Review and comparison of methods to study the contribution of variables in artificial neural network models

Ecological Modelling

Volumn 160, Issue 3, 2003, Pages 249-264

  Gevrey, Muriel ^a   Dimopoulos, Ioannis ^b   Lek, Sovan ^a  

^a CNRS   (France)

^b TECHNOLOGICAL EDUCATIONAL INSTITUTE OF KALAMATA   (Greece)

Author keywords

Artificial neural networks; Backpropagation; Habitat modelling; Non linear relationships; Partial derivatives; Perturbation; Sensitivity analysis; Trout; Variables contribution

Indexed keywords

ANALYTICAL METHOD; ARTIFICIAL NEURAL NETWORK; ECOLOGICAL MODELING; HABITAT SELECTION;

SALMO TRUTTA; SALMONIDAE;

EID: 0037442845     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3800(02)00257-0     Document Type: Conference Paper

References (51)

1. Balls, G.-R., Palmer Brown, D., Sanders, G.-E., 1996. Investigating microclimatic influences on ozone injury in clover (Trifolium subterraneum) using artificial neural networks. New Phytologist 132, 271-280.
2. Brosse, S., Guegan, J.-F., Tourenq, J.-N., Lek, S., 1999. The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecological Modelling 120, 299-311.
3. Colosanti, R.L., 1991. Discussions of the possible use of neural network algorithms in ecological modelling. Binary 3, 13-15.
4. Crisp, D.T., Carling, P.A., 1989. Observations on sitting, dimension and structure of salmonid redds. Journal of Fish Biology 34, 119-134.
5. Delacoste, M., 1995. Analyse de la variabilité spatiale de la reproduction de la truite commune (Salmo trutta L.). Etude à l'échelle du micro et du macrohabitat dans 6 rivières des Pyrénées centrales. Ph.D. of Institut National Polytechnique de Toulouse, p. 133
6. Delacoste, M., Baran, P., Dauba, F., Belaud, A., 1993. Etude du macrohabitat de reproduction de la truite commune (Salmo trutta L.) dans une rivière pyrénéenne, La Neste du Louron. Evaluation d'un potentiel de l'habitat physique de reproduction. Bulletin Francais de la Peche et de la Pisciculture 331, 341-356.
7. Dimopoulos, Y., Bourret, P., Lek, S., 1995. Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Processing Letters 2, 1-4.
8. Dimopoulos, I., Chronopoulos, J., Chronopoulou Sereli, A., Lek, S., 1999. Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecological Modelling 120, 157-165.
9. Edwards, M., Morse, D.R., 1995. The potential for computer-aided identification in biodiversity research. Tree 10 (4), 153-158.
10. El-Keib, A.A., Ma, X., 1995. Application of artificial neural networks in voltage stability assessment. IEEE Transactions on Power Systems 10, 1890-1896.
11. Engelbrecht, A.P., Cloete, I., Zurada, J.M., 1995. Determining the Significance of Input Parameters Using Sensitivity Analysis. From Natural to Artificial Neural Computation. Springer, Malaga-Torremolinos, Spain.
12. Garson, G.D., 1991. Interpreting neural network connection weights. Artificial Intelligence Expert 6, 47-51.
13. Geman, S., Bienenstock, E., Doursat, R., 1992. Neural networks and the bias/valance dilemma. Neural Computation 4, 1-58.
14. Goh, A.T.C., 1995. Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering 9, 143-151.
15. Guo, Z., Uhrig, R.E., 1992. Using modular neural networks to monitor accident conditions in nuclear power plants, SPIE. Applications of Artificial Neural Networks III 1709, 505-516.
16. Hsu, C.T., Tzeng, Y.M., Chen, C.S., Cho, M.Y., 1995. Distribution feeder loss analysis by using an artificial neural network. Electric Power Systems Research 34, 85-90.
17. Kim, S.H., Yoon, C., Kim, B.J., 2000. Structural monitoring system based on sensitivity analysis and a neural network. Computer-aided Civil and Infrastructure Engineering 155, 309-318.
18. Lae, R., Lek, S., Moreau, J., 1999. Predicting fish yield of African lakes using neural networks. Ecological Modelling 120, 325-335.
19. Lek, S., Belaud, A., Dimopoulos, I., Lauga, J., Moreau, J., 1995. Improved estimation, using neural networks, of the food consumption of fish populations. Marine Freshwater Research 46, 1229-1236.
20. Lek, S., Belaud, A., Baran, P., Dimopoulos, I., Delacoste, M., 1996a. Role of some environmental variables in trout abundance models using neural networks. Aquatic Living Resources 9, 23-29.
21. Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., Aulagnier, S., 1996b. Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling 90, 39-52.
22. Lek, S., Guiresse, M., Giraudel, J.-L., 1999. Predicting stream nitrogen concentration from watershed features using neural networks. Water Research 33, 3469-3478.
23. Liong, S.Y., Lim, W.H., Kojiri, T., Hori, T., 2000a. Advance flood forecasting for flood stricken Bangladesh with a fuzzy reasoning method. Hydrological Processes 14, 431-448.
24. Liong, S.Y., Lim, W.H., Paudyal, G.N., 2000b. River stage forecasting in Bangladesh: neural network approach. Journal of Computing in Civil Engineering 14, 1-8.
25. Lu, R.S., Lai, J.L., Lo, S.L., 1998. Predicting solute transfer to surface runoff using neural networks. Water Science and Technology 38, 173-180.
26. Maier, H.R., Dandy, G.C., 1996. The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research 32, 1013-1022.
27. Maier, H.R., Dandy, G.C., 2000. Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications. Environmental Modelling and Software 15, 101-124.
28. Maier, H.R., Dandy, G.C., Burch, M.D., 1998. Use of artificial neural networks for modelling Cyanobacteria anabaena spp. in the river Murray, South Australia. Ecological Modelling 105, 257-272.
29. Malavoi, J.R., 1989. Typologie des faciès d'écoulement ou unités morpho-dynamiques d'un cours d'eau à haute énergie. Bulletin Francais de la Peche et de la Pisciculture 315, 189-210.
30. Manel, S., Dias, J.M., Ormerod, S.J., 1999. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling 120, 337-347.
31. Mastrorillo, S., Dauba, F., Oberdorff, T., Guegan, J.-F., