Skip to main content
SpringerLink
Log in

Application of Artificial Intelligence to Risk Analysis for Forested Ecosystems

  • Chapter
Risk Analysis in Forest Management

Part of the book series: Managing Forest Ecosystems ((MAFE,volume 2))

Abstract

Forest ecosystems are subject to a variety of natural and anthropogenic disturbances that extract a penalty from human population values. Such value losses (undesirable effects) combined with their likelihoods of occurrence constitute risk. Assessment or prediction of risk for various events is an important aid to forest management. Artificial intelligence (AI) techniques have been applied to risk analysis owing to their ability to deal with uncertainty, vagueness, incomplete and inexact specifications, intuition, and qualitative information. This paper examines knowledge-based systems, fuzzy logic, artificial neural networks, and Bayesian belief networks and their application to risk analysis in the context of forested ecosystems. Disturbances covered are: fire, insects/diseases, meteorological, and anthropogenic. Insect/disease applications use knowledge-based system methods exclusively, whereas meteorological applications use only artificial neural networks. Bayesian belief network applications are almost nonexistent, even though they possess many theoretical and practical advantages. Embedded systems -that use AI alongside traditional methods-are, not unexpectedly, quite common.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literature Cited

  • Allman, W. F., 1989: Apprendices of wonder. Bantam, New York.

    Google Scholar 

  • Ball, B. J., 1997: Fuel moisture prediction in homogeneous fuels using GIS and neural networks. AI Applications 11 (3): 73–78.

    Google Scholar 

  • Bansal, A., Kauffman, R. J. and Weitz, R. R, 1993: Comparing the modeling performance of regression and neural networks as data quality varies: A business value approach. Journal of Management Information Systems 10(1): 11–32

    Google Scholar 

  • Barr, A. and Feigenbaum, E. A. (Eds.), 1982: The handbook of artificial intelligence. William Kaufmann, Inc., Los Altos CA

    Google Scholar 

  • Beer, T. and Ziolkowski, F., 1995: Environmental risk assessment: an Australian perspective. Commonwealth of Australia, Supervising Scientist Report 102 http://www. environment. gov.au/ssg/pubs/risk/risk_foreword. hurl.

    Google Scholar 

  • Christopherson, D., 1997: Artificial intelligence and weather forecasting: an update. AI Applications 11(1): 8193.

    Google Scholar 

  • Downing, K. and Bartos, D. L, 1991: AI methods in support of forest science: Modelling endemic level mountain pine beetle population dynamics. AI. 4pplications. 5 (2): 105–115.

    Google Scholar 

  • Frankel, D. S., Draper, J. S., Peak, J. E. and McLeod, J. C., 1995: Artificial intelligence needs workshop 4–5

    Google Scholar 

  • November 1993, Boston MA. Bulletin of the American Meteorological Society 76 (5): 728–738.

    Google Scholar 

  • Gardner, M. W. and Dorling, S. R, 1998: Artificial neural networks (the multilayer perceptron)- A review of applications in the atmospheric sciences. Applications in the Atmospheric Sciences 32 (14–15): 2627–2636.

    CAS  Google Scholar 

  • Hall, T., Brooks, H. E. and Doswell 111, C. A., 1999: Precipitation forecasting using a neural network. Weather and Forecasting 14 (3): 338–345.

    Article  Google Scholar 

  • Hebb, D. 0., 1949: Organization of Behavior. Science Editions, New York.

    Google Scholar 

  • Heckerman, D. and Shachter, R., 1995: Decision-theoretic foundations for causal reasoning. Journal of Artificial Intelligence Research 3: 405430.

    Google Scholar 

  • Hill, T., Marquez, L, O’Connor, M. and Remus, W., 1994: Artificial neural network models for forecasting and decision making. International Journal of Forecasting 10: 5–15.

    Article  Google Scholar 

  • Kourtz, P., 1987: Expert system dispatch of forest fire control resources. AI Applications 1(1): 1–8.

    Google Scholar 

  • Kuligowski, R. J. and Barros, A. P., 1998: Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather and Forecasting 13 (4): 1194–1204.

    Article  Google Scholar 

  • Lackey, R. T., 1997: If ecological risk assessment is the answer, what is the question? Human and Ecological Risk Assessment 5 (6): 921–928.

