Mapping of potentially toxic elements in the urban topsoil of St. Petersburg (Russia) using regression kriging and random forest algorithms-Reference-Cited by-同舟云学术

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Mapping of potentially toxic elements in the urban topsoil of St. Petersburg (Russia) using regression kriging and random forest algorithms

Published:2023-11-09 Issue:23 Volume:82 Page:
ISSN:1866-6280
Container-title:Environmental Earth Sciences
language:en
Short-container-title:Environ Earth Sci

Author:

Suleymanov Azamat,Polyakov Vyacheslav,Kozlov Alexander,Abakumov Evgeny,Kuzmenko Petr,Telyagissov Salavat

Funder

Ministry of Science and Higher Education of the Russian Federation

Publisher

Springer Science and Business Media LLC

Subject

Earth-Surface Processes,Geology,Pollution,Soil Science,Water Science and Technology,Environmental Chemistry,Global and Planetary Change

Link

https://link.springer.com/content/pdf/10.1007/s12665-023-11272-9.pdf

Reference51 articles.

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2. Agyeman PC, Kebonye NM, John K, Borůvka L, Vašát R, Fajemisim O (2022) Prediction of nickel concentration in peri-urban and urban soils using hybridized empirical Bayesian kriging and support vector machine regression. Sci Rep 12(1):3004. https://doi.org/10.1038/s41598-022-06843-y

3. Azizi K, Ayoubi S, Nabiollahi K, Garosi Y, Gislum R (2022) Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran. J Geochem Explor 233:106921. https://doi.org/10.1016/j.gexplo.2021.106921

4. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

5. Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in Central Iowa soils. Soil Sci Soc Am J 58(5):1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x

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