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A Bayesian approach to modeling lost person behaviors based on terrain features in Wilderness Search and Rescue

Author

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  • Lanny Lin

    (Brigham Young University)

  • Michael A. Goodrich

    (Brigham Young University)

Abstract

In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating efforts, but it often depends almost exclusively on the missing person profile, prior experience, and subjective judgment. We propose a Bayesian model that uses publicly available terrain features data to help model lost-person behaviors. This approach enables domain experts to encode uncertainty in their prior estimations and also makes it possible to incorporate human behavior data collected in the form of posterior distributions, which are used to build a first-order Markov transition matrix for generating a temporal, posterior predictive probability distribution map. The map can work as a base to be augmented by search and rescue workers to incorporate additional information. Using a Bayesian χ 2 test for goodness-of-fit, we show that the model fits a synthetic dataset well. This model also serves as a foundation for a larger framework that allows for easy expansion to incorporate additional factors such as season and weather conditions that affect the lost-person’s behaviors.

Suggested Citation

  • Lanny Lin & Michael A. Goodrich, 2010. "A Bayesian approach to modeling lost person behaviors based on terrain features in Wilderness Search and Rescue," Computational and Mathematical Organization Theory, Springer, vol. 16(3), pages 300-323, September.
  • Handle: RePEc:spr:comaot:v:16:y:2010:i:3:d:10.1007_s10588-010-9066-2
    DOI: 10.1007/s10588-010-9066-2
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    References listed on IDEAS

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    1. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. David Reitter & Christian Lebiere, 2010. "A cognitive model of spatial path-planning," Computational and Mathematical Organization Theory, Springer, vol. 16(3), pages 220-245, September.
    2. Frank E. Ritter & William G. Kennedy & Bradley J. Best, 2013. "The best papers from BRIMS 2011: models of users and teams interacting," Computational and Mathematical Organization Theory, Springer, vol. 19(3), pages 283-287, September.

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