Chapter 19 Neural network applications in coastal ecological modeling

Elsevier Oceanography Series

Volumn 67, Issue C, 2003, Pages 505-532

  Scardi, Michele ^a  

^a UNIVERSITY OF ROME TOR VERGATA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 67349102570     PISSN: 04229894     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0422-9894(03)80136-8     Document Type: Article

References (63)

1. Aguilera P.A., Garrido Frenich A., Torres J.A., Castro H., Martinez Vidal J.L., and 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 17 (2001) 4053-4062
2. Aussem A., and Hill D. Wedding connectionist and algorithmic modelling towards forecasting Caulerpa taxifolia development in the north-western Mediterranean sea. Ecological Modelling 120 (1999) 225-236
3. Aussem A., and Hill D. Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea. Neurocomputing 30 (2000) 71-78
4. Balls G.R., Palmer Brown D., and Sanders G.E. Investigating microclimatic influences on ozone injury in clover (Trifolium subterraneum) using artificial neural networks. New Phytology 132 (1996) 271-280
5. Baran P., Lek S., Delacoste M., and Belaud A. Stochastic models that predict trouts popu-lation densities or biomass on macrohabitat scale. Hydrobiologia 337 (1996) 1-9
6. Barciela R.M., Garcia E., and Fernández E. Modelling primary production in a coastal embayment affected by upwelling using dynamic ecosystem models and artificial neural networks. Ecological Modelling 120 (1999) 199-211.
7. Behrenfeld M.J., and Falkowski P.G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnology and Oceanography 42 1 (1997) 1-20
8. Bradshaw C.J.A., Davis L.S., Purvis M., Zhou Q., and Benwell G.L. Using artificial neural networks to model the suitability of coastline for breeding by New Zealand fur seals (Arctocephalus forsten). Ecological Modelling 148 (2002) 111-131
9. Brey T., and Gerdes D. High Antarctic macrobenthic community production. Journal of Experimental Marine Biology and Ecology 231 (1998) 191-200
10. Brey T., Jarre-Teichmann A., and Borlich O. Artificial neural network versus multiple linear regression: Predicting P:B ratios from empirical data. Marine Ecology Progress Series 140 (1996) 251-256
11. Chon T.-S., Park Y.S., Moon K.H., and Cha E.Y. Patternizing communities by using an artificial neural network. Ecological Modelling 90 (1996) 69-78
12. Cohen J. A coefficient of agreement of nominal scales. Educational and Psychological Mea-surement 20 (1960) 37-46
13. Colasanti R.L. Discussions of the possible use of neural network algorithms in ecological modelling. Binary 3 (1991) 13-15
14. Cole B.E., and Cloern J.E. Significance of biomass and light availability to phytoplankton productivity in San Francisco Bay. Marine Ecology Progress Series 17 (1984) 15-24
15. Cole B.E., and Cloern J.E. An empirical model for estimating phytoplankton productivity in estuaries. Marine Ecology Progress Series 36 (1987) 299-305
16. Deo M.C., and Sridhar Naidu C. Real time wave forecasting using neural networks. Ocean Engineering 26 (1999) 191-203
17. Deo M.C., Jha A., Chaphekar A.S., and Ravikant K. Neural networks for wave forecasting. Ocean Engineering 28 (2001) 889-898
18. Dimopoulos I., Bourret P., and Lek S. Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Processing Letters 2 (1995) 1-4
19. Dimopoulos I., Chronopoulos J., Chronopoulou Sereli A., and Lek S. Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecological Modelling 120 2-3 (1999) 157-165
20. Edwards M., and Morse D.R. The potential for computer-aided identification in biodiversity research. Trends in Ecological Evolution 10 4 (1995) 153-158
21. Eppley R.W., Stewart E., Abbott M.R., and Heyman U. Estimating ocean primary produc-tion from satellite chlorophyll Introduction to regional differences and statistics for the Southern California Bight. Journal of Plankton Research 7 (1985) 57-70
22. Fielding A.H. An introduction to machine learning methods. In: A.H. F. (Ed). Machine Learning Methods for Ecological Applications (1999), Kluwer Publishers, Boston, Dordrecth, London 1-35
23. Garson G.D. Interpreting neural network connection weights. Artificial Intelligence Expert 6 (1991) 47-51.
24. Giraudel J.L., and Lek S. A comparison of self-organizing map algorithm and some con-ventional statistical methods for ecological community ordination. Ecological Modelling 146 (2001) 329-339.
25. Giske J., Huse G., and Fiksen O. Modelling spatial dynamics of fish. Reviews in Fish Biol-ogy and Fisheries 8 (1998) 57-91
26. Guégan J.F., Lek S., and Oberdorff T. Energy availability and habitat heterogeneity predict global riverine fish diversity. Nature 391 (1998) 382-384
27. Györgyi G. Inference of a rule by a neural network with thermal noise. Physical Review Letters 64 (1990) 2957-2960
28. Hammitt A.M., and Bartlett E.B. Determining functional relationships from trained neural networks. Mathematical and Computer Modelling 22 (1995) 83-103
29. Harding L.W.Jr., Mallonee M.E., and Perry E.S. Toward a predictive understanding of primary productivity in a temperate, partially stratified estuary. Estuarine, Coastal and Shelf Science 55 (2002) 437-463
30. 696p. Haykin S. Neural Networks: A Comprehensive Foundation (1994), Macmillan/IEEE Press, New York, NY
31. Hoang H., Recknagel F., Marshall J., and Choy S. Predictive modelling of macroinvertebrate assemblages for stream habitat assessments in Queensland (Australia). Ecological Modelling 195 (2001) 195-206
32. Hornik K., Stinchcombe M., and White H. Multilayer feedforward networks are universal approximators. Neural Networks 2 (1989) 359-366
33. Huang W., and Foo S. Neural network modeling of salinity variation in Apalachicola River. Water Research 36 (2002) 356-362
34. Jeong K.-S., Joo G.-J., Kim H.-W., Ha K., and Recknagel F. Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent artificial neural network. Ecological Modelling 146 (2001) 115-129
35. Keiner L.E., and Yan X.-H. Neural network model for estimating sea surface chlorophyll and sediments from Thematic Mapper imagery. Remote Sensing in the Environment 66 (1998) 53-165
36. Kingston K.S., Ruessink B.G., van Enckevort I.M.J., and Davidson M.A. Artificial neural network correction of remotely sensed sandbar location. Marine Geology 169 (2000) 137-160
37. 217p. Kremer J.N., and Nixon S.W. A Coastal Marine Ecosystem: Simulation and Analysis (1978), Springer-Verlag, New York
38. Lee T.L., and Jeng D.S. Application of artificial neural networks in tide-forecasting. Ocean Engineering 29 (2002) 1003-1022
39. Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., and Aulagnier S. Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling 90 1 (1996) 39-52
40. Lek S., Belaud A., Baran P., Dimopoulos I., and Delacoste M. Role of some environmental variables in trout abundance models using neural networks. Aquatic Living Research 9 (1996) 23-29
41. Lek S., and Guégan J.E. Artificial neural networks as a tool in ecological modelling. An introduction. Ecological Modelling 120 (1999) 65-73
42. Manel S., Dias J.-M., and Ormerod S.J. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: A case study with a Himalayan river bird. Ecological Modelling 120 (1999) 337-347
43. Mase H., and Kitano T. Prediction model for occurrence of impact wave force. Ocean Engi-neering 26 (1999) 949-961
44. Mase H., Sakamoto M., and Sakai T. Neural network for stability analysis of rubble-mound breakwaters. Journal of Waterway Port, Coastal and Ocean Engineering 121 (1995) 294-299.
45. Olden J.D., and Jackson D.A. A comparison of statistical approaches for modelling fish species distributions. Freshwater Biology 47 (2002) 1976-1995
46. Pearson R.G., Dawson T.P., Berry P.M., and Harrison P.A. SPECIES: A spatial evaluation of climate impact on the envelope of species. Ecological Modelling 154 (2002) 289-300
47. Pineda F. Generalization of backpropagation to recurrent neural networks. Physical Review Letters 19 59 (1987) 2229-2232
48. Recknagel F. Applications of machine learning to ecological modeling. Ecological Modelling 146 (2001) 303-310
49. Recknagel F., French M., Harkonen P., and Yabunaka K.I. Artificial neural network ap-proach for modelling and prediction of algal blooms. Ecological Modelling 96 (1997) 11-28
50. Recknagel F., Fukushima T., Hanazato T., Takamura N., and Wilson H. Modelling and prediction of phyto-and Zooplankton dynamics in Lake Kasumigaura by artificial neural networks. Lakes & Reservoirs 3 (1998) 123-133
51. Rumelhart D.E., Hinton G.E., and Williams G.E. Learning representations by back-propagating errors. Nature 323 (1986) 533-536
52. Sandidge J.C., and Holyer R.J. Coastal bathymetry from hyperspectral observations of water radiance. Remote Sensing of the Environment 65 (1998) 341-352
53. Scardi M. Artificial neural networks as empirical models of phytoplankton production. Ma-rine Ecology Progress Series 139 (1996) 289-299
54. Scardi M. Neuronal network models of phytoplankton primary production. In: S. L., and J.-F. G. (Eds). Artificial Neuronal Networks: Application To Ecology And Evolution (2000), Springer-Verlag, Berlin/Heidelberg 115-129
55. Scardi M. Advances in neural network modeling of phytoplankton primary production. Ecological Modelling 146 (2001) 33-35
56. Scardi M., and Harding L.W.Jr. Developing an empirical model of phytoplankton primary production: A neural network case study. Ecological Modelling 120 (1999) 213-223
57. Scardi, M., Lek, S., Park., Y.S., Verdonschot, P., and J0rgensen, S.E., in press. Improving neural network models by means of theoretical ecological knowledge. Ecological Modelling, in press.
58. Sen S.T., and Smeins F.E. Predicting grassland community changes with an artificial neural network model. Ecological Modelling 84 (1996) 91-97
59. Smith R.C., and Baker K.S. The bio-optical state of ocean waters and remote sensing. Limnology and Oceanography 23 2 (1978) 247-259
60. Ultsch A., and Röske F. Self-organizing feature maps predicting sea levels. Information Sciences 144 (2002) 91-125
61. Walter M., Recknagel F., Carpenter C., and Bormans M. Predicting eutrophication effects in the Burrinjuck Reservoir (Australia) by means of the deterministic model SALMO and the recurrent neural network model ANN. Ecological Modelling 146 (2001) 97-113
62. Yabunaka K., Masaaki H., and Murakami A. Novel application of a back-propagation artificial neural network model formulated to predict algal blooms. Water Science Technology 36 5 (1997) 89-97
63. Zhang Y., Pulliainen J., Koponen S., and Hallikainen M. Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data. Remote Sensing of the Environment 81 (2002) 327-336