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Reprint of “Validation approaches of an expert-based Bayesian Belief Network in northern Ghana, West Africa”

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  • Kleemann, Janina
  • Celio, Enrico
  • Fürst, Christine

Abstract

Model validation is a precondition for credibility and acceptance of a model. However, it appears that there is no scientific standard for validation of Bayesian Belief Networks (BBNs). In this paper, we present a novel combination of BBN validation approaches. A set of qualitative and quantitative validation approaches for the BBN structure, the Conditional Probability Tables and the BBN output is presented and discussed. The validation approaches were tested for a BBN on food provision under land use and land cover changes and different weather scenarios in rural northern Ghana. Experts played an important role in developing and validating the BBN due to data scarcity. Furthermore, selected nodes and the BBN output were compared to existing data. A sensitivity analysis was conducted. Validation approaches show that structural model uncertainties are still high and reliability of input data is low. However, the extreme-condition test shows that the BBN works according to the assumed system understanding that food provision decreases under floods, droughts, land pressure and poverty. Therefore, the BBN can provide general trends for output nodes but lacks reliability if detailed results of single system components are required.

Suggested Citation

  • Kleemann, Janina & Celio, Enrico & Fürst, Christine, 2018. "Reprint of “Validation approaches of an expert-based Bayesian Belief Network in northern Ghana, West Africa”," Ecological Modelling, Elsevier, vol. 371(C), pages 101-118.
    • Handle: RePEc:eee:ecomod:v:371:y:2018:i:c:p:101-118
    DOI: 10.1016/j.ecolmodel.2017.12.021
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    6. Barton, D.N. & Saloranta, T. & Moe, S.J. & Eggestad, H.O. & Kuikka, S., 2008. "Bayesian belief networks as a meta-modelling tool in integrated river basin management -- Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin," Ecological Economics, Elsevier, vol. 66(1), pages 91-104, May.
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