Chapter 12 . Pattern Analysis of Endemie Fish Speeies in Rivers
245
Garson GD (1991) Interpreting neural network connection weights. Artificial Intelligence
Expert 6, 47-51
Geman S, Bienenstock E, Doursat R (1992) Neural Network and the biaslvariance dilemna.
Neural Comput. 4, 1-58
Goh ATC (1995) Back-propagation neural networks for modelling complex systems.
Artificial Intelligence Engineering 9, 143-151
Guegan JF, Lek S, Oberdorff T (1998) Energy availability and habitat heterogeneity predict
global riverine fish diversity. Nature 391, 382-384
Huntingford C, Cox PM (1997) Use of statistical and neural network techniques to detect
how stomatal conductance responds to changes in the local environment. Ecological
Modelling 97, 217-246
Jain AK, Dube RC, Chen C (1987) Bootstrap techniques for error estimation. IEEE Trans.
Patt. Anal. Mach. IntelI. PAMI 9,628-633
Kohavi R (1995) A study of cross-validation and bootstrap for estimation and model
selection. Proceeding of the 14 th International Joint Conference on Artificial
Intelligence, Morgan Kaufmann Publishers, pp. 1137-1143
Laberge C, Cluis D, Mercier G (2000) Metal bioleaching prediction in continuous
processing of municipal sewage with Thiobacillus ferrooxidans using neural networks.
Water. Resource Research. 34 (4), 1145-1156
Lek S, Belaud A, Baran P, Dimopoulos I, De1acoste M (1996a) Role of some
environmental variables in trout abundance models using neural networks. Aquatic
Living. Resource 9, 23-29
Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (l996b)
Application of neural networks to modelling nonlinear relationships in
ecology. Ecological Modelling 90,39-52
Lieth H (1975) Modelling the primary productivity of the world. Primary Productivity of
the Biosphere, eds. Lieth, H. & Whittaker, R. H. New York: Springer-Verlag, pp. 237263
Maier HR, Dandy GC (1996) The use of artificial neural networks for the prediction of
water quality parameters. Water Resources Research, 32 (4), 1013-1022
Maier HR, Dandy GC (1999) Empirical comparison of various methods for training feedforward neural networks for salinity forecasting. Water Resources Research, 35 (8),
2591-9596
Oberdorff T, Lek S, Guegan JF (1999). Patterns of endemism in riverine fish of the
Northern Hemisphere. Ecology Letters, 2, 75-81
Özesmi SL, Özesmi U (1999) An artificial neural network approach to spatial habitat
modelling with interspecific interaction. Ecological Modelling 116, 15-31
Parue10 JM, Tomasel F (1997) Prediction of functional characteristics of ecosystems: a
comparison of artificial neural networks and regression models. Ecological Modelling
98,173-186
Ramos-Nino ME, Rarnirez-Rodriguez CA, Clifford MN, Adams MR (1997) A comparison
of quantitative structure-activity relationships for the effect of benzoic and cinnarnic
acids on Listeria monocytogenes using multiple linear regression, artificial neural
network and fuzzy systems. Journal of Applied Microbiology 82, 168-176
Recknage1 F, French M, Harkonen P, Yabunaka KI (1997) Artificial neural network
approach for modelling and prediction of algal blooms. Ecological Modelling 96, 1128
245
Garson GD (1991) Interpreting neural network connection weights. Artificial Intelligence
Expert 6, 47-51
Geman S, Bienenstock E, Doursat R (1992) Neural Network and the biaslvariance dilemna.
Neural Comput. 4, 1-58
Goh ATC (1995) Back-propagation neural networks for modelling complex systems.
Artificial Intelligence Engineering 9, 143-151
Guegan JF, Lek S, Oberdorff T (1998) Energy availability and habitat heterogeneity predict
global riverine fish diversity. Nature 391, 382-384
Huntingford C, Cox PM (1997) Use of statistical and neural network techniques to detect
how stomatal conductance responds to changes in the local environment. Ecological
Modelling 97, 217-246
Jain AK, Dube RC, Chen C (1987) Bootstrap techniques for error estimation. IEEE Trans.
Patt. Anal. Mach. IntelI. PAMI 9,628-633
Kohavi R (1995) A study of cross-validation and bootstrap for estimation and model
selection. Proceeding of the 14 th International Joint Conference on Artificial
Intelligence, Morgan Kaufmann Publishers, pp. 1137-1143
Laberge C, Cluis D, Mercier G (2000) Metal bioleaching prediction in continuous
processing of municipal sewage with Thiobacillus ferrooxidans using neural networks.
Water. Resource Research. 34 (4), 1145-1156
Lek S, Belaud A, Baran P, Dimopoulos I, De1acoste M (1996a) Role of some
environmental variables in trout abundance models using neural networks. Aquatic
Living. Resource 9, 23-29
Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (l996b)
Application of neural networks to modelling nonlinear relationships in
ecology. Ecological Modelling 90,39-52
Lieth H (1975) Modelling the primary productivity of the world. Primary Productivity of
the Biosphere, eds. Lieth, H. & Whittaker, R. H. New York: Springer-Verlag, pp. 237263
Maier HR, Dandy GC (1996) The use of artificial neural networks for the prediction of
water quality parameters. Water Resources Research, 32 (4), 1013-1022
Maier HR, Dandy GC (1999) Empirical comparison of various methods for training feedforward neural networks for salinity forecasting. Water Resources Research, 35 (8),
2591-9596
Oberdorff T, Lek S, Guegan JF (1999). Patterns of endemism in riverine fish of the
Northern Hemisphere. Ecology Letters, 2, 75-81
Özesmi SL, Özesmi U (1999) An artificial neural network approach to spatial habitat
modelling with interspecific interaction. Ecological Modelling 116, 15-31
Parue10 JM, Tomasel F (1997) Prediction of functional characteristics of ecosystems: a
comparison of artificial neural networks and regression models. Ecological Modelling
98,173-186
Ramos-Nino ME, Rarnirez-Rodriguez CA, Clifford MN, Adams MR (1997) A comparison
of quantitative structure-activity relationships for the effect of benzoic and cinnarnic
acids on Listeria monocytogenes using multiple linear regression, artificial neural
network and fuzzy systems. Journal of Applied Microbiology 82, 168-176
Recknage1 F, French M, Harkonen P, Yabunaka KI (1997) Artificial neural network
approach for modelling and prediction of algal blooms. Ecological Modelling 96, 1128