Skip to main content
Log in

Exploring the database of a soil environmental survey using a geo-self-organizing map: A pilot study

  • Published:
Journal of Geographical Sciences Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

A model integrating geo-information and self-organizing map (SOM) for exploring the database of soil environmental surveys was established. The dataset of 5 heavy metals (As, Cd, Cr, Hg, and Pb) was built by the regular grid sampling in Hechi, Guangxi Zhuang Autonomous Region in southern China. Auxiliary datasets were collected throughout the study area to help interpret the potential causes of pollution. The main findings are as follows: (1) Soil samples of 5 elements exhibited strong variation and high skewness. High pollution risk existed in the case study area, especially Hg and Cd. (2) As and Pb had a similar topo-logical distribution pattern, meaning they behaved similarly in the soil environment. Cr had behaviours in soil different from those of the other 4 elements. (3) From the U-matrix of SOM networks, 3 levels of SEQ were identified, and 11 high risk areas of soil heavy metal-contaminated were found throughout the study area, which were basically near rivers, factories, and ore zones. (4) The variations of contamination index (CI) followed the trend of construction land (1.353) > forestland (1.267) > cropland (1.175) > grassland (1.056), which suggest that decision makers should focus more on the problem of soil pollution surrounding industrial and mining enterprises and farmland.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Alvarez-Guerra M, González-Piñuela C, Andrés A et al., 2008. Assessment of self-organizing map artificial neural networks for the classification of sediment quality. Environment International, 34 (6): 782–790.

    Article  Google Scholar 

  • Anagu I, Ingwersen J, Utermann J et al., 2009. Estimation of heavy metal sorption in German soils using artificial neural networks. Geoderma, 152(1/2): 104–112.

    Article  Google Scholar 

  • Astel A, Tsakovski S, Barbieri P et al., 2007. Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. Water Research, 41 (19): 4566–4578.

    Article  Google Scholar 

  • Bação F, Lobo V, Painho M., 2004. Geo-self-organizing map (Geo-SOM) for building and exploring homogeneous regions. In: Egenhofer M J, Freksa C, Miller H J. Third International Conference on GIScience. Berlin: Springer, 22–37.

    Google Scholar 

  • Buszewski B, Kowalkowski T., 2006. A new model of heavy metal transport in the soil using nonlinear artificial neural networks. Environmental Engineering Science, 23 (4): 589–595.

    Article  Google Scholar 

  • Cai L M, Huang L C, Zhou Y Z et al., 2010. Heavy metal concentrations of agricultural soils and vegetables from Dongguan, Guangdong. Journal of Geographical Sciences, 20 (1): 121–134.

    Article  Google Scholar 

  • Chang D H, Islam S, 2000. Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sensing of Environment, 74 (3): 534–544.

    Article  Google Scholar 

  • Cockx L, Van Meirvenne M, Verbeke L P C et al., 2009. Extracting topsoil information from EM38DD sensor data using a neural network approach. Soil Science Society of America Journal, 73 (6): 2051–2058.

    Article  Google Scholar 

  • Dotaniya M L, Meena V D, Rajendiran S et al., 2017. Geo-accumulation indices of heavy metals in soil and groundwater of Kanpur, India under long term irrigation of tannery effluent. Bulletin of Environmental Contamination and Toxicology, 98 (5): 706–711.

    Article  Google Scholar 

  • Gao B, Lu A, Pan Y et al., 2017. Additional sampling layout optimization method for environmental quality grade classifications of farmland soil. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (12): 5350–5358.

    Article  Google Scholar 

  • Guan Y, Shao C, Gu Q et al., 2016. Study of a comprehensive assessment method of the environmental quality of soil in industrial and mining gathering areas. Stochastic Environmental Research and Risk Assessment, 30 (1): 91–102.

    Article  Google Scholar 

  • Huang K X, Qin L M, Wu S Z et al., 2012. Situation and remedial measures for heavy metals pollution in Hechi city of Guangxi. Journal of Guangxi Academy of Sciences, 28 (4): 320–324. (in Chinese)

    Google Scholar 

  • Huang Y, Ye H, Zhang L et al., 2017. Prediction of soil organic matter using ordinary kriging combined with the clustering of self-organizing map: A case study in Pinggu District, Beijing, China. Soil Science, 182 (2): 52–62.

    Article  Google Scholar 

  • Jaffar S T A, Luo F, Ye R et al., 2017. The extent of heavy metal pollution and their potential health risk in top-soils of the massively urbanized district of Shanghai. Archives of Environmental Contamination and Toxicology, 73 (3): 362–376.

    Article  Google Scholar 

  • Kohonen T, 1982. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43 (1): 59–69.

    Article  Google Scholar 

  • Kohonen T, Somervuo P, 2002. How to make large self-organizing maps for nonvectorial data. Neural Networks, 15(8/9): 945–952.

