A machine learning and geostatistical hybrid method to improve spatial prediction accuracy of soil potentially toxic elements-Reference-Cited by-同舟云学术

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A machine learning and geostatistical hybrid method to improve spatial prediction accuracy of soil potentially toxic elements

Published:2022-09-04 Issue:2 Volume:37 Page:681-696
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Molla Abiot,Zhang Weiwei,Zuo Shudi,Ren Yin,Han Jigang

Funder

Scientific Research Foundation of Shanghai Landscaping & City Appearance Administrative Bureau

National Social Science Foundation of China

Ningbo Municipal Department of S&T

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-022-02284-1.pdf

Reference87 articles.

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2. Amari T, Ghnaya T, Abdelly C (2017) Nickel, cadmium and lead phytotoxicity and potential of halophytic plants in heavy metal extraction. South Afr J Bot 111:99–110. https://doi.org/10.1016/j.sajb.2017.03.011

3. Ballabio C, Panagos P, Lugato E et al (2018) Copper distribution in European topsoils: An assessment based on LUCAS soil survey. Sci Total Environ 636:282–298. https://doi.org/10.1016/j.scitotenv.2018.04.268

4. Behrens T, Zhu A, Schmidt K, Scholten T (2010) Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma 155:175–185. https://doi.org/10.1016/j.geoderma.2009.07.010

5. Bhagat SK, Tung TM, Yaseen ZM (2019) Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.119473

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