    Article  Google Scholar 

  • Lackey, R. T., 1999: Radically contested assertions in ecosystem management. Journal of Sustainable Forestry 9 (1/2): 21–34.

    Article  Google Scholar 

  • Lakshmanan, V. and Witt, A., 1997: A fuzzy logic approach to detecting severe updrafts. Al Applications 11 (1): 1–12.

    Google Scholar 

  • Lam, D. C. L, Swayne, D. A., Story, J. and Fraser, A. S., 1989: Watershed acidification models using the knowledge-based systems approach. Ecological Aodelling 47 (1/2): 131–152.

    Article  CAS  Google Scholar 

  • Lenihan, J. M., Daly, C., Bachelet, D. and Neilson, R P., 1998: Simulating broad-scale fire severity in a dynamic global vegetation model. Northwest Science 72: 91–103.

    Google Scholar 

  • Loh, D. K, Connor, M. D. and Janiga, P., 1991: Jack pine budwonn decision support system: A prototype. AI Applications 5 (4): 29–48.

    Google Scholar 

  • Luger, G. F. and Stubblefield, W. D., 1989: Artificial intelligence: structures and strategies for complex problem solving. Benjamin/Cmnnings Publishing Co., Redwood City CA.

    Google Scholar 

  • Marzban, C., Paik, H. and Stumpf, G. J., 1997: Neural networks vs. Gaussian discriminant analysis. AI Applications 11(1): 49–58.

    Google Scholar 

  • Marzban, C. and Stumpf, G. J., 1996: A neural network for tornado prediction based on Doppler radar-derived attributes. Journal ofAppliedMeteorology 35: 617–626.

    Google Scholar 

  • Marzban, C. and Stumpf, G. J., 1998: A neural network for damaging wind prediction. Weather and Forecasting 13 (1): 151–163.

    Article  Google Scholar 

  • Messing, R. H., Croft, B. A. and Currans, K, 1989: Assessing pesticide risk to arthropod natural enemies using expert system technology. Al Applications 3 (2): 1–12.

    Google Scholar 

  • Nunnari, G., Nucifora, A. F. M. and Randieri, C., 1998: The application of neural techniques to the modelling of time-series of atmospheric pollution data. Ecological Modelling 111: 187–205.

    Article  CAS  Google Scholar 

  • Olson, R. L, Wagner, T. L. and Willers, J. L. 1990: A framework for modeling uncertain reasoning in ecosystem management 2. Bayesian belief networks. 4lApplications 4 (4): 11–24.

    Google Scholar 

  • Pattie, D. C., 1992: Using neural networks to forecast recreation in wilderness areas. AI Applications 6 (2): 5759.

    Google Scholar 

  • Pearl, J., 1988: Probabilistic reasoning ìn intelligent systems: networks of plausible inference. Morgan Kauffman, San Mateo CA.

    Google Scholar 

  • Potter, W. D., Deng, X., Li, J., Xu, M., Wei, Y., Lappas, I., Twcry, M. J. and Bennett, D. J., 2000: A web-based expert system for gypsy moth risk assessment. Computers and Electronics in.-Igriculture 27 (1–3): 95–105.

    Article  Google Scholar 

  • Power, J. M. and Saarenmaa, H., 1995: Object-oriented modeling and GIS integration in a decision support system for the management of eastern hemlock looper in Newfoundland. Computers and Electronics in Agriculture 12 (1): 1–18.

    Article  Google Scholar 

  • Reynolds, K. M. and Holsten, E. H., 1994: Relative importance of risk factors for spnice beetle outbreaks. Canadian Journal of Forest Resources 24: 2089–2095.

    Article  Google Scholar 

  • Reynolds, K. M. and Holsten, E. H., 1996: Classification of spruce beetle hazard in Lutz and Sitka spruce stands on the Kenai Peninsula, Alaska. Forest Ecology and 21 fanagement 84: 251–262.

    Article  Google Scholar 

  • Ruisanchez, L, Potokar, P. and Zupan, J., 1996: Classification of energy dispersion x-ray spectra of mineralogical samples by artificial neural networks. Journal of Chemical Information and Computer Sciences 36 (2): 214–220.

