| 基于多源辅助变量和随机森林模型的耕地土壤重金属含量空间分布预测 |
| 摘要点击 758 全文点击 207 投稿时间：2023-03-04 修订日期：2023-04-04 |
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| 中文关键词 土壤重金属 环境变量 随机森林 空间预测 影响因子 |
| 英文关键词 soil heavy metals environmental variables random forest spatial prediction influencing factors |
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| 中文摘要 |
| 农田土壤重金属含量的空间预测对于监测耕地污染和确保生态农业可持续发展至关重要.从地形、气候、土壤属性、遥感信息、植被指数和人为活动这6个方面选取了32个环境变量作为辅助变量,并构建随机森林（RF）、回归克里格（RK）、普通克里格（OK）和多元线性回归（MLR）模型来预测耕地土壤中As、Cd、Cr、Cu、Hg、Ni、Pb和Zn的含量.结果表明,与RK、OK和MLR相比,RF模型对As、Cd、Cr、Hg、Pb和Zn的预测性能更高,而OK和RK模型分别对Cu和Ni含量的预测精度更高,表现为预测拟合优度（R2）最高而平均绝对误差（MAE）和均方根误差（RMSE）最低.不同预测方法对同种土壤重金属元素预测结果的空间分布趋势基本一致,8种重金属含量的高值区均分布在南部的平原地区,但RF模型对空间预测的细节刻画得更为突出.随机森林影响因子重要性排序表明,兰溪市土壤重金属含量空间分异主要受Se、TN、pH、海拔、年均温、年均降雨量、距河流距离和距工厂距离的共同影响.因此,随机森林可以作为土壤重金属空间预测的一种有效方法,为区域土壤污染调查、评价和管控提供科学参考. |
| 英文摘要 |
| Spatial prediction of the concentrations of soil heavy metals (HMs) in cultivated land is critical for monitoring cultivated land contamination and ensuring sustainable eco-agriculture. In this study, 32 environmental variables from terrain, climate, soil attributes, remote-sensing information, vegetation indices, and anthropogenic activities were used as auxiliary variables, and random forest (RF), regression Kriging (RK), ordinary Kriging (OK), and multiple linear regression (MLR) models were proposed to predict the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in cultivated soils. In comparison to those of RK, OK, and MLR, the RF model had the best prediction performance for As, Cd, Cr, Hg, Pb, and Zn, whereas the OK and RK models had highest prediction performance for Cu and Ni, respectively, showing that R2 was the highest, and mean absolute error (MAE) and root mean square error (RMSE) were the lowest. The prediction performance of the spatial distribution of soil HMs under different prediction methods was basically consistent. The high value areas of eight HMs concentrations were all distributed in the southern plain area. However, the RF model depicted the details of spatial prediction more prominently. Moreover, the importance ranking of influencing factors derived from the RF model indicated that the spatial variation in concentrations of the eight HMs in Lanxi City were mainly affected by the combined effects of Se, TN, pH, elevation, annual average temperature, annual average rainfall, distance from rivers, and distance from factories. Given the above, random forest models could be used as an effective method for the spatial prediction of soil heavy metals, providing scientific reference for regional soil pollution investigation, assessment, and management. |
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