Using machine learning to predict selenium and cadmium contents in rice grains from black shale-distributed farmland area-Reference-Cited by-同舟云学术

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Using machine learning to predict selenium and cadmium contents in rice grains from black shale-distributed farmland area

Published:2024-02 Issue: Volume:912 Page:168802
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Guo Rucan,Ren Rui,Wang Lingxiao,Zhi Qian,Yu Tao,Hou Qingye,Yang Zhongfang

Funder

China Geological Survey

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference62 articles.

1. Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR);Abrougui;Soil Tillage Res.,2019

2. Artificial neural network for stress-strain behavior of sandy soils: knowledge based verification;Banimahd;Comput. Geotech.,2005

3. Selenium geochemistry of paddy soils in Yangtze River Delta;Cao;Environ. Int.,2001

4. Estimating the soil respiration under different land uses using artificial neural network and linear regression models;Ebrahimi;Catena,2019

5. Influence of black shales on soils and edible plants in the Ankang area, Shaanxi Province, P. R. of China;Fang;Environ. Geochem. Health,2002

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1. Improved mapping of heavy metals in agricultural soils using machine learning augmented with spatial regionalization indices;Journal of Hazardous Materials;2024-10

2. Exogenous selenium promotes cadmium reduction and selenium enrichment in rice: Evidence, mechanisms, and perspectives;Journal of Hazardous Materials;2024-09

3. Effects and mechanisms of different exogenous organic matters on selenium and cadmium uptake by rice in natural selenium-cadmium-rich soil;Heliyon;2024-09

4. Prediction of maize copper content in copper-poor area based on machine learning;2024-08-27

5. Uptake and Transport of Selenium in a Soil–Tea Plant–Tea Infusion System: A Study of Typical Tea Plantations in a Selenium-Rich Area of China;Forests;2024-05-24

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