    CAS  Google Scholar 

  • Rumelhart, D. E. and McClelland, J. L., 1986: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. The MIT Press, Cambridge MA.

    Google Scholar 

  • Rust, M., 1988: White pine blister rust hazard rating: An expert systems approach.41 Applications 2 (2–3): 4750.

    Google Scholar 

  • Salchenberger, L. M., Cinar, E. M. and Lash, N. A., 1992: Neural networks: A new tool for predicting thrift failures. Decision Sciences 23 (4): 899–916.

    Article  Google Scholar 

  • Schmoldt, D. L, 1987: Evaluation of an Expert System Approach to Forest Pest Management of Red Pine (Pinus resinosa). Ph.D. dissertation. University Microfilms International.

    Google Scholar 

  • Schmoldt, D. L, 1991: Simulation of plant physiological processes using fuzzy variables. AI Applications 5 (4): 3–16.

    Google Scholar 

  • Schmoldt, D. L. and Rauseher, H. M., 1996: Building Knowledge-Based.Systems for Natural Resource Management. Chapman Hall, New York.

    Book  Google Scholar 

  • Schmucker, K. J., 1984: Fuzzy sets, natural language computations, and risk analysis. Computer Science Press, Rockville MD.

    Google Scholar 

  • Shortliffe, E. H. and Buchanan, B. G., 1984: A model of inexact reasoning in medicine. In B. G. Buchanan and E. H. Shortliffe (eds.), Rule-based expert systems. Reading MA, Addison-Wesley, p. 232–262.

    Google Scholar 

  • Smith, C. G., 1985: Ancestral voices: language and the evolution of human consciousness. Prentice-Hall, Inc.,Englewood Cliffs NJ.

    Google Scholar 

  • Stock, M., 1996: Estimating the risk of escape of prescribed fires: an expert system approach. AI Applications 10 (2): 63–73.

    Google Scholar 

  • Tam, K. Y. and Kiang, M. Y., 1992: Managerial applications of neural networks: The case of bank failure predictions. Management Science 38 (7): 926–947.

    Article  Google Scholar 

  • Tomlins, K. L and Gray, C., 1994: Prediction of quality and origin of black tea and pine resin samples from chromatographic and sensory information: evaluation of neural networks. Food Chemistry 50: 157–165.

    Article  CAS  Google Scholar 

  • Varus, O., Kleve, B. and Kettunen, J., 1993: Evaluation of a real-time forecasting system for river water quality. Environmental Monitoring and Assessment 28(201–213):

    Google Scholar 

  • Vega-Garcia, C., Lee, B. S., Woodard, P. M. and Titus, S. J., 1996: Applying neural network technology to human-caused wildfire occurrence prediction. AI Applications 10 (3): 9–18.

    Google Scholar 

  • Wasserman, P. D., 1989: Neural computing: theory and practice. Van Nostrand Reinhold, New York. Waterman, D. A. and Hayes-Roth, F., 1978: An overview of pattern-directed inference systems. In D. A.

    Google Scholar 

  • Waterman and F. Hayes-Roth (eds.), Pattern-directed inference systems. New York, Academic, p. 251–269.

    Google Scholar 

  • Weiss, S. M. and Kulikowski, C. A., 1991: Computer Systems That Learn. Morgan Kaufmann Publishers, Inc., San Mateo, 223 pp.

    Google Scholar 

  • Zadeh, L. A., 1965: Fuzzy sets. Information and Control 8: 338–353.

    Article  Google Scholar 

  • Zadeh, L A., 1978: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1: 3–28.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. USDA Forest Service, Southern Research Station,Biological Systems Engineering Dept, University of Wisconsin, 460 Henry Mall, Madison, WI, 53706-1561, USA

    Daniel L. Schmoldt

Authors
  1. Daniel L. Schmoldt
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Georg-August-University, Göttingen, Germany

    Klaus von Gadow

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Schmoldt, D.L. (2001). Application of Artificial Intelligence to Risk Analysis for Forested Ecosystems. In: von Gadow, K. (eds) Risk Analysis in Forest Management. Managing Forest Ecosystems, vol 2. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-2905-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-94-017-2905-5_3

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-5683-2

  • Online ISBN: 978-94-017-2905-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation