An extension of the Bluecat approach and software to allow uncertainty assessment for predictions given by any environmental (multi)model has been released in September 2024.
The extension is presented in a submitted paper, titled "Uncertainty estimation for environmental multimodel predictions: the BLUECAT approach and software". Preprint is available here.
A new version of the Bluecat software has been released in the R and Python environments. Such new version stands alone, namely, it is not associated to a given deterministic model, thus allowing its association and application to any environmental model selected by the user.
Click here to access the R version of the software in GitHub.
Click here to access the Python version of the software in GitHub.
To allow application to multimodel prediction, we introduce a measure of uncertainty as model selection criterion to be used at each prediction step. We proposed four alternatives for such measure. Accordingly, at each prediction step uncertainty of each model is measured and the prediction - along with confidence limits - of the least uncertain model is picked up. See Figure below.
Bluecat model selection procedure.
Please refer to the Bluecat Home Page for additional details.
Thank you for your interest!
Ciao,
Alberto
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