Ecology and Environmental Sciences ›› 2025, Vol. 34 ›› Issue (6): 950-960.DOI: 10.16258/j.cnki.1674-5906.2025.06.012
• Research Article [Environmental Science] • Previous Articles Next Articles
MENG Chang1,2(), HONG Mei1,2,*(
), LI Fei1,2,*(
)
Received:
2024-11-14
Online:
2025-06-18
Published:
2025-06-11
通讯作者:
* 李斐, E-mail: 作者简介:
孟畅(1997年生),女,硕士研究生,主要研究方向为农业遥感。E-mail: 2021202040012@emails.imau.edu.cn
基金资助:
CLC Number:
MENG Chang, HONG Mei, LI Fei. Collaborative Enhancement of Soil Heavy Metal Prediction Accuracy Using Hyperspectral Sensitive Band Selection and Machine Learning[J]. Ecology and Environmental Sciences, 2025, 34(6): 950-960.
孟畅, 红梅, 李斐. 高光谱敏感波段筛选与机器学习协同提升土壤重金属预测精度[J]. 生态环境学报, 2025, 34(6): 950-960.
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URL: https://www.jeesci.com/EN/10.16258/j.cnki.1674-5906.2025.06.012
类别 | 名称 | 缩略词 | 参考 |
---|---|---|---|
过滤法 | 相关分析 | CA | Liu et al., |
互信息系数 | MI | Zhou et al., | |
相关特征 | RELIEF | Li et al., | |
最大信息系数 | MIC | Liu et al., | |
最小冗余 | MRMR | Gu et al., | |
包裹法 | K选择 | SKB | Liu et al., |
可变迭代空间收缩法 | VISSA | Zhang et al., | |
连续投影算法 | SPA | Mei et al., | |
遗传算法 | GA | Rostami et al., | |
竞争性自适应重加权抽样 | CARS | Mei et al., | |
无信息变量消除 | UVE | Song et al., | |
嵌入法 | 随即森林重要度 | RFI | Yang et al., |
逐步多元线性回归 | SMLR | Liu et al., | |
偏最小二乘回归-VIP | PLSR-VIP | Yang et al., | |
岭回归 | RR | Malik et al., | |
套索回归 | LR | Tibshirani, |
Table 1 Band selection method
类别 | 名称 | 缩略词 | 参考 |
---|---|---|---|
过滤法 | 相关分析 | CA | Liu et al., |
互信息系数 | MI | Zhou et al., | |
相关特征 | RELIEF | Li et al., | |
最大信息系数 | MIC | Liu et al., | |
最小冗余 | MRMR | Gu et al., | |
包裹法 | K选择 | SKB | Liu et al., |
可变迭代空间收缩法 | VISSA | Zhang et al., | |
连续投影算法 | SPA | Mei et al., | |
遗传算法 | GA | Rostami et al., | |
竞争性自适应重加权抽样 | CARS | Mei et al., | |
无信息变量消除 | UVE | Song et al., | |
嵌入法 | 随即森林重要度 | RFI | Yang et al., |
逐步多元线性回归 | SMLR | Liu et al., | |
偏最小二乘回归-VIP | PLSR-VIP | Yang et al., | |
岭回归 | RR | Malik et al., | |
套索回归 | LR | Tibshirani, |
区域与元素 | 变异系数/% | 重金属质量分数/(mg∙kg−1) | ||||||
---|---|---|---|---|---|---|---|---|
最大值 | 最小值 | 平均值 | 偏斜率 | 内蒙古土壤背景值 | 中国土壤背景值 | |||
区域a | Cu | 1.39 | 1617.45 | 4.82 | 218.47 | 2.28 | 22.91 | 100.00 |
Zn | 1.34 | 354.66 | 24.81 | 32.37 | 1.72 | 48.60 | 300.00 | |
Cr | 1.29 | 193.08 | 12.61 | 19.97 | 1.20 | 68.20 | 350.00 | |
Pb | 1.39 | 559.92 | 1.75 | 64.92 | 2.11 | 34.20 | 250.00 | |
区域b | Cu | 0.75 | 432.00 | 3.77 | 126.68 | 1.33 | - | - |
Zn | 0.30 | 123.56 | 8.46 | 68.87 | 0.61 | - | - | |
Cr | 0.14 | 87.92 | 36.43 | 66.48 | 0.76 | - | - | |
Pb | 1.21 | 715.84 | 4.62 | 77.74 | 2.49 | - | - |
Table 2 Descriptive statistics of heavy metal mass fraction in the studied area
区域与元素 | 变异系数/% | 重金属质量分数/(mg∙kg−1) | ||||||
---|---|---|---|---|---|---|---|---|
最大值 | 最小值 | 平均值 | 偏斜率 | 内蒙古土壤背景值 | 中国土壤背景值 | |||
区域a | Cu | 1.39 | 1617.45 | 4.82 | 218.47 | 2.28 | 22.91 | 100.00 |
Zn | 1.34 | 354.66 | 24.81 | 32.37 | 1.72 | 48.60 | 300.00 | |
Cr | 1.29 | 193.08 | 12.61 | 19.97 | 1.20 | 68.20 | 350.00 | |
Pb | 1.39 | 559.92 | 1.75 | 64.92 | 2.11 | 34.20 | 250.00 | |
区域b | Cu | 0.75 | 432.00 | 3.77 | 126.68 | 1.33 | - | - |
Zn | 0.30 | 123.56 | 8.46 | 68.87 | 0.61 | - | - | |
Cr | 0.14 | 87.92 | 36.43 | 66.48 | 0.76 | - | - | |
Pb | 1.21 | 715.84 | 4.62 | 77.74 | 2.49 | - | - |
Figure 6 Independent verification results of the optimal sensitive band method combined with the GBDT model to estimate heavy metal concentration in soil
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