The Korean Journal of Ecology

The Ecological Society of Korea (한국생태학회)
권호 표지

Pattern Recognition of Long-term Ecological Data in Community Changes by Using Artificial Neural Networks: Benthic Macroinvertebrates and Chironomids in a Polluted Stream

  • Published : 2000.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

On community data. sampled in regular intervals on a long-term basis. artificial neural networks were implemented to extract information on characterizing patterns of community changes. The Adaptive Resonance Theory and Kohonen Network were both utilized in learning benthic macroinvertebrate communities in the Soktae Stream of the Suyong River collected monthly for three years. Initially, by regarding each monthly collection as a separate sample unit, communities were grouped into similar patterns after training with the networks. Subsequently, changes in communities in a sequence of samplings (e.g., two-month, four-month, etc.) were given as input to the networks. After training, it was possible to recognize new data set in line with the sampling procedure. Through the comparative study on benthic macroinvertebrates with these learning processes, patterns of community changes in chironomids diverged while those of the total benthic macro-invertebrates tended to be more stable.

Keywords

References

  1. The benthos of lakes Brinkhurst,R.O.
  2. Applied Optics v.26 ART2:self-organization of stable category recognition codes for analog input patterns Carpenter,G.A.;S.Grossberg
  3. Ecological Modelling v.90 Patternizing communities by using an artificial neural network Chon,T.S.;Y.S.Park;K.H.Moon;E.Y.Cha
  4. Ecological Modelling Temporal patternization of community dynamics by using unsupervised learning algorithms in artificial neural networks Chon,T.S.;Y.S.Park;J.H.Park
  5. Annual Review of Ecology and Systematics v.10 Feeding ecology of stream invertebrates Cummins,K.W.;M.J.Klug
  6. Transactions of the ASAW v.37 Neural network models for predicting flowering and physiological maturity of soybean Elizondo,D.A.;R.W.McClendon;G.Hoongenboom
  7. IEEE Transactions on Neural Networks v.5 Dynamic recurrent neural networks: theory and applications Giles,C.L.;G.M.Kuhn;R.J.Williams
  8. Internationale Revue der Gesamten Hydrobiologie v.68 Production and ecology of benthic chironomid larvae (Diptera) in Lake Hayes, New Zealand, a warm-monomictic eutrophic lake Graham,A.A.;C.W.Burns
  9. Neurocompouting Hecht-Nielsen,R.
  10. Biological indicators of freshwater pollution and environmental management Hellawell,J.M.
  11. Ecological Modelling v.97 Use of statistical and neural network techniques to detect how stomatal conductance reponds to changes in the local environment Huntingford,C.;P.M.Cox
  12. Limnology and Oceanography v.35 Growth and voltinism of lotic midge larvae: patterns across an Appalachian mountain basin Huryn,A.D.
  13. Korean J. Ecology v.18 Monthly changes in benthic macroinvertebrate communities streams of the Suyong River Kang,D.H.;T.S.Chon;Y.S.Park
  14. Self-organization and associative memory Kohonen,T.
  15. Digital Neural Networks Kung,S.Y.
  16. Developments in numercial ecology Constrained clustering Legendre,P.;P.Legendre(ed.);L.Legendre(ed.)
  17. Amer. Natur. v.125 Succession of species within a community: chronological clustering, with applications to marine and freshwater zooplankton Legendre,P.;S.Dallot;L.Legendre
  18. Developments in numercial ecology Legendre,P;L.Legendre
  19. Ecological Modeling v.90 Application of neural networks to modelling nonlinear relationships in ecology Lek,S.;M.Delacoste;P.Baran;I.Dimopoulos;J.Lauga;S.Aulagnier
  20. Fresenius J. Anal. Chem. v.344 Comparing the performance of neural networks to well-established methods of multivariate data analysis: the clssification of mass spectral data Lohninger,H.;F.Stancl
  21. Chemom. Intell. Lab. Syst. v.18 Two-dimensional mapping of IR spectra using a parallel implemented self-organising feature map Melssen,W.J.;J.R.M.Smits;G.H.Rolf;G,Kateman
  22. Adaptive pattern recognition and neural networks Pao,Y.H.
  23. J. Econ. Entomol. v.84 Spatial and temporal changes in aphid (Homoptera: Aphididae) species assemblages collected with suction traps in Idaho. Quinn,M.A.;S.E.Halbert;L.Williams Ⅲ.
  24. Eur. J. Clin. Chem. Clin. Biochem. v.31 Self-organizing neural networks as a means of cluster analysis in clinical chemistry Reibnegger,G.;G.Weiss;H.Wachter
  25. Statistical Ecology: A primer of methods and computing Rudwig,J.A.;J.F.Reynolds
  26. Holarctic Ecology v.2 Chironomid communities as water quality indicators Saether,O.A.
  27. Biological indicatiors of water quality The continental system for the assessment of river water quality Sladecek,V.;James,A.(ed.);L.Evison(ed.)
  28. Entomologica Scandinavica Supplement v.29 Influence of temperature and food quality on the life history of an epiphytic chironomid Storey,A.W.
  29. Ecological Modelling v.84 Predicting grassland community changes with an artificial neural network model Tan,S.S.;F.E.Smeins
  30. Ecological Modelling v.85 A comparison of fuzzy expert systems, neural network and neuro-fuzzy approaches controlling energy and material flows Tuma,A.;H.D.Haasis;O.Rentz
  31. Ecology v.73 Complex dynamics in ecological time series Turchin,P.;A.D.Taylor
  32. Neural computing: theory and practice Wasserman,P.D.
  33. Chemistry and Industry The biological systems of stream classification used by the Trent River Board Woodiwiss,F.S.
  34. Biol. Cyber. v.71 Calculation of the Volterra kernels of non-linear dynamic systems using an artificial neural network Wray,J.;G.G.R.Green
  35. Korean J. of Limnology v.32 Effects of pollution on communities of Chironomidae (Diptera) in the Soktae stream, a tributary of the Suyong river Youn,B.J.;T.S.Chon
  36. Introduction to artificial neural systems Zurada,J.M.