A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model
To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil...
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| Published in | The Science of the total environment Vol. 825; p. 153948 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
15.06.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0048-9697 1879-1026 1879-1026 |
| DOI | 10.1016/j.scitotenv.2022.153948 |
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| Abstract | To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R2) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs.
[Display omitted]
•A novel interpolation model was developed to predict soil HMs in a provincial scale.•Combining genetic algorithm and neural network model enhanced prediction accuracy.•GANN interpolation method performed advantages to 3 dominant traditional methods.•The optimized method showed lower root mean square error values and data anomalies. |
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| AbstractList | To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R²) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs. To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R ) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs. To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R2) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs. [Display omitted] •A novel interpolation model was developed to predict soil HMs in a provincial scale.•Combining genetic algorithm and neural network model enhanced prediction accuracy.•GANN interpolation method performed advantages to 3 dominant traditional methods.•The optimized method showed lower root mean square error values and data anomalies. To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R2) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs.To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R2) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs. |
| ArticleNumber | 153948 |
| Author | Chen, Zhiliang Chen, Xingling Su, Chuanghong Zhu, Hanghai Yin, Guangcai Qiu, Jinrong He, Zechen Wang, Tieyu |
| Author_xml | – sequence: 1 givenname: Guangcai surname: Yin fullname: Yin, Guangcai organization: Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China – sequence: 2 givenname: Xingling surname: Chen fullname: Chen, Xingling organization: Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China – sequence: 3 givenname: Hanghai surname: Zhu fullname: Zhu, Hanghai organization: Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China – sequence: 4 givenname: Zhiliang surname: Chen fullname: Chen, Zhiliang organization: Research center for eco-environment restoration technology, South China Institute of Environmental Science, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510006, China – sequence: 5 givenname: Chuanghong surname: Su fullname: Su, Chuanghong organization: Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China – sequence: 6 givenname: Zechen surname: He fullname: He, Zechen organization: Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China – sequence: 7 givenname: Jinrong surname: Qiu fullname: Qiu, Jinrong organization: Research center for eco-environment restoration technology, South China Institute of Environmental Science, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510006, China – sequence: 8 givenname: Tieyu surname: Wang fullname: Wang, Tieyu email: wangt@stu.edu.cn organization: Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35219652$$D View this record in MEDLINE/PubMed |
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| Keywords | Interpolation Soil heavy metals Genetic algorithm Neural network model |
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| SubjectTerms | algorithms China environment Environmental Monitoring - methods Genetic algorithm Interpolation kriging Metals, Heavy - analysis Neural network model neural networks Neural Networks, Computer prediction Risk Assessment Soil Soil heavy metals Soil Pollutants - analysis Spatial Analysis |
| Title | A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model |
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