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ARTIFICIAL NEURAL NETWORK AND KRIGING INTERPOLATION FOR THE CHEMICAL ELEMENTS CONTENTS IN THE SURFACE LAYER OF SOIL ON A BACKGROUND AREA

Sergeev, Alexander, Buevich, Alexander, Medvedev, Alexander, Subbotina, Irina, Sergeeva, Marina

First published: 2015https://doi.org/10.5593/sgem2015/b32/s13.007View metrics

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Title
ARTIFICIAL NEURAL NETWORK AND KRIGING INTERPOLATION FOR THE CHEMICAL ELEMENTS CONTENTS IN THE SURFACE LAYER OF SOIL ON A BACKGROUND AREA
Authors
Sergeev, Alexander, Buevich, Alexander, Medvedev, Alexander, Subbotina, Irina, Sergeeva, Marina
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 15th International Multidisciplinary Scientific GeoConference SGEM2015, WATER RESOURCES. FOREST, MARINE AND OCEAN ECOSYSTEMS
Publisher
Stef92 Technology
Year
2015
Pages
49-56
ISSN
1314-2704
ISBN
978-619-7105-37-7
Language
en
Publication type
Conference Paper
References28

  1. Sergeev A. P., Baglaeva E. M., Shichkin A. V., Case of soil surface chromium anomaly of a northern urban, Atmospheric Pollution Research 1, pp 44-49, 2010.

  2. Webster R., Oliver M., Geostatistics for Environmental Scientists, John Wiley& Sons, Chichester, pp UL–UL9, 2001.

  3. Liu X.M., Zhao K.L., Xu J.M., Zhan M.H., Si B., Wang F., Spatial variability of soil organic matter and nutrients in paddy fields at various scales in southeast China, Environ. Geol. 53, pp 1139–1147, 2008.

  4. Worsham L., Markewitz D., Nibbelink N., Incorporating spatial dependence into estimates of soil carbon contents under different land covers, Soil Sci. Soc.Am., J. 74, pp 635–646, 2010.

  5. Liu ZeLin; Peng Chang Hui; Xiang Wen Hua; et al., Application of artificial neural networks in global climate change and ecological research, Сhinese science bulletin, Vol. 55, pp. 3853-3863, 2010.

  6. Shaker R.; Tofan L.; Bucur M.; et al., Network modelling approach applied to dobrogea, Romania, Journal of environmental protection and ecology, v ol. 11, pp 337-348, 2010.

  7. Tracey, Jeff A.; Zhu, Jun; Crooks, Kevin R., Modeling and inference of animal movement using artificial neural networks, Environmental and ecological statistics,v ol. 18, pp 393-410, 2011.

  8. Watts, Michael J.; Worner S.P., Comparing ensemble and cascaded neural networks that combine biotic and abiotic variables to predict insect species distribution, Ecological informatics, vol. 3, pp 354-366, 2008.

  9. Sahoo G.B.; Schladow S.G.; Reuter, J. E., Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models, Journal of hydrology, v ol.378, pp 325-342, 2009. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org 15th International Multidisciplinary Scientific GeoConferences SGEM2015

  10. Helama S.; Makarenko N.G.; Karimova L.M.; et al., Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms, Annales geophysicae, v ol. 27, pp 1097-1111, 2009.

  11. Tosh, Colin R.; Ruxton, Graeme D., The need for stochastic replication of ecological neural networks, Philosophical transactions of the royal society b- biological sciences, v ol. 362, pp 455-460, 2007.

  12. Ozesmi S.L; Tan C.O; Ozesmi U, Methodological issues in building, training, and testing artificial neural networks in ecological applications, 3rd Conference of the International-Society-for-Ecological-Informatics (ISEI), Rome, Italy, v ol. 195, pp 83-93, 2006.

  13. Gevrey M; Dimopoulos I; Lek S, Two-way interaction of input variables in the sensitivity analysis of neural network models, 3rd Conference of the International- Society-for-Ecological-Informatics (ISEI), Rome, Italy, v ol. 195, pp 43-50, 2006.

  14. Fuqiang Dai, Qigang Zhou, Zhiqiang Lv, Xuemei Wang, Gangcai Liu, Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau, Ecological Indicators 45, pp 184–194, 2014. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org

  15. Sergeev A. P., Baglaeva E. M., Shichkin A. V., Case of soil surface chromium anomaly of a northern urban, Atmospheric Pollution Research 1, pp 44-49, 2010.

  16. Webster R., Oliver M., Geostatistics for Environmental Scientists, John Wiley& Sons, Chichester, pp UL–UL9, 2001.

  17. Liu X.M., Zhao K.L., Xu J.M., Zhan M.H., Si B., Wang F., Spatial variability of soil organic matter and nutrients in paddy fields at various scales in southeast China, Environ. Geol. 53, pp 1139–1147, 2008.

  18. Worsham L., Markewitz D., Nibbelink N., Incorporating spatial dependence into estimates of soil carbon contents under different land covers, Soil Sci. Soc.Am., J. 74, pp 635–646, 2010.

  19. Liu ZeLin; Peng Chang Hui; Xiang Wen Hua; et al., Application of artificial neural networks in global climate change and ecological research, Сhinese science bulletin, Vol. 55, pp. 3853-3863, 2010.

  20. Shaker R.; Tofan L.; Bucur M.; et al., Network modelling approach applied to dobrogea, Romania, Journal of environmental protection and ecology, v ol. 11, pp 337-348, 2010.

  21. Tracey, Jeff A.; Zhu, Jun; Crooks, Kevin R., Modeling and inference of animal movement using artificial neural networks, Environmental and ecological statistics,v ol. 18, pp 393-410, 2011.

  22. Watts, Michael J.; Worner S.P., Comparing ensemble and cascaded neural networks that combine biotic and abiotic variables to predict insect species distribution, Ecological informatics, vol. 3, pp 354-366, 2008.

  23. Sahoo G.B.; Schladow S.G.; Reuter, J. E., Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models, Journal of hydrology, v ol.378, pp 325-342, 2009. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org 15th International Multidisciplinary Scientific GeoConferences SGEM2015

  24. Helama S.; Makarenko N.G.; Karimova L.M.; et al., Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms, Annales geophysicae, v ol. 27, pp 1097-1111, 2009.

  25. Tosh, Colin R.; Ruxton, Graeme D., The need for stochastic replication of ecological neural networks, Philosophical transactions of the royal society b- biological sciences, v ol. 362, pp 455-460, 2007.

  26. Ozesmi S.L; Tan C.O; Ozesmi U, Methodological issues in building, training, and testing artificial neural networks in ecological applications, 3rd Conference of the International-Society-for-Ecological-Informatics (ISEI), Rome, Italy, v ol. 195, pp 83-93, 2006.

  27. Gevrey M; Dimopoulos I; Lek S, Two-way interaction of input variables in the sensitivity analysis of neural network models, 3rd Conference of the International- Society-for-Ecological-Informatics (ISEI), Rome, Italy, v ol. 195, pp 43-50, 2006.

  28. Fuqiang Dai, Qigang Zhou, Zhiqiang Lv, Xuemei Wang, Gangcai Liu, Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau, Ecological Indicators 45, pp 184–194, 2014. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org

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