Predicting total phosphorus levels as indicators for shallow lake management

The discusser thanks the authors for investigating the ability of modified random forest algorithm to predicting total phosphorus levels as indicators for shallow lake management. The abilities of machine learning techniques such as optimization algorithms today have been well documented in engineer...

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Vydáno v:	Ecological indicators Ročník 107; s. 105664
Hlavní autor:	Mohammadi, Babak
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.12.2019
Témata:	Algorithm’s parameters Data mining technique Random forest Variable selection Data mining technique Random forest Algorithm’s parameters Variable selection
ISSN:	1470-160X, 1872-7034
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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Shrnutí:	The discusser thanks the authors for investigating the ability of modified random forest algorithm to predicting total phosphorus levels as indicators for shallow lake management. The abilities of machine learning techniques such as optimization algorithms today have been well documented in engineering sciences. In this discussion, the discusser has tried to clarify the process of the paper of “Predicting total phosphorus levels as indicators for shallow lake management”(doi: https://doi.org/10.1016/j.ecolind.2018.09.002). The discusser would like to call attention to some important points, which may be taken into consideration by the authors and other potential researchers.
ISSN:	1470-160X 1872-7034
DOI:	10.1016/j.ecolind.2019.105664