Chapter 8· Analysis of Stream Macroinvertebrate Communities
177
Park YS, Kwak IS, Cha EY, Lek S, Chon TS (2001a) Relational patterning on different
hierarchical levels in communities of benthic macroinvertebrates in an urbanized
stream using an artificial neural network. J. Asia-Pacific Entomol. (Submitted)
Park YS, Kwak IS, Chon TS, Kim JK, Jl'lrgensen SE (2001b) Implementation of artificial
neural networks in pauerning and prediction of exergy in response to temporal
dynamies ofbenthic macroinvertebrate communities in streams. Ecol. Model., 146. (In
Press)
Quinn MA, Halbert SE, Williarns III L (1991) Spatial and temporal changes in aphid
(Homoptera: Aphididae) species assemblages collected with suction traps in Idaho. J.
Econ. Entomol., 84,1710-1716
Recknagel F, French M, Harkonen P, Yabunaka KI (1997) Artificial neural network
approach for modelling and prediction of algal blooms. Eco. Model., 96, 11-28
Recknagel F, Wilson H (2000) Elucidation and prediction of aquatic ecosystems by
artificial neuronal networks, In: Lek, S. and Guegan, J.F. (Eds.), Artificial Neuronal
Networks: Application to Ecology and Evolution. Springer-Verlag, Berlin, pp. 143-155
Rumelhart DE, Hinton GE, Williarns RJ (1986) Learning internal representations by error
propagation. In: Rumelhart, D.E. and McCelland, J.L. (Eds.), Parallel distributed
processing: Explorations in the microstructure of cognition, Vol. I: Foundations, MIT
Press, Cambridge, pp. 318-362
Salvador R, Piool J, Tarantola S, Pla E (2001) Global sensitivity analysis and scale effects
of a fire propagation model used over Mediterranean shrublands. Ecol. Model., 136,
175-189
Scardi M (2000) Neuronal network models of phytoplankton primary production, In: Lek.
S. and Guegan, J.F. (Eds.), Artificial Neuronal Networks: Application to Ecology and
Evolution. Springer-Verlag, Berlin, pp. 116-129
Sladecek V (1979) Continental systems for the assessment of river water quality. In: James,
A. and Evison, L. (Eds.), Biological Indicators of Water Quality. John Wiley & Sons,
Chichester, pp. 3.1-3.32
SpeIlerberg IF (1991) Monitoring Ecological Change. Cambridge University Press, 334 pp
Stankovski V, Debeljak M, Bratko I, Adamic M (1998) Modelling the population dynamies
of red deer (Cervus elaphus L.) with regard to forest deve!opment. Ecol. Model., 108,
143-153
Tan SS, Smeins FE (1996) Predicting grassland comrnunity changes with an artificial
neural network model. Ecol. Model., 84, 91-97
Tittizer TI, Koth P (1979) Possibilities and limitations of biological methods of water
analysis. In: James, A. and Evison, L. (Eds.), Biological Indicators of Water Quality.
John Wiley and Sons, Chichester, Great Britain, pp. 4.1-4.21
Tuma A, Haasis HD, Rentz 0 (1996) A comparison of fuzzy expert systems, neural
networks and neuro-fuzzy approaches controlling energy and material flows. Ecol.
Model., 85, 93-98
Wasserman PD (1989) Neural computing: Theory and practice. Van Nostrand Reinhold,
New York
Welch EB, Linde! T (1992) Ecological effects of wastewater 'Applied limnology and
pollutant effects'. Chapman & Hall, 425 pp
Williarns RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent
neural networks. Neural Computation, 1, 270-280
Wray J, Green GGR (1994) Calculation of the Volterra kerneIs of non-linear dynamic
systems using an artificial neural network. Biol. Cybern., 71,187-195
177
Park YS, Kwak IS, Cha EY, Lek S, Chon TS (2001a) Relational patterning on different
hierarchical levels in communities of benthic macroinvertebrates in an urbanized
stream using an artificial neural network. J. Asia-Pacific Entomol. (Submitted)
Park YS, Kwak IS, Chon TS, Kim JK, Jl'lrgensen SE (2001b) Implementation of artificial
neural networks in pauerning and prediction of exergy in response to temporal
dynamies ofbenthic macroinvertebrate communities in streams. Ecol. Model., 146. (In
Press)
Quinn MA, Halbert SE, Williarns III L (1991) Spatial and temporal changes in aphid
(Homoptera: Aphididae) species assemblages collected with suction traps in Idaho. J.
Econ. Entomol., 84,1710-1716
Recknagel F, French M, Harkonen P, Yabunaka KI (1997) Artificial neural network
approach for modelling and prediction of algal blooms. Eco. Model., 96, 11-28
Recknagel F, Wilson H (2000) Elucidation and prediction of aquatic ecosystems by
artificial neuronal networks, In: Lek, S. and Guegan, J.F. (Eds.), Artificial Neuronal
Networks: Application to Ecology and Evolution. Springer-Verlag, Berlin, pp. 143-155
Rumelhart DE, Hinton GE, Williarns RJ (1986) Learning internal representations by error
propagation. In: Rumelhart, D.E. and McCelland, J.L. (Eds.), Parallel distributed
processing: Explorations in the microstructure of cognition, Vol. I: Foundations, MIT
Press, Cambridge, pp. 318-362
Salvador R, Piool J, Tarantola S, Pla E (2001) Global sensitivity analysis and scale effects
of a fire propagation model used over Mediterranean shrublands. Ecol. Model., 136,
175-189
Scardi M (2000) Neuronal network models of phytoplankton primary production, In: Lek.
S. and Guegan, J.F. (Eds.), Artificial Neuronal Networks: Application to Ecology and
Evolution. Springer-Verlag, Berlin, pp. 116-129
Sladecek V (1979) Continental systems for the assessment of river water quality. In: James,
A. and Evison, L. (Eds.), Biological Indicators of Water Quality. John Wiley & Sons,
Chichester, pp. 3.1-3.32
SpeIlerberg IF (1991) Monitoring Ecological Change. Cambridge University Press, 334 pp
Stankovski V, Debeljak M, Bratko I, Adamic M (1998) Modelling the population dynamies
of red deer (Cervus elaphus L.) with regard to forest deve!opment. Ecol. Model., 108,
143-153
Tan SS, Smeins FE (1996) Predicting grassland comrnunity changes with an artificial
neural network model. Ecol. Model., 84, 91-97
Tittizer TI, Koth P (1979) Possibilities and limitations of biological methods of water
analysis. In: James, A. and Evison, L. (Eds.), Biological Indicators of Water Quality.
John Wiley and Sons, Chichester, Great Britain, pp. 4.1-4.21
Tuma A, Haasis HD, Rentz 0 (1996) A comparison of fuzzy expert systems, neural
networks and neuro-fuzzy approaches controlling energy and material flows. Ecol.
Model., 85, 93-98
Wasserman PD (1989) Neural computing: Theory and practice. Van Nostrand Reinhold,
New York
Welch EB, Linde! T (1992) Ecological effects of wastewater 'Applied limnology and
pollutant effects'. Chapman & Hall, 425 pp
Williarns RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent
neural networks. Neural Computation, 1, 270-280
Wray J, Green GGR (1994) Calculation of the Volterra kerneIs of non-linear dynamic
systems using an artificial neural network. Biol. Cybern., 71,187-195