EGU2020-21348
https://doi.org/10.5194/egusphere-egu2020-21348
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Predict potential risk of bark beetle disturbance applying Bayesian Belief Networks

Meryem Tahri1, Jan Kašpar1, Harald Vacik2, and Robert Marušák1
Meryem Tahri et al.
  • 1Czech University of Life Sciences Prague (CZU), Faculty of Forestry and Wood Sciences , Forest management, Czechia (tahri@fld.czu.cz)
  • 2University of Natural Resources and Life Sciences Vienna - BOKU · Institute of Silviculture, Austria

In recent years, due to climate and environmental change, most forest areas suffer from land degradation, mainly caused by the bark beetle disturbance which damaged many tree species. The complex multiple interactions between climate and influence factors have highlighted the need for an efficiency integrated model based on decision support system, determining important implications and support for forest management planning. Based on expert perceptions, the Bayesian Belief Networks (BBN) approach provides a more consistent method of handling uncertainties, aiming to facilitate the interpretation of interdependencies between factors considered against risk. In this research, we have developed a BBN algorithm and program to estimate the potential risk in national scale, this technique was compared to the fuzzy logic model. Both models propose rapid solution for solving complex decision problems, they could be reused in other worldwide similar study areas.

How to cite: Tahri, M., Kašpar, J., Vacik, H., and Marušák, R.: Predict potential risk of bark beetle disturbance applying Bayesian Belief Networks, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21348, https://doi.org/10.5194/egusphere-egu2020-21348, 2020

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