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Hyperspectral reflectance-driven estimation of water quality parameters using hybrid random forest and support vector regression techniques.

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Title: Hyperspectral reflectance-driven estimation of water quality parameters using hybrid random forest and support vector regression techniques.
Authors: ElGharbawi, Tamer1, tgh@eng.suez.edu.eg, Kaloop, Mosbeh R.2,3,4, mosbeh@mans.edu.eg, Hu, Jong Wan2,3, jongp24@inu.ac.kr, Zarzoura, Fawzi4, fawzihamed@mans.edu.eg
Source: Environmental Earth Sciences; Jul2025, Vol. 84 Issue 13, p1-20, 20p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 186987716
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  Data: Hyperspectral reflectance-driven estimation of water quality parameters using hybrid random forest and support vector regression techniques.
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Earth+Sciences%22">Environmental Earth Sciences</searchLink>; Jul2025, Vol. 84 Issue 13, p1-20, 20p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=186987716
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      – Type: doi
        Value: 10.1007/s12665-025-12387-x
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      – Code: eng
        Text: English
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        PageCount: 20
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      – TitleFull: Hyperspectral reflectance-driven estimation of water quality parameters using hybrid random forest and support vector regression techniques.
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            NameFull: ElGharbawi, Tamer
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            NameFull: Kaloop, Mosbeh R.
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            NameFull: Hu, Jong Wan
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              Text: Jul2025
              Type: published
              Y: 2025
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              Value: 84
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              Value: 13
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