Uncertainity Management Technique to Support Biological Modeling for Conservation of Priority Tree Species

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Addis Ababa University


Bayesian belief networks (BBNs) are useful tools for modeling biological predictions and aiding species conservation and managing uncertainty in decision-making. This paper provides practical indications for predicting, building, testing, and eliciting BBNs. Primary steps in this process include preparing data for experiment and predicting of the hypothesized “causal(dependency) relationship or conditional independence” of major biological factors affecting the target tree species or biological outcome of interest. A total of 1200 cases and 9 attributes were used for BN model prediction with 10-fold cross validation and building BBN model before elicitation process; and reinforcing the model after experts’ opinion; testing and visualizing the model with instance examples to see the conditional probabilities of the predictive inference thereby evaluating the final application model have been conducted respectively. To this end, the average prediction accuracy for the BN model is 75.76%, and this is a promising indication for the domain experts to make decision in their future endeavors. The paper also shows that the Bayesian network classifier has a potential to be used as a tool for prediction of biological modeling to forward about conservation actions in the field of forestry. In general, the whole research process can be a good input for further in-depth study and thus, making a good pragmatic analysis in the real world situations.



Bayesian belief networks, BBNs