algalactivity2: algalactivity Bayesian Networks

In bnRep: A Repository of Bayesian Networks from the Academic Literature

algalactivity Bayesian Networks

Description

Influence of resampling techniques on Bayesian network performance in predicting increased algal activity.

Format

A discrete Bayesian network to to predict chlorophyll-a (chl-a) using a range of water quality parameters as predictors (Fig. 7 of the referenced paper). Probabilities were given within the referenced paper. The vertices are:

C

(0, 1);

Chl_a

(0, 1);

DO

(0, 1);

N

(0, 1);

P

(0, 1);

pH

(0, 1);

Te

(0, 1);

Tu

(0, 1);

Value

An object of class bn.fit. Refer to the documentation of bnlearn for details.

References

Rezaabad, M. Z., Lacey, H., Marshall, L., & Johnson, F. (2023). Influence of resampling techniques on Bayesian network performance in predicting increased algal activity. Water Research, 244, 120558.

bnRep documentation built on April 12, 2025, 1:13 a.m.