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A Comparative Assessment of the Influences of Human Impacts on Soil Cd Concentrations Based on Stepwise Linear Regression, Classification and Regression Tree, and Random Forest Models

文献类型： 外文期刊

作者： Qiu, Lefeng 1 ; Wang, Kai 2 ; Long, Wenli 3 ; Wang, Ke 4 ; Hu, Wei 1 ; Amable, Gabriel S. 5 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Rural Dev, Hangzhou, Zhejiang, Peoples R China

2.Ningbo Univ, Sch Marine Sci, Ningbo 315211, Zhejiang, Peoples R China

3.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou, Zhejiang, Peoples R China

4.Zhejiang Univ, Inst Remote Sensing & Informat Syst Applicat, Hangzhou 310003, Zhejiang, Peoples R China

5.Univ Cambridge, Dept Geog, Downing Pl, Cambridge CB2 3EN, England

期刊名称：PLOS ONE （ 影响因子：3.24； 五年影响因子：3.788 ）

ISSN： 1932-6203

年卷期： 2016 年 11 卷 3 期

页码：

收录情况： SCI

摘要： Soil cadmium (Cd) contamination has attracted a great deal of attention because of its detrimental effects on animals and humans. This study aimed to develop and compare the performances of stepwise linear regression (SLR), classification and regression tree (CART) and random forest (RF) models in the prediction and mapping of the spatial distribution of soil Cd and to identify likely sources of Cd accumulation in Fuyang County, eastern China. Soil Cd data from 276 topsoil (0-20 cm) samples were collected and randomly divided into calibration (222 samples) and validation datasets (54 samples). Auxiliary data, including detailed land use information, soil organic matter, soil pH, and topographic data, were incorporated into the models to simulate the soil Cd concentrations and further identify the main factors influencing soil Cd variation. The predictive models for soil Cd concentration exhibited acceptable overall accuracies (72.22% for SLR, 70.37% for CART, and 75.93% for RF). The SLR model exhibited the largest predicted deviation, with a mean error (ME) of 0.074 mg/kg, a mean absolute error (MAE) of 0.160 mg/kg, and a root mean squared error (RMSE) of 0.274 mg/kg, and the RF model produced the results closest to the observed values, with an ME of 0.002 mg/kg, an MAE of 0.132 mg/kg, and an RMSE of 0.198 mg/kg. The RF model also exhibited the greatest R-2 value (0.772). The CART model predictions closely followed, with ME, MAE, RMSE, and R-2 values of 0.013 mg/kg, 0.154 mg/kg, 0.230 mg/kg and 0.644, respectively. The three prediction maps generally exhibited similar and realistic spatial patterns of soil Cd contamination. The heavily Cd-affected areas were primarily located in the alluvial valley plain of the Fuchun River and its tributaries because of the dramatic industrialization and urbanization processes that have occurred there. The most important variable for explaining high levels of soil Cd accumulation was the presence of metal smelting industries. The good performance of the RF model was attributable to its ability to handle the non-linear and hierarchical relationships between soil Cd and environmental variables. These results confirm that the RF approach is promising for the prediction and spatial distribution mapping of soil Cd at the regional scale.

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