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<p>Bayesian belief networks BBNs effectively model uncertainty in interconnected systems, though their construction and parameterisation present significant methodological challenges. This study introduces a Published Scientific Literature (PSL) review as a novel, evidence‑synthesis‑driven method for constructing a transparent and traceable Bayesian Belief Network structure in data‑poor environmental domains. My method helps capture this resource’s knowledge systematically, resulting in the construction of a BBN structure and data curation. I amplified a systematic review, a mapping process of identifying linkages between variables and constructing the network, and its evaluation with domain experts. The goal is to base the BBN on credible data sources supported by traceable evidence to identify key variables with interdependencies. I identified and coded narrative themes (as parameters) and subthemes (as parametric states) and extracted data frequency (as cases for parametrization) during a systematic review (SR). For illustration, I built a prototype BBN structure and parametrised the model using a case file originating through SR, synthesised evidence for tillage adaptation as a natural flood management strategy for sustainable farming and flood alleviation. This approach introduces the PSL review technique with a data curation framework as a sound foundation for model developers to build a basic BBN model for multi-disciplinary exploration.</p>

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Bayesian network model development: evidence synthesis using published scientific literature review for tillage adaptation as a natural flood management strategy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Qaisar Ali

摘要

Bayesian belief networks BBNs effectively model uncertainty in interconnected systems, though their construction and parameterisation present significant methodological challenges. This study introduces a Published Scientific Literature (PSL) review as a novel, evidence‑synthesis‑driven method for constructing a transparent and traceable Bayesian Belief Network structure in data‑poor environmental domains. My method helps capture this resource’s knowledge systematically, resulting in the construction of a BBN structure and data curation. I amplified a systematic review, a mapping process of identifying linkages between variables and constructing the network, and its evaluation with domain experts. The goal is to base the BBN on credible data sources supported by traceable evidence to identify key variables with interdependencies. I identified and coded narrative themes (as parameters) and subthemes (as parametric states) and extracted data frequency (as cases for parametrization) during a systematic review (SR). For illustration, I built a prototype BBN structure and parametrised the model using a case file originating through SR, synthesised evidence for tillage adaptation as a natural flood management strategy for sustainable farming and flood alleviation. This approach introduces the PSL review technique with a data curation framework as a sound foundation for model developers to build a basic BBN model for multi-disciplinary exploration.