A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model

Yin, Geping; Chen, Xingling; Zhu, Hanghai; Chen, Zhiliang; Su, Chuanghong; He, Zechen; Qiu, Jinrong; Wang, Tieyu

doi:10.1016/j.scitotenv.2022.153948

Cited by 34 publications

(6 citation statements)

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“…ArcGIS 10.7 software (ESRI, Redlands, CA, USA) was applied to create the spatial distributions of heavy metals and health risk properties using an universal kriging method 2,15 . All the statistical and correlation analyses were conducted using SPSS 22.0 (SPSS Inc., Chicago, IL, USA).…”

Section: Discussionmentioning

confidence: 99%

“…With the rapid development of China's economy, the accumulation of soil heavy metals, including Cu, Co, Zn, Cd, As, Pb, Cr and Hg, due to the advancement of agricultural intensification, industrialization and urbanization has caused serious ecological and environmental problems, posing a grievous threat to sustainable economic development and human public health [1][2][3][4] . In regard to the toxicity, nonbiodegradability, persistence and bioaccumulation of heavy metals, the concentrations, distribution, source apportionment, and potential ecological risks of soil heavy metals have attracted notable attention worldwide 2,[5][6][7] . Previous studies indicated that more than 50% of soils in agricultural and industrial land areas worldwide have been polluted with heavy metals [8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

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Spatial Distribution, Risk Assessment and Sources of Heavy Metals in Roadside Soils Exposed to the Zhengzhou-Kaifeng Intercity Railway in Huanghuai Plain, China

Duan,

Peng,

Liu

et al. 2024

Soil and Sediment Contamination: An International Journal

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Heavy metal accumulation in soil can seriously harm human health, and it is necessary to identify the accumulation status and access the potential risks for local pollution control and sustainable economic development. This study evaluated the pollution level, spatial distribution, potential risk and sources of soil heavy metals including As, Co, Cr, Cu, Zn, Pb, Ni, and Cd along the Zhengzhou-Kaifeng intercity railway and compared pollution characteristics in north side soils with south side soils of the railway. A total of 260 soil samples were collected from a section along the railway, and the average concentrations of As,

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Distribution, Risk Assessment and Sources of Heavy Metals in Roadside Soils Exposed to the Zhengzhou-Kaifeng Intercity Railway in Huanghuai Plain, China

Duan,

Peng,

Liu

et al. 2024

Soil and Sediment Contamination: An International Journal

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“…Over the past decades, kriging algorithms have been widely used to perform statistical inference of heavy-metal concentration data at unsampled locations. − The prototype of kriging algorithms originates from ordinary kriging, which is also the most frequently used for the risk assessment of soil pollution. − For example, Xie et al used ordinary kriging to map the spatial distribution of soil heavy metals with a high prediction accuracy for average concentrations. Since pollution data are often highly positively skewed, ordinary kriging is not always optimal. , Some more suitable algorithms, such as logarithmic kriging, universal kriging, and indicator kriging, have been subsequently proposed. − Li and Heap evaluated the performance of different kriging algorithms through comparative studies and found that data variation was a dominant impact factor.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Soil Sampling with Information Entropy at Heavy-Metal Sites

et al. 2023

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Knowledge of the spatial distribution of heavy metals is indispensable for successful risk analysis of contaminated sites. The common practice is to obtain soil samples for spatial interpolation through site investigation, which generally involves preliminary and detailed surveys. In this study, we propose an information entropy-based site investigation (IESI) method in which an optimal design step is implemented to guide soil sampling at the detailed survey stage. Two types of information entropy (i.e., relative entropy and Shannon entropy) are used to design the optimal sampling strategy. The results show that, within the IESI method, relative entropy is superior to Shannon entropy in guiding soil sampling. Combined with ordinary kriging, the IESI method outperforms conventional surveys for hypothetical and actual heavy metal-contaminated sites as it can identify new polluted and clean areas. For quantitative comparisons, the IESI method coupled with ordinary kriging, logarithmic ordinary kriging, and universal kriging with linear and quadratic trends can improve the interpolation accuracy by 16–43% at the actual heavy metal-contaminated site. Upon further examination of the IESI method, informative sampling points are mainly distributed around the polluted areas identified by the preliminary survey with soil pollution probabilities between 0.75 and 0.95. This work provides an effective tool for delineating the spatial distribution and valuable insights into identifying encryption areas at heavy-metal sites.

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“…In selecting the characteristic spectral wavelengths of vis-NIR spectra, the competitive adaptive reweighted sampling (CARS) (Liu et al, 2021), genetic algorithm (GA) (Chen et al, 2022;Yin et al, 2022), successive projection algorithm (SPA) (Mesquita et al, 2018), and uninformative variable elimination (Song et al, 2020) methods have been more widely used. The CARS algorithm has been shown to be able to select the optimal combination of spectral variables from full wavelength data to reveal the relationship between spectral reflectance and soil properties (Xing et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Estimation of soil organic matter in the Ogan-Kuqa River Oasis, Northwest China, based on visible and near-infrared spectroscopy and machine learning

Zhou

Ding

et al. 2023

J. Arid Land

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Visible and near-infrared (vis-NIR) spectroscopy technique allows for fast and efficient determination of soil organic matter (SOM). However, a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information. Therefore, this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM.The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017. Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test. The successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths. Finally, partial least squares regression (PLSR) and random forest (RF) models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content. The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features (i.e., 1400.0, 1900.0, and 2200.0 nm), and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0-510.0 nm. Both models can achieve a more satisfactory prediction of the SOM content, and the RF model had better accuracy than the PLSR model. The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination (R 2 ) of 0.78 and the residual prediction deviation (RPD) of 2.38. The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content. Therefore, combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.

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A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model

Cited by 34 publications

References 50 publications

Spatial Distribution, Risk Assessment and Sources of Heavy Metals in Roadside Soils Exposed to the Zhengzhou-Kaifeng Intercity Railway in Huanghuai Plain, China

Spatial Distribution, Risk Assessment and Sources of Heavy Metals in Roadside Soils Exposed to the Zhengzhou-Kaifeng Intercity Railway in Huanghuai Plain, China

Optimizing Soil Sampling with Information Entropy at Heavy-Metal Sites

Estimation of soil organic matter in the Ogan-Kuqa River Oasis, Northwest China, based on visible and near-infrared spectroscopy and machine learning

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