    Article  Google Scholar 

  • Kong X T, 2014. China must protect high-quality arable land. Nature, 506(7486): 7.

    Article  Google Scholar 

  • Li X, Gao B, Pan Y et al., 2016. The soil heavy metal content mapping based on Sandwich model. In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Piscataway: IEEE, 1–6.

    Google Scholar 

  • Li Y, Li C K, Tao J J et al., 2011. Study on spatial distribution of soil heavy metals in Huizhou City based on BP-ANN modeling and GIS. Procedia Environmental Sciences, 10. 1953–1960.

    Article  Google Scholar 

  • Muleta M K, Nicklow J W, 2005. Decision support for watershed management using evolutionary algorithms. Journal of Water Resources Planning and Management, 131 (1): 35–44.

    Article  Google Scholar 

  • Nadal M, Schuhmacher M, Domingo J W, 2004. Metal pollution of soils and vegetation in an area with petrochemical industry. Science of the Total Environment, 321(1–3): 59–69.

    Article  Google Scholar 

  • Nemerow N., 1974. Scientific Stream Pollution Analysis. New York: McGraw-Hill.

    Google Scholar 

  • Olawoyin R, Nieto A, Grayson R L et al., 2013. Application of artificial neural network (ANN)–self-organizing map (SOM) for the categorization of water, soil and sediment quality in petrochemical regions. Expert Systems with Applications, 40 (9): 3634–3648.

    Article  Google Scholar 

  • Pan Y, Li H, 2016. Investigating heavy metal pollution in mining brownfield and its policy implications: A case study of the Bayan Obo rare earth mine, Inner Mongolia, China. Environmental Management, 57 (4): 879–893.

    Article  Google Scholar 

  • Patel R M, Prasher S O, God P K et al., 2002. Soil salinity prediction using artificial neural networks. Journal of the American Water Resources Association, 38 (1): 91–100.

    Article  Google Scholar 

  • Rivera D, Sandoval M, Godoy A, 2015. Exploring soil databases: A self-organizing map approach. Soil Use and Management, 31 (1): 121–131.

    Article  Google Scholar 

  • Sakizadeh M, Mirzaei R, Ghorbani H, 2017. Support vector machine and artificial neural network to model soil pollution: A case study in Semnan Province, Iran. Neural Computing and Applications, 28 (11): 3229–3238.

    Article  Google Scholar 

  • Somaratne S, Seneviratne G, Coomaraswamy U, 2005. Prediction of soil organic carbon across different land-use patterns. Soil Science Society of America Journal, 69 (5): 1580–1589.

    Article  Google Scholar 

  • Tóth G, Hermann T, Da Silva M R et al., 2016. Heavy metals in agricultural soils of the European Union with implications for food safety. Environment International, 88. 299–309.

    Article  Google Scholar 

  • Vesanto J, 2002. Data exploration process based on the self-organizing map [D]. Helsinki: Helsinki University of Technology.

    Google Scholar 

  • Wang J F, Haining R, Liu T J et al., 2013. Sandwich estimation for multi-unit reporting on a stratified heterogeneous surface. Environment and Planning A, 45 (10): 2515–2534.

    Article  Google Scholar 

  • Wang J F, Zhang T L, Fu B J, 2016. A measure of spatial stratified heterogeneity. Ecological Indicators, 67. 250–256.

    Article  Google Scholar 

  • Wang Y B, Liu C W, Wang S W, 2015. Characterization of heavy-metal-contaminated sediment by using unsuper-vised multivariate techniques and health risk assessment. Ecotoxicology and Environmental Safety, 113. 469–476.

    Article  Google Scholar 

  • Wu Y., 2015. Characteristics of soil heavy metal contamination around industrial and mining enterprises in Diao-jiang river basin, Guangxi Zhuang Autonomous Region, China [D]. Beijing: University of Chinese Academy of Sciences. (in Chinese)

    Google Scholar 

  • Yang C, Guo R, Wu Z et al., 2014. Spatial extraction model for soil environmental quality of anomalous areas in a geographic scale. Environmental Science and Pollution Research, 21 (4): 2697–2705.

    Article  Google Scholar 

  • Zhou P, Zhao Y, Zhao Z et al., 2015. Source mapping and determining of soil contamination by heavy metals using statistical analysis, artificial neural network, and adaptive genetic algorithm. Journal of Environmental Chemical Engineering, 3 (4): 2569–2579.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaoyong Liao.

Additional information

Foundation: Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDA19040302; The Key Research Program of the Chinese Academy of Sciences, No.KFZD-SW-111

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, X., Tao, H., Gong, X. et al. Exploring the database of a soil environmental survey using a geo-self-organizing map: A pilot study. J. Geogr. Sci. 29, 1610–1624 (2019). https://doi.org/10.1007/s11442-019-1644-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11442-019-1644-8

Keywords

Navigation