Abstract
This paper presents an associated visualization model for the nonlinear and multivariate ecological data prediction processes. Estimating impacts of changes in environmental conditions on biological entities is one of the required ecological data analyses. For the causality analysis, it is desirable to explain complex relationships between influential environmental data and responsive biological data through the process of ecological data predictions. The proposed Self-Organizing Map Tree utilizes Self-Organizing Maps as nodes of a tree to make association among different ecological domain data and to observe the prediction processes. Nonlinear data relationships and possible prediction outcomes are inspected through the processes of the SOMT that shows a good predictability of the target output for the given inputs.
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References
Aguilera, P.A., Frenich, A.G., Torres, J.A., Castro, H., Vidal, J.L.M., Canton, M.: Application of the kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality. Water Research 35, 4053–4062 (2001)
Chon, T.S., Park, Y.S., Moon, K.H., Cha, E.Y.: Patternizing communities by using an artificial neural network. Ecological Modelling 90, 69–78 (1996)
Compin, A., Cereghino, R.: Spatial patterns of macroinvertebrate functional feeding groups in streams in relation to physical variables and land-cover in southwestern france. Landscape Ecology 22, 1215–1225 (2007)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., New York (2001)
Giddings, E.M.P., Bell, A.H., Beaulieu, K.M., Cuffney, T.F., Coles, J.F., Brown, L.R., Fitzpatrick, F.A., Falcone, J., Sprague, L.A., Bryant, W.L., Peppler, M.C., Stephens, C., McMahon, G.: Selected physical, chemical, and biological data used to study urbanizing streams in nine metropolitan areas of the united states, 1999-2004. Technical Report Data Series 423, National Water-Quality Assessment Program, U.S. Geological Survey (2009)
Giraudel, J.L., Lek, S.: A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecological Modelling 146, 329–339 (2001)
Kalteh, A.M., Hjorth, P., Berndtsson, R.: Review of the self-organizing map (som) approach in water resources: Analysis, modelling and application. Environmental Modelling and Software 23, 835–845 (2008)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Information Sciences. Springer, Heidelberg (2001)
Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J.: Som-pak: The self-organizing map program package. Technical Report Version 3.1, SOM Programming Team, Helsinki University of Technology, Helsinki (1995)
Lek, S., Guegan, J.F.: Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling 120, 65–73 (1999)
Madzarov, G., Gjorgjevikj, D., Chorbev, I.: A multi-class svm classifier utilizing binary decision tree. In: Informatica, pp. 233–241 (2009)
Mele, P.M., Crowley, D.E.: Application of self-organizing maps for assessing soil biological quality. Agriculture, Ecosystems and Environment 126, 139–152 (2008)
Novotny, V., Virani, H., Manolakos, E.: Self organizing feature maps combined with ecological ordination techniques for effective watershed management. Technical Report 4, Center for Urban Environmental Studies, Northeastern University, Boston (2005)
Park, Y.S., Cereghino, R., Compin, A., Lek, S.: Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling 160, 265–280 (2003)
Sauvage, V.: The t-som (tree-som). In: Sattar, A. (ed.) Canadian AI 1997. LNCS, vol. 1342, pp. 389–397. Springer, Heidelberg (1997)
Tran, T.L., Knight, C.G., O’Neill, R.V., Smith, E.R., O’Connell, M.: Self-organizing maps for integrated environmental assessment of the mid-atlantic region. Environmental Management 31, 822–835 (2003)
Uriarte, E.A., Martin, F.D.: Topology preservation in som. International Journal of Mathematical and Computer Sciences 1(1), 19–22 (2005)
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Chung, Y., Takatsuka, M. (2011). The Self-Organizing Map Tree (SOMT) for Nonlinear Data Causality Prediction. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_16
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DOI: https://doi.org/10.1007/978-3-642-24958-7_16
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