A feature selection-based hybrid model for accurate spatial prediction of potentially toxic elements in sediments: Application to Poyang Lake, China
文献类型：期刊论文
| 作者 | Guo, Guanghui1,3; Anjiang, Meiduo1,2,3; Zhang, Ruiqing2; Wei, Chaoyang1,3; Lei, Mei1,3 |
| 刊名 | JOURNAL OF HAZARDOUS MATERIALS
 |
| 出版日期 | 2025-10-15 |
| 卷号 | 498页码:140008 |
| 关键词 | Machine learning Spatial distribution prediction Environmental predictors Sediments Potentially toxic elements |
| ISSN号 | 0304-3894 |
| DOI | 10.1016/j.jhazmat.2025.140008 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurately predicting spatial distribution of potentially toxic elements (PTEs) in sediments is crucial for protecting aquatic ecosystem but remains challenging due to complex interactions of environmental variables. This study developed an integrated framework by combining optimal machine learning (ML) with ordinary kriging (OK) and feature selection to improve prediction accuracy of PTEs in Poyang Lake sediments. Three ML models-random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)-were evaluated to identify the optimal approach for predicting PTE concentrations. Feature selection techniques including relative importance analysis and recursive feature elimination were applied to identify suitable predictors for each PTE. RF model outperformed the others across all PTEs (R-2>0.70). Distinct sets of predictors were identified for each PTE, further refining RFOK model. Incorporating selected predictors, RFOK significantly enhanced prediction accuracy, increasing R-2 by 37.5 % (Cr) to 421 % (Cd) relative to OK, and by 133 % (Cr) to 457 % (Pb) relative to inverse distance weighting, effectively capturing fine-scale spatial variability. The findings highlight the effectiveness of the RFOK hybridization and the importance of feature selection in enhancing prediction accuracy within complex multifactor aquatic environments, providing scientific supports for designing targeted protection strategies against PTE pollution in aquatic ecosystem. |
| URL标识 | 查看原文 |
| WOS关键词 | HEAVY-METALS ; RISK-ASSESSMENT ; MACHINE ; POLLUTION ; WATER |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001589242200009 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217509]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Guo, Guanghui |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 2.Inner Mongolia Univ, Sch Ecol & Environm, Hohhot 010021, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Guo, Guanghui,Anjiang, Meiduo,Zhang, Ruiqing,et al. A feature selection-based hybrid model for accurate spatial prediction of potentially toxic elements in sediments: Application to Poyang Lake, China[J]. JOURNAL OF HAZARDOUS MATERIALS,2025,498:140008. |
| APA | Guo, Guanghui,Anjiang, Meiduo,Zhang, Ruiqing,Wei, Chaoyang,&Lei, Mei.(2025).A feature selection-based hybrid model for accurate spatial prediction of potentially toxic elements in sediments: Application to Poyang Lake, China.JOURNAL OF HAZARDOUS MATERIALS,498,140008. |
| MLA | Guo, Guanghui,et al."A feature selection-based hybrid model for accurate spatial prediction of potentially toxic elements in sediments: Application to Poyang Lake, China".JOURNAL OF HAZARDOUS MATERIALS 498(2025):140008. |
入库方式： OAI收割
来源：地理科学与资源研究所
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