Use of random forest for assessing the effect of water quality parameters on the biological status of surface waters

DOI10.1007/S13137-023-00229-6zbMath1517.62086OpenAlexW4384928007WikidataQ122446269 ScholiaQ122446269MaRDI QIDQ6113805

Adrienne Clement, Orsolya Szomolányi

Publication date: 9 August 2023

Published in: GEM - International Journal on Geomathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13137-023-00229-6

zbMATH Keywords

random forestmultiple stressorsbiological quality elementsphysico-chemical parameterswater framework directive

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items (1)

Predicting the spatial distribution of stable isotopes in precipitation using a machine learning approach: a comparative assessment of random forest variants

Cites Work

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• Bagging predictors
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• Statistical Analysis of Financial Data in S-Plus
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• Predicting the spatial distribution of stable isotopes in precipitation using a machine learning approach: a comparative assessment of random forest variants

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