| A novel four-dimensional prediction model of soil heavy metal pollution: Geographical explanations beyond artificial intelligence "black box" | |
Wang, Qi1; Li, Cangbai2; Hao, DM(郝冬梅)3 ; Xu, Yafei3; Shi, Xuewen3; Liu, Tongxu1; Sun, Weimin1; Zheng, Zelong4; Liu, Jianfeng5; Li, Wanqi3; Liu, Wengang3; Zheng, Jiaxue6; Li, Fangbai1 | |
| 2023-09-15 | |
| Online publication date | 2023-06 |
| Source Publication | JOURNAL OF HAZARDOUS MATERIALS Impact Factor & Quartile |
| ISSN | 0304-3894 |
| Volume | 458 |
| page numbers | 11 |
| Abstract | The current artificial intelligence (AI)-based prediction approaches of soil pollutants are inadequate in estimating the geospatial source-sink processes and striking a balance between the interpretability and accuracy, resulting in poor spatial extrapolation and generalization. In this study, we developed and tested a geographically interpretable four-dimensional AI prediction model for soil heavy metal (Cd) contents (4DGISHM) in Shaoguan city of China from 2016 to 2030. The 4DGISHM approach characterized spatio-temporal changes in source-sink processes of soil Cd by estimating spatio-temporal patterns and the effects of drivers and their interactions of soil Cd at local to regional scales using TreeExplainer-based SHAP and parallel ensemble AI algorithms. The results demonstrate that the prediction model achieved MSE and R2 values of 0.012 and 0.938, respectively, at a spatial resolution of 1 km. The predicted areas exceeding the risk control values for soil Cd across Shaoguan from 2022 to 2030 increased by 22.92% at the baseline scenario. By 2030, enterprise and transportation emissions (SHAP values 0.23 and 0.12 mg/kg, respectively) were the major drivers. The influence of driver interactions on soil Cd was marginal. Our approach surpasses the limitations of the AI "black box" by integrating spatio-temporal source-sink explanation and accuracy. This advancement enables geographically precise prediction and control of soil pollutants. © 2023 Elsevier B.V. |
| Keyword | Artificial intelligence Heavy metals Risk perception Soil pollution Soils 'current Black boxes Ensemble learning Geographical explanation Heavy metals pollution Prediction modelling Soil heavy metals Soil pollutant Source-sink TreeSHAP |
| Publisher | Elsevier B.V. |
| DOI | 10.1016/j.jhazmat.2023.131900 |
| Indexed By | EI ; SCIE |
| Language | 英语 |
| WOS Research Area | Engineering ; Environmental Sciences & Ecology |
| WOS Subject | Engineering, Environmental ; Environmental Sciences |
| WOS ID | WOS:001034972300001 |
| EI Accession Number | 20232714330009 |
| EI Keywords | Forecasting |
| EI Classification Number | 483.1 Soils and Soil Mechanics ; 531 Metallurgy and Metallography ; 723.4 Artificial Intelligence ; 914.1 Accidents and Accident Prevention |
| Original Document Type | Journal article (JA) |
| PMID | 37385097 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/532256 |
| Collection | 管理学院 |
| Corresponding Author | Li, Fangbai |
| Affiliation | 1.National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Science, Guangzhou; 510650, China; 2.Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou; 510006, China; 3.School of Management, Lanzhou University, Lanzhou; 730099, China; 4.Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou; 510180, China; 5.Research Institute of Forestry, Chinese Academy of Forestry, Beijing; 100091, China; 6.School of Data Science and Artificial Intelligence, Dongbei University of Finance & Economics, Dalian; 116025, China |
| Recommended Citation GB/T 7714 | Wang, Qi,Li, Cangbai,Hao, Dongmei,et al. A novel four-dimensional prediction model of soil heavy metal pollution: Geographical explanations beyond artificial intelligence "black box"[J]. JOURNAL OF HAZARDOUS MATERIALS,2023,458. |
| APA | Wang, Qi.,Li, Cangbai.,Hao, Dongmei.,Xu, Yafei.,Shi, Xuewen.,...&Li, Fangbai.(2023).A novel four-dimensional prediction model of soil heavy metal pollution: Geographical explanations beyond artificial intelligence "black box".JOURNAL OF HAZARDOUS MATERIALS,458. |
| MLA | Wang, Qi,et al."A novel four-dimensional prediction model of soil heavy metal pollution: Geographical explanations beyond artificial intelligence "black box"".JOURNAL OF HAZARDOUS MATERIALS 458(2023). |
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