Simulation for Emergency Management Track, San Diego, CA, USA, 36-44.
13. Diaz, F., Fdez-Riverola, F., and Corchado, J.M., 2006. GENE-CBR: a case-based reasoning tool for cancer diagnosis using microarray data sets. Computational Intelligence, 22 (3-4), 254-268.
14. Didan, K. and Yitayew, M., 2009. Prototype geographic information system for agricultural water quality management using cropsyst. Journal of Irrigation and Drainage Engineering, 135 (1), 58-67.
15. Dokas, I.M., Karras, D.A., and Panagiotakopoulos, D.C., 2009. Fault tree analysis and fuzzy expert systems: early warning and emergency response of landfill operations. Environmental Modelling & Software, 24 (1), 8-25.
16. Faucher, C., 2001. Easy definition of new facets in the frame-based language Objlog. Data and Knowledge Engineering, 38 (3), 223-263.
17. Fedra, K. and Winkelbauer, L., 2002. A hybrid expert system, GIS and simulation modeling for environmental and technological risk management. Civil and Infrastructure Engineering, 17 (2), 131-142.
18. Forbes, D.R., Smith, S.D., and Horner, R.M.W., 2010. The selection of risk management techniques using case-based reasoning. Civil Engineering and Environmental Systems, 27 (2), 107-121.
19. Fu, T., 2006.Environmental emergency and typical cases. Beijing, China: China Environmental Science Press (in Chinese).
20. Gheorghe, A.V. and Vamanu, D., 1995. Pilot decision support system for nuclear power emergency management. Safety Science, 20 (1), 13-26.
21. Gheorghe, A.V. and Vamanu, D.V., 1998. Decision-support software tools for integrated risk assessment of hazardous substances in complex terrain. International Journal of Environment and Pollution, 9 (4), 352-370.
22. Guerrier, P. and Serodes, J., 1995. Canadian expert systems for technological accidents preparedness and response. In: Proceedings of the 1995 10th international conference on applications of artificial intelligence in engineering, Udine, Italy, 357-375.
23. Guo, L., et al., 2009. Emergency decision-making case base system for river region based on CBR-AHP. In: ICEET: 2009 international conference on energy and environment technology, vol. 2, Guilin, China, 539-542.
24. Haque, A., 2004. Disaster preparedness ina digital environment: acommunity preparedness and response model (CPR) for local governments. In: International conference on politics and information systems: technologies and applications, vol. 2, Orlando, FL, USA, 168-173.
25. Heino, P. and Kakko, R., 1998. Risk assessment modelling and visualization. Safety Science, 30 (1-2), 71-77.
26. Juhola, M. and Laurikkala, J., 2007. On metricity of two heterogeneous measures in the presence of missing values. Artificial Intelligence Review, 28 (2), 163-178.
27. King, J.M.P., Banares-Alcantara, R., and Manan, Z.A., 1999. Minimising environmental impact using CBR: an azeotropic distillation case study. Environmental Modelling & Software, 14 (5), 359-366.
28. Kolodner, L., 1992. An introduction to case-based reasoning. Artificial Intelligence Review, 6 (1), 3-34.
29. Kuo, T.C., 2010. Combination of case-based reasoning and analytical hierarchy process for providing intelligent decision support for product recycling strategies. Expert Systems with Applications, 37 (8), 5558-5563.
30. Lee, G.H., 2008. Rule-based and case-based reasoning approach for internal audit of bank. Knowledge-Based System, 21 (2), 140-147.
31. Leung, Y., et al., 2005. An environmental decision-support system for the management of water pollution in a tidal river network. International Journal of Geographical Information Science, 19 (4), 483-500.
32. Madhusudan, T., Zhao, J.L., and Marshall, B., 2004.A case-based reasoning framework for workflow model management. Data & Knowledge Engineering, 50 (1), 87-115.
33. Minsky, M., 1975. A framework for representing knowledge. In: P.H. Winston, ed. The psychology of computer vision. New York: McGraw-Hill.
34. Mohamed, H., Sulaiman, S., and Sabudin, M., 2009. A hybrid of rule and frame based approach in solving Hajj complex Problems. In: 2009 international conference of soft computing and pattern recognition, Malacca, Malaysia, 466-471.
35. Qinn, N.W.T. and Jacobs, K.C., 2007. Design and implementation of an emergency environmental response system to protect migrating salmon in the lower San Joaquin River, California. Environmental Modelling & Software, 22 (4), 416-422.
36. Raman, R., 2004. Accounting for dynamic processes in process emergency response using event tree modeling. In: Center for Chemical Process Safety - 19th annual international conference - Emergency planning preparedness, prevention and response, Orlando, FL, USA, 197-213.
37. Riverola, F.F. and Corchado, J.M., 2004. FSfRT: forecasting system for red tides. Applied Intelligence, 21 (3), 251-264.
38. Sanchez-Marre, M., et al., 1997. Learning and adaptation in wastewater treatment plants through Case-Based Reasoning. Microcomputers in Civil Engineering, 12 (4), 251-266.
39. Schank, R., 1982. Dynamic memory.A theory of reminding and learning in computers and people. Cambridge: Cambridge University Press.
40. Scott, A., Tysklind, M., and Fangmark, I., 2002. Selection of a representative set of chemical accidents from a complex data matrix for the development of environment-accident index. Journal of Hazardous Materials, 91 (1-3), 63-80.
41. Topouzelis, K., et al., 2007. Detection and discrimination between oil spills and look-alike phenomena through neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 62 (4), 264-270.
42. Topouzelis, K., Stathakis, D., and Karathanassi, V., 2009. Investigation of genetic algorithms contribution to feature selection for oil spill detection. International Journal of Remote Sensing, 30 (3), 611-625.
43. Trepel, M., 2007. Evaluationofthe implementationofagoal-orientedpeatland rehabilitation plan. Ecological Engineering, 30 (2), 167-175.
44. Verdenius, F. and Broeze, J., 1999. Generalised and instance-specific modelling for biological systems. Environmental Modelling and Software, 14 (5), 339-348.
45. Wang, H.J., Chiou, C.W., and Juan, Y.K., 2008. Decision support model based on case-based reasoning approach for estimating the restoration budget of historical buildings. Expert Systems with Applications: An International Journal, 35 (4), 1601-1610.
46. Waston, I., 1997. Applying case-based reasoning: techniques for enterprise systems. San Mateo, CA: Morgan Kaufman.
47. Waston, I. and Marir, F., 1994. Case-based reasoning: a review. The Knowledge Engineering Review, 9 (4), 355-381.