PyBanshee version (1.0): A Python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features

Koot, Paul; Mendoza-Lugo, Miguel Angel; Paprotny, Dominik; Morales-Nápoles, Oswaldo; Ragno, Elisa; Worm, D

doi:10.1016/j.softx.2022.101279

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…Figure 8 shows one test example containing a reduced crosscorrelation matrix (panel A) and a corresponding NPBN graph (panel B) indicating the interrelation between the concentrations of the keynote chemicals in the soil, the diversity of the soil microbial community and the local tree growth dynamics. To test the potential predictability, we employed Bayesian inference with a bootstrap-based validation scheme according to (Koot et al, 2023) with the upper layer of the graph (pH and keynote chemicals) in Figure 8B representing the input variables, while predicting the variables in the lower layer of the same graph. Figure 8C shows the results of the regression analysis between the observational and the predicted soil microbial diversity and tree ring width trend metrics, both characterized by correlation coefficients R > 0.8.…”

Section: Discussionmentioning

confidence: 99%

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The impact of Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation: a natural long-term in situ experiment in a planted pine forest

Bogachev,

Tishin,

Gafurov

et al. 2023

Front. Environ. Sci.

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Increased anthropogenic pressure including intensification of agricultural activities leads to long-term decline of natural biotopes, with planted forests often considered as promising compensatory response, although reduced biodiversity and ecosystem stability represent their common drawbacks. Here we present a complex investigation of the impact of a large Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation in a planted Scots pine forest representing a natural in situ experiment on an engineered ecosystem. After settling around 2006, the colony expanded for 15 years, leading to the intensive deposition of nutrients with feces, food remains and feather thereby considerably altering the local soil biogeochemistry. Thus, lower pH levels around 4.5, 10- and 2-fold higher concentrations of phosphorous and nitrogen, as well as 1.2-fold discrepancies in K, Li, Mn, Zn and Co., respectively, compared to the surrounding control forest area could be observed. Unaltered total organic carbon (Corg) suggests repressed vegetation, as also reflected in the vegetation indices obtained by remote sensing. Moreover, reduced soil microbial diversity with considerable alternations in the relative abundance of Proteobacteria, Firmicutes, Acidobacteriota, Actinobacteriota, Verrucomicrobiota, Gemmatimonadota, Chujaibacter, Rhodanobacter, and Bacillus has been detected. The above alterations to the ecosystem also affected climate stress resilience of the trees indicated by their limited recovery from the major 2010 drought stress, in marked contrast to the surrounding forest (p = 3∙10−5). The complex interplay between geographical, geochemical, microbiological and dendrological characteristics, as well as their manifestation in the vegetation indices is explicitly reflected in the Bayesian network model. Using the Bayesian inference approach, we have confirmed the predictability of biodiversity patterns and trees growth dynamics given the concentrations of keynote soil biogeochemical alternations with correlations R > 0.8 between observations and predictions, indicating the capability of risk assessment that could be further employed for an informed forest management.

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Section: Discussionmentioning

confidence: 99%

“…Bayesian network interaction model (Hanea et al, 2015), following a methodology similar to (Paprotny and Morales-Nápoles, 2017; Koot et al, 2023).…”

Section: Figurementioning

confidence: 99%

The impact of Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation: a natural long-term in situ experiment in a planted pine forest

Bogachev,

Tishin,

Gafurov

et al. 2023

Front. Environ. Sci.

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“…As stated in the previous section, Matlatzinca (v.1.0.0) was used to retrieve the conditional rank correlations. The software was developed by researchers of the TU Delft, The Netherlands, and is strongly based on PyBANSHEE (v.1.0), a Python-based open-source implementation of the MATLAB toolbox BANSHEE (v.1.3) [37][38][39]. Matlatzinca is used to schematize and quantify a dependence model, specifically the GCBN, and is accessible on https://github.com/grongen/Matlatzinca (accessed on 26 April 2023).…”

Section: Softwarementioning

confidence: 99%

Elicitation of Rank Correlations with Probabilities of Concordance: Method and Application to Building Management

Ramousse,

Mendoza-Lugo,

Rongen

et al. 2024

Entropy

Self Cite

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Constructing Bayesian networks (BN) for practical applications presents significant challenges, especially in domains with limited empirical data available. In such situations, field experts are often consulted to estimate the model’s parameters, for instance, rank correlations in Gaussian copula-based Bayesian networks (GCBN). Because there is no consensus on a ‘best’ approach for eliciting these correlations, this paper proposes a framework that uses probabilities of concordance for assessing dependence, and the dependence calibration score to aggregate experts’ judgments. To demonstrate the relevance of our approach, the latter is implemented to populate a GCBN intended to estimate the condition of air handling units’ components—a key challenge in building asset management. While the elicitation of concordance probabilities was well received by the questionnaire respondents, the analysis of the results reveals notable disparities in the experts’ ability to quantify uncertainty. Moreover, the application of the dependence calibration aggregation method was hindered by the absence of relevant seed variables, thus failing to evaluate the participants’ field expertise. All in all, while the authors do not recommend to use the current model in practice, this study suggests that concordance probabilities should be further explored as an alternative approach for the elicitation of dependence.

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“…NPBNs are well described by Hanea et al (2015). The NPBN in this research is implemented using the Python toolbox BANSHEE (Paprotny et al, 2020;Koot et al, 2023). NPBNs have been used in different fields of application such as hydrology (Paprotny and Morales Napoles, 2017) and flood risk (Paprotny et al, 2021;Couasnon et al, 2018).…”

Section: Literature Overviewmentioning

confidence: 99%

The Influence of Spatial Variation on the Design of Foundations of Immersed Tunnels: Advanced Probabilistic Analysis

't Hart,

Morales-Napoles,

Jonkman

2023

Preprint

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PyBanshee version (1.0): A Python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features

Cited by 11 publications

References 14 publications

The impact of Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation: a natural long-term in situ experiment in a planted pine forest

The impact of Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation: a natural long-term in situ experiment in a planted pine forest

Elicitation of Rank Correlations with Probabilities of Concordance: Method and Application to Building Management

The Influence of Spatial Variation on the Design of Foundations of Immersed Tunnels: Advanced Probabilistic Analysis

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