Abstract
Agencies and organizations charged with priority setting require analytical approaches that are accurate, efficient, and reliable. Increasingly, decision analysis is applied using formal techniques that are measurable and repeatable. This paper surveys available methods ranging from expert judgment approaches to complex statistical models, and considers the benefits and issues raised for decision making that applies various approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baxt, W.G. 1991. Use of an Artificial Neural Network for the Diagnosis of Myocardial Infarction. Annals of Internal Medicine, 115, 843–848: 1991.
Breiman, L; Friedman, J.H.; Olshen, R.; Stone, C.J., 1984. Classification and Regression Trees. Wadsworth International Group, Belmont, CA.
Commission of European Communities. 1997. European Union System for the Evaluation of Substances. EUSES 1.0 User Manual.
Commission on Geosciences, Environment, and Resources. 1998. Setting Priorities for Drinking Water Contaminants. National Academy Press. P. 113.
Davis, G.A., M. Swanson, and S. Jones. 1994. Comparative Evaluation of Chemical Ranking and Scoring Methodologies. Prepared for U.S. Environmental Protection Agency, Office of Pollution Prevention and Toxics, Washington, DC http://eerc.ra.utk.edu/clean/pdfs/CECRSM.pdf.
Fielding, A. 2000a. Biological Data Processing II: Multivariate Techniques. http://149.170.199.144/multivar/intro.htm#Multivariate
Fielding, A. 2000b. Joining Clusters: Clustering Algorithms. (http://149.170.199.144/multivar/ca_alg.htm)
Flug, M., H.L.H. Seitz, and J.F. Scott. 2000. Multicriteria Decision Analysis Applied to Glen Canyon Dam. Journal of Water Resources Planning and Management, ASCE, Vol. 126: 270–276.
Freeman, K. 2000. “Psychic networks: training computers to predict algal blooms.” Environmental Health Perspectives; Oct 108(10): A464–7.
Fuzzy Logic and Fuzzy Expert Systems Newsgroup. http://www-2.cs.cmu.edu/Groups/AI/html/faqs/ai/fuzzy/part1/faq.html
Fuzzy Logic in Environmental Sciences: A Bibliography. http://www.bjarne.ca/fuzzy_environment/#refs
Goossens, L.H.J., and R.M. Cooke. 2001. Expert Judgment Elicitation in Risk Assessment, In: I. Linkov and J. Palma-Oliveira (eds.), Assessment and Management of Environmental Risks, 411–426. NATO Science Series IV. Earth and Environmental Sciences Vol. 4. Kluwer Academic Publishers. Dordrecht.
Grassi M.; Villani S.; Marinoni A. Classification methods for the identification of ‘case’ in epidemiological diagnosis of asthma. European Journal of Epidemiology, 2001,vol. 17,no. 1, pp. 19–29(11)
Green, P.J. and B.W. Silverman. 1994. Nonparametric Regression and Generalized Linear Models A Roughness Penalty Approach. Chapman and Hall, London, U.K.
Harmonized Integrated Classification System for Human Health and Environmental Hazards of Chemical Substances and Mixtures. OECD Series on Testing and Assessment No. 33
Hastie, T. 1996. Computation: Neural Networks. In: J. Wiley (ed), Encyclopedia of Biostatistics.
Hastie, T. R. Tibshirani, and A. Buja. Flexible Discriminant and Mixture Models. Chapter 1 in, Statistics and Neural Networks — Advances at the Interface J. W. Kay, and D. M. Titterington 2000. Oxford University Press.
Hastie, T., Tibshirani, R., and Friedman, J., 2001. The Elements of Statistical Learning. Springer-Verlag, New York.
Hastie, T.J. and Tibshirani, R.J., 1990. Generalized Additive Models. Chapman and Hall, London.
Hastie, T.J. and Tibshirani, R.J., 1996. Discriminant analysis by Gaussian mixtures, Journal of Royal Statistical Society, B. 58:155–176.
Hastie, T.J., Buja, A., and Tibshirani, R.J., 1995. Penalized discriminant analysis. Annals of Statistics, 23:73–102.
Hastie, T.J., Tibshirani, R.J., and Buja, A., 1994. Flexible discriminant analysis by optimal scoring. Journal of American Statistical Association. 89:1255–1270.
Heckerman, D. 1995 A Tutorial on Learning With Bayesian Networks. Microsoft Research. MSR-TR-95-06
Heller, M., and Q. Wang, 1996. “Improving Potable Water Demand Forecasts with Neural Networks,” in Proceedings of UCOWR 1996, San Antonio, TX. New Waves Volume 9: 2.
