Prediction Model of Heavy Metal Pollution Distribution in Agricultural Soil from Network Perspective

Jing Xu, Tongke Fan

Ekoloji, 2019, Issue 108, Pages: 801-805

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Abstract

Aiming at the problem of low accuracy of traditional prediction model of heavy metal pollution distribution in soil, a prediction model of heavy metal pollution distribution in agricultural soil based on network perspective is proposed. From the perspective of network, the distribution of heavy metal pollution in agricultural soils is predicted and selected according to three prediction types: pollution state prediction, rapid deterioration prediction and slow deterioration prediction. A prediction model of heavy metal pollution distribution in agricultural soils is established, and the degree of heavy metal pollution in agricultural soils is analyzed by using the prediction model of heavy metal pollution distribution in agricultural soils. The experimental results show that the designed model has high prediction accuracy.

Keywords

network perspective, soil, heavy metal pollution, distribution

References

  • Bo EL, Zheng HD, Liu XG, et al. (2017) Evaluation of heavy metal pollution and risk assessment of dietary intake of pollutants in Chinese mitten crab (eriocheir sinensis) in main aquaculture areas of northeast china. Applied Ecology and Environmental Research 15(1):181-188.
  • Chen M, Lu W, Hou Z, et al. (2017) Heavy metal pollution in soil associated with a large-scale cyanidation gold mining region in southeast of Jilin, China. Environmental Science & Pollution Research 24(3):3084-3096.
  • Fan N, Cui YX, Peng Y, et al. (2018) Prediction and evaluation of heavy metal concentration in soil around coal-fired power plant based on BP neural network. Environmental Technology 31(2):52-56.
  • Jia Y, Li ZW, Wang DQ, et al. (2017) Optimum model of heavy metal pollution analysis in urban surface soil. Journal of Dalian University of Technology 57(2):202-206.
  • Praveena SM (2018) Spatial eco-risk assessment and prediction of heavy metal pollution in surface soil: a preliminary assessment of an urban area from a developing country. Toxin Reviews (1):1-8.
  • Santosfrancés F, Martinezgraña A, Alonso RP, et al. (2017) Geochemical background and baseline values determination and spatial distribution of heavy metal pollution in soils of the Andes mountain range (Cajamarca-Huancavelica, Peru). International Journal of Environmental Research & Public Health 14(8):859.
  • Shen Y, Zhao N, Xia M, et al. (2017) A deep q-learning network for ship stowage planning problem. Polish Maritime Research 24(SI):102-109.
  • Zhang J, Zhang X, Qiao Z, et al. (2019) Control system of intelligent monitoring and warning equipment for marine heavy metal pollution in port. Ekoloji 28(UNSP e107208107):1807-1813.