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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, 2017. "Validation approaches of an expert-based Bayesian Belief Network in Northern Ghana, West Africa," Ecological Modelling, Elsevier, vol. 365(C), pages 10-29.
    • Handle: RePEc:eee:ecomod:v:365:y:2017:i:c:p:10-29
    DOI: 10.1016/j.ecolmodel.2017.09.018
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    Citations

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    Cited by:

    1. Kabir, Golam & Balek, Ngandu Balekelayi Celestin & Tesfamariam, Solomon, 2018. "Consequence-based framework for buried infrastructure systems: A Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 290-301.
    2. Mrinal Kanti Sen & Subhrajit Dutta & Golam Kabir, 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    3. Afshin Ghahramani & John McLean Bennett & Aram Ali & Kathryn Reardon-Smith & Glenn Dale & Stirling D. Roberton & Steven Raine, 2021. "A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil," Sustainability, MDPI, vol. 13(20), pages 1-23, October.
    4. Hongmi Koo & Janina Kleemann & Christine Fürst, 2020. "Integrating Ecosystem Services into Land-Use Modeling to Assess the Effects of Future Land-Use Strategies in Northern Ghana," Land, MDPI, vol. 9(10), pages 1-24, October.
    5. Rongchen Zhu & Xiaofeng Hu & Xin Li & Han Ye & Nan Jia, 2020. "Modeling and Risk Analysis of Chemical Terrorist Attacks: A Bayesian Network Method," IJERPH, MDPI, vol. 17(6), pages 1-23, March.

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