Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

被引:4
|
作者
Nie, Shunqi [1 ]
Chen, Honghua [1 ]
Sun, Xinxin [1 ]
An, Yunce [1 ]
机构
[1] Nanjing Forestry Univ, Sch Civil Engn, Nanjing 210037, Peoples R China
基金
中国国家自然科学基金;
关键词
soil heavy metals; random forest; spatial interpolation; spatial distribution prediction; POLLUTION; ACCUMULATION; SURFACE; HEALTH; RISK; AREA;
D O I
10.3390/su16114358
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Mastering the spatial distribution of soil heavy metal content and evaluating the pollution status of soil heavy metals is of great significance for ensuring agricultural production and protecting human health. This study used a machine learning model to study the spatial distribution of soil heavy metal content in a coastal city in eastern China. Having obtained six soil heavy metal contents, including Cr, Cd, Pb, As, Hg, and Ni, environmental variables such as precipitation, soil moisture, and population density were selected. Random forest (RF) was used to model the spatial distribution of soil heavy metal content. The research findings indicate that the RF model demonstrates a robust predictive capability in discerning the spatial distribution of soil heavy metals, and environmental factor variables can explain 60%, 52.3%, 53.5%, 63.1%, 61.2%, and 51.2% of the heavy metal content of Cr, Cd, Pb, As, Hg, and Ni in soil, respectively. Among the chosen environmental variables, precipitation and population density exert notable influences on the predictive outcomes of the model. Specifically, precipitation exhibits the most substantial impact on Cr and Ni, whereas population density emerges as the primary determinant for Cd, Pb, As, and Hg. The RF prediction results show that Cr and Ni in the study area are less affected by human activities, while Cd, Pb, As, and Hg are more affected by human industrial and agricultural production. Research has shown that using RF models for predicting soil heavy metal distributions has certain significance.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] A Hadoop Performance Prediction Model Based on Random Forest
    Zhendong Bei
    Zhibin Yu
    Huiling Zhang
    Chengzhong Xu
    Shenzhong Feng
    Zhenjiang Dong
    Hengsheng Zhang
    ZTECommunications, 2013, 11 (02) : 38 - 44
  • [42] Spatial prediction of soil properties through hybridized random forest model and combination of reflectance spectroscopy and environmental covariates
    Shahabi, Aram
    Nabiollahi, Kamal
    Davari, Masoud
    Zeraatpisheh, Mojtaba
    Heung, Brandon
    Scholten, Thomas
    Taghizadeh-Mehrjardi, Ruhollah
    GEOCARTO INTERNATIONAL, 2022, 37 (27) : 18172 - 18195
  • [43] Prediction of respiratory diseases based on random forest model
    Yang, Xiaotong
    Li, Yi
    Liu, Lang
    Zang, Zengliang
    FRONTIERS IN PUBLIC HEALTH, 2025, 13
  • [44] A spatial distribution - Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil
    Liu, Jiawei
    Kang, Hou
    Tao, Wendong
    Li, Hanyu
    He, Dan
    Ma, Lixia
    Tang, Haojie
    Wu, Siqi
    Yang, Kexin
    Li, Xuxiang
    SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 859
  • [45] Heavy Metals in Wheat Grown in Sewage Irrigation: A Distribution and Prediction Model
    Yu, Xiaoman
    Wang, Zuwei
    Lynn, Alexandra
    Cai, Jianchao
    Huangfu, Yanchong
    Geng, Yong
    Tang, Jiaxi
    Zeng, Xiangfeng
    POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2016, 25 (01): : 413 - 418
  • [46] A new method for spatial three-dimensional prediction of soil heavy metals contamination
    Shen, Fengbei
    Xu, Chengdong
    Wang, Jinfeng
    Hu, Maogui
    Guo, Guanlin
    Fang, Tingting
    Zhu, Xingbao
    Cao, Hongying
    Tao, Huan
    Hou, Yixuan
    CATENA, 2024, 235
  • [47] Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals
    Sergeev, A. P.
    Buevich, A. G.
    Baglaeva, E. M.
    Shichkin, A. V.
    CATENA, 2019, 174 : 425 - 435
  • [48] Uncertainty in the spatial prediction of soil texture Comparison of regression tree and Random Forest models
    Liess, Mareike
    Glaser, Bruno
    Huwe, Bernd
    GEODERMA, 2012, 170 : 70 - 79
  • [49] Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging
    Bei Zhang
    Yong Yang
    Scientific Reports, 7
  • [50] Spatiotemporal modeling and prediction of soil heavy metals based on spatiotemporal cokriging
    Zhang, Bei
    Yang, Yong
    SCIENTIFIC REPORTS, 2017, 7