Hinton, G.E. 1992. How Neural Networks Learn from Experience. Scientific American, September, 1992: 145–151.
http://www.epa.gov/oppfead1/harmonization/docs/doc/integr~1.doc.
http://smig.usgs.gov/SMIG/nnmodel_refs.htmlhttp://wtvw-stat.stanford.edu/~hastie/Papers/
Huuskonen, J., 2001: Estimation of water solubility from atom-type electrotopological state indices. Environ. Toxicol. Chem. 20, 491–497.
Huuskonen, J., 2000 Livingstone, D.J. & Tetko, I.V.: Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indeces, J. Chem. Inf. Comput. Sci. 40, 947–955.
Inductive Solutions, Inc. 2001. Neural Network and NNet Sheet FAQ http://www.inductive.com/softnet.htm
Janssen, R. 2001. On the use of multi-criteria analysis in environmental impact assessment in The Netherlands. Journal of Multi-Criteria Decision Analysis, vol. 10: 101–109.
Kerr, M. 2001. The Delphi Process, http://www.rararibids.org.uk/documents/bid79-delphi.htm
Kilsson, N.J. 1996. Introduction to Machine Learning. http://robotics.stanford.edu/people/nilsson/mlbook.html
Kon, M.A., and L. Plaskoa. 1997. Neural Networks, Radial Basis Functions, and Complexity. Proceedings of Bialowieza Conference on Statistical Physics, 122–145. http://math.bu.edu/people/mkon/nnpap3.pdf
Lin, H. and S. Wang. 2001. GIS Supported Modeling of Water Quality Using Artificial Neural Network (ANN) in the Tomorrow/Waupaca River Watershed. http://www.uwsp.edu/water/portage/action/sheng.htm
Lootsma, F. A. 2000. The decision analysis and support project. Journal of Multi-Criteria Decision Analysis, vol. 9: 7–10.
Michie, D., D.J. Spiegelhalter, and C.C. Taylor (eds). 1994. Machine Learning, Neural and Statistical Classification. Ellis Horwood. http://www.amsta.leeds.ac.uk/~charles/statlog/
Nerini D.; Durbec J.P.; Mante C.; Garcia F.; Ghattas B. Forecasting Physicochemical Variables by a Classification Tree Method. Application to the Berre Lagoon (South France). Acta Biotheoretica, December 2000, vol. 48, no. 3/4, pp. 181–196(16).
Neusciences. 2001. Support Vector Machines. http://www.neusciences.com/Technologies/collaboration_nats.htm
Nighswonger, G. 2000. ANNs Provide Tools for Increased Diagnostic Accuracy Medical Device and Diagnostic Industry. January Edition.
NRC. 1999a. Setting Priorities for Drinking Water Contaminants. National Academy Press.
NRC. 1999b. Identifying Future Drinking Water Contaminants. National Academy Press.
NRC. 2001. Classifying Drinking Water Contaminants for Regulatory Consideration. National Academy Press.
Organisation for Economic Co-operation and Development. 2001. Joint Meeting of the Chemicals Committee and the Working Party on Chemicals, Pesticides and Biotechnology. http://www1.oecd.org/ehs/Class/hclfinaw.pdf
Pontil, M, R. Rifkin, and T. Evgeniou. 1998. From Regression to Classification in Support Vector Machines. Massachusetts Institute of Technology, AI Memo 1649. ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-1649.pdf
Pontil, M. and A. Verri. 1997. Properties of Support Vector Machines. Massachusetts Institute of Technology, AI Memo 1612. ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-1612.pdf
Qian, S. and C. W. Anderson. Exploring Factors Controlling the Variability of Pesticide Concentrations in the Willamette River Basin Using Tree-Based Models. Environ. Sci. Technol. 1999. 33, 3332–3340.
Qian, S., W. Warren-Hicks, J. Keating, D.R.J. Moore and R. S Teed. 2000. A Predictive Model of Mercury Fish Tissue Concentrations for the Southeastern United States. Environ. Sci. Technol. 35(5):941–947.
R. Brause, T. Langsdorf, M. Hepp. Credit Card Fraud Detection by Adaptive Neural Data Mining. Internal Report 7/99, FB Informatik, University of Frankfurta.M., 1999.
R-F Yu, R.F., S.F Kang, S-L Liaw and M-c Chen. 2000. Application of artificial neural network to control the coagulant dosing in water treatment plant. Water Science & Technology Vol 42 No 3–4 pp 403.
Ripley, B.D., 1996. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, UK
S. Lawrence, C. L. Giles, A. Tsoi, and A. Back, January 1997, “Face recognition: A convolutional neural-network approach,” IEEE Trans, on Neural Networks, vol. 8, pp. 98–113.
Sarle, W. 2001. FAQ’s on Neural Networks. ftp://ftp.sas.com/pub/neural/FAQ.html#questions
Sarle, W.S. 1994. Neural Networks and Statistical Models. Proceedings of the Nineteenth Annual SAS Users Group International Conference, April, 1994.
