Soil & Water Res., 2018, 13(1):11-17 | DOI: 10.17221/139/2016-SWR

Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)Original Paper

Marzieh MOKARRAM1, Mahdi NAJAFI-GHIRI1, Abdol Rassoul ZAREI2*
1 Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran
2 Department of Range and Watershed Management, College of Agriculture, Fasa University, Fasa, Iran

Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.

Keywords: artificial neural network (ANN); component layers; power function; self-organizing maps (SOM); learning an algorithm

Published: March 31, 2018  Show citation

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MOKARRAM M, NAJAFI-GHIRI M, ZAREI AR. Using self-organizing maps for determination of soil fertility (case study: Shiraz plain). Soil & Water Res. 2018;13(1):11-17. doi: 10.17221/139/2016-SWR.
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    References

    1. Bijanzadeh E., Mokarram M. (2016): The self-organizing map for determination of main features related to biological yield and yield of wheat. Australian Journal of Crop Science, 10: 539. Go to original source...
    2. Buza A., McDonald J.A., Michalak J., Stuetzle W. (1991): Interactive data visualization using focusing and linking. In: Proc. IEEE Conf. Visualization, San Diego, Oct 22-25, 1991: 156-163. Go to original source...
    3. Buza A., McDonald J.A., Michalak J., Stuetzle W. (2014): Use of morphometric analysis and self-organizing maps for alluvial fan classification: case study on Ostorankooh altitudes, Iran. In: 1st Conf. Visualization, Teheran, Apr 22-23, 2014: 156-163.
    4. Dahnke W.C. (1988): Recommended cation tests and measures of cation exchange capacity. In: Recommended Chemical Soil Test Procedures for the North Central Region. North Central Regional Research Publication No. 221: 15-16.
    5. Dhubkarya D.C., Nagariya D., Kapoor R. (2010): Implementation of a radial basis function using VHDL. Global Journal of Computer Science and Technology, 10: 16-19.
    6. Dragomir O.E., Dragomir F., Radulescu M. (2014): Matlab application of Kohonen self- organizing map to classify consumers' load profiles. Procedia Computer Science, 31: 474-479. Go to original source...
    7. Ferentinou M., Sakellariou M. (2007): Computational intelligence tools for the prediction of slope performance. Computers and Geotechnics, 34: 362-384. Go to original source...
    8. Ferentinou M., Hasiotis T., Sakellariou M. (2010): Clustering of geotechnical properties of marine sediments through self-organizing maps: An example from the Zakynthos Canyon valley system, Greece. Advances in Natural and Technological Hazards Research, 28: 43-54. Go to original source...
    9. Fytilis N., Rizzo D.M. (2013): Coupling self-organizing maps with a Naïve Bayesian classifier: Stream classification studies using multiple assessment data. Water Resources Research, 11: 7747-7762. Go to original source...
    10. Klobucar D., Subasic M. (2012): Using self-organizing maps in the visualization and analysis of forest inventory. iForest Biogeosciences and Forestry, 5: 216-223. Go to original source...
    11. Kohonen T. (1990). The self-organizing map. Proceedings of the IEEE, 78: 1464-1480. Go to original source...
    12. Kohonen T. (1995): Self organizing maps. Antiquity, 70: 588-611. Go to original source...
    13. Lee K.C., Cho H.R., Kim J.S. (2007): A self-organizing feature map driven approach to fuzzy approximate reasoning. Expert Systems with Applications, 33: 509-521. Go to original source...
    14. Lindsay W.L., Norvell W.A. (1978): Development of a DTPA soil test for zinc, iron, manganese and copper. Soil Science Society of American Journal, 42: 969-974. Go to original source...
    15. Mangiameli P., Chen S.K., West D. (1996): A comparison of SOM neural network and hierarchical clustering methods. European Journal of Operational Research, 6: 402-417. Go to original source...
    16. Merdun H. (2011): Self-organizing map artificial neural network application in multidimensional soil data analysis. Neural Computing and Applications, 20: 1295-1303. Go to original source...
    17. Mokarram M., Seif A., Sathyamoorthy D. (2014): Use of morphometric analysis and self-organizing maps for alluvial fan classification: case study on Ostorankooh altitudes, Iran. In: IOP Conference Series: Earth and Environmental Science, Tehran, Apr 22-23, 2014: 45. Go to original source...
    18. Olawoyin R., Nieto A., Grayson R.L., Hardisty F., Oyewole S. (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: 3634-3648. Go to original source...
    19. Rivera D., Sandoval M., Godoy A. (2015): Exploring soil databases: a self-organizing map approach. Soil Use and Management, 31: 121-131. Go to original source...
    20. Rumelhart D.E., McClelland J.L. (1986): Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MIT Press. Go to original source...
    21. Ultsch A., Simeon H.P. (1989): Exploratory Data Analysis Using Kohonen Networks on Transporters. Dortmund, Department of Computer Science, University of Dortmund.
    22. Van Hulle M.M. (2012): Self-organizing maps. In: Rozenberg G., Bäck T., Kok J.N. (eds): Handbook of Natural Computing, Berlin, Springer: 585-622. Go to original source...
    23. Venna J., Kaski S. (2001): Neighborhood preservation in nonlinear projection methods: An experimental study. Lecture Notes in Computer Science, 21: 485-491. Go to original source...
    24. Vesanto J., Himberg J., Alhoniemi E., Parhankangas J. (1999): Self-organizing map in Matlab: the SOM Toolbox. In: Proc. Matlab DSP Conf., Espoo, Nov 16-17, 1999: 35-40.
    25. Wang Z.-B., Chen Y.-F., Chen Y.-H. (2009): Functional grouping and establishment of distribution patterns of invasive plants in china using self-organizing maps and indicator species analysis. Archives of Biological Sciences, 1: 71-78. Go to original source...

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