Artificial neural network prediction models of heavy metal polluted soil resistivity-Reference-Cited by-同舟云学术

Artificial neural network prediction models of heavy metal polluted soil resistivity

Published:2019-04-05 Issue:9 Volume:25 Page:1570-1590
ISSN:1964-8189
Container-title:European Journal of Environmental and Civil Engineering
language:en
Short-container-title:European Journal of Environmental and Civil Engineering

Author:

Chu Ya¹²,Liu Songyu¹,Cai Guojun¹,Bian Hanliang³

Affiliation:

1. Institute of Geotechnical Engineering, Jiangsu Key Laboratory of Urban Underground Engineering and Environmental Safety, Southeast University, Nanjing, Jiangsu, China

2. Department of Civil Architectural and Environment Engineering, Missouri University of Science and Technology, Rolla, MO, USA

3. Department of Civil and Architectural Engineering, Henan University, Kaifeng, China

Funder

the Key Project of Natural Science Foundation of China

the "Twelfth Five-Year" National Science and Technology Support Plan

Publisher

Informa UK Limited

Subject

Civil and Structural Engineering,Environmental Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/19648189.2019.1585962

Reference51 articles.

1. Laboratory Studies of the Flow of Some Organic Solvents and Their Aqueous Solutions Through Bentonite and Kaolin Clays

2. Electrical Resistivity of Compacted Clays

3. Using complex permittivity and artificial neural networks to identify and classify copper, zinc, and lead contamination in soil

4. Electrical Dispersion in Relation to Soil Structure

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