Smith, L. 2001. An Introduction to Neural Networks, http://www.cs.stir.ac.uk/~lss
SRA. 2001. P.A. Murphy, G.E. Rice, USEPA. Overview of Comparative Risk-Integration of Scientific Ideas and Approaches, Society for Risk Analysis Annual Meeting December 5, 2001. Seattle, WA.
StatSoft. 2002. Statistica Neural Networks. http://www.statsoftinc.com/textbook/stneunet.html#intro
Shatkin, J.A. and J.M. Palma-Oliviera, C.A. Patton, C. Saraiva. 1998. Comparative Risk Assessment Method to Evaluate Impacts of Portuguese Industrial Waste Disposal. Society for Risk Annual Meeting and Expositon.
Stiber, NA., M. Pantazidou, and M.J. Small. 1999. Expert system methodology for evaluating reductive dechlorination at TCE sites. Environ. Sci. Technol. 33:3012–3020.
Stow, C.A. and Borsuk, M.E., 2002. Enhancing causal assessment of estuarine fishkills using graphical models. To appear in Ecosystems.
The Cadmus Group, Inc. 1992. The Cadmus Risk Index Approach.
The Consummate Design Center. 1996. The Delphi Process. http://www.tcdc.com/dmeths/dmeth5b.htm
Turoff, M. and S.R. Hiltz. 1996. Computer Based Delphi Processes. http://eies.njit.edu/~turoff/Papers/delphi3.html
U.S. Environmental Protection Agency. 1994. Waste Minimization Prioritization Tool Beta Test Version 1.0: User’s Guide and System Document.
US Environmental Protection Agency (US EPA). 1994. Chemical Hazard Evaluation Management Strategies: A Method for Ranking and Scoring Chemicals by Potential Human Health and Environmental Impacts, http://www.epa.gov/opptintr/cgi-bin/claritgw
US EPA. 1997. Announcement of the Draft Drinking Water Candidate Contaminant List; Notice. 62 FR 52194.
US EPA. 1998. Agency Guidance for Conducting External Peer Review of Environmental Regulatory Modeling http://www.epa.gov/ospinter/spc/modelpr.htm
US EPA. 2001a. Screening Level Tools, http://www.epa.gov/opptintr/exposure/docs/screen.htm
US EPA. 2001b. Source Ranking Database (SRD).
US EPA. 2001c. Use Clusters Scoring System.
Wei, B., N. Sugiura, and T. Maekawa. 2001. “Use of artificial neural network in the prediction of algal blooms.” Water Research. Jun; 35(8): 2022–8.
Weisman, O., and Z. Pollack. 1995. The Perceptron. http://www.cs.bgu.ac.il/~omri/Perceptron/
Wenstop, F., and K. Seip. 2001. Legitimacy and quality of multi-criteria environmental policy analysis: a meta-analysis of five MCE studies in Norway. Journal of Multi-Criteria Decision Analysis, vol. 10:53–64.
Wilson, R.A., and F. Keil. 2001. Decision Trees. The MIT Encyclopedia of the Cognitive Sciences. http://cognet.mit.edu/MITECS/Entry/utgoff.html
Woo, Y., D. Lai, J.L. McLain, M.K. Manibusan, and V. Dellarco. 2002. Use of Mechanism-Based Structure-Activity Relationships Analysis in Carcinogenic Potential Ranking for Drinking Water Disinfection By-Products. Environmental Health Perspectives, Vol. 110:75–87.
Z Solutions. 1999. A Light Introduction to Neural Networks, http://zsolutions.com/light.htm
Zaknixh, A. 1998. Artificial Neural Networks: An introductory course. http://www.maths.uwa.edu.au/~rkealley/ann_all/
Zhu, J. and Hastie, T., 2001. “Kernel Logistic Regression and the Import Vector Machine”, refereed paper accepted for NIPS2001 conference, Vancouver, November 2001.
Neelakantan, T., Brion, G.M., and Lingireddy, S., 2001, Neural network modeling of cryptosporidium and giardia concentrations in the Delaware River, Water Science and Technology, 43(12), 125–132.
Tain, Y-1, T. Kanade and J.F. Cohn. 2001. Recognizing Action Units for Facial Expression Analysis. Transactions on Pattern Analysis and Machine Intelligence. 23:2, 97–115.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Kluwer Academic Publishers
About this paper
Cite this paper
Shatkin, J.A., Qian, S. (2004). Classification Schemes for Priority Setting and Decision Making. In: Linkov, I., Ramadan, A.B. (eds) Comparative Risk Assessment and Environmental Decision Making. Nato Science Series: IV: Earth and Environmental Sciences, vol 38. Springer, Dordrecht. https://doi.org/10.1007/1-4020-2243-3_13
Download citation
DOI: https://doi.org/10.1007/1-4020-2243-3_13
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-1895-4
Online ISBN: 978-1-4020-2243-2
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)