EGU2020-1400
https://doi.org/10.5194/egusphere-egu2020-1400
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using Bayesian Network for Soil Organic Carbon Prediction despite its Incredible Complexity

Tabassom Sedighi1, Jacqueline Hannam2, and Ron Corstanje3
Tabassom Sedighi et al.
  • 1Cranfield University, School of Water, Energy and Environment, Centre for Environmental and Agricultural Information, United Kingdom of Great Britain and Northern Ireland (t.sedighi@cranfield.ac.uk)
  • 2Cranfield University, School of Water, Energy and Environment, Centre for Environmental and Agricultural Information, United Kingdom of Great Britain and Northern Ireland (j.a.hannam@cranfield.ac.uk)
  • 3Cranfield University, School of Water, Energy and Environment, Centre for Environmental and Agricultural Information, United Kingdom of Great Britain and Northern Ireland (roncorstanje@cranfield.ac.uk)

In this research, static Bayesian networks (BN) is presented for predicting Soil Organic Carbon (SOC) in the complex and open soil systems. BN is a graphical computational model which provides a simple technique to define the nonlinear dependencies and, therefore, to implement a compact representation of the complex systems. Moreover, the BN is used as a simulation tool for effective processing of the complex system outcomes by probability propagation methods. This permits evaluation and potential intervention in complex soil systems and determines the dependencies between different variables. We use a BN to identify key factors in predicting England and Wales SOC. Then, we explore the relationships between different key factors such as geographical, environmental or climate and their roles individually in predicting SOC, particularly to identify those which have the highest impact. The proposed BN is also used to calculate the effectiveness of these interventions where the uncertainties associated with these casual relationships at the same time. This approach works with data from the variety of sources and handles a mix of subjective and objective data and can incorporate variables which differ across the contexts. The effectiveness of the technique is demonstrated with a case study to predict SOC.

How to cite: Sedighi, T., Hannam, J., and Corstanje, R.: Using Bayesian Network for Soil Organic Carbon Prediction despite its Incredible Complexity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1400, https://doi.org/10.5194/egusphere-egu2020-1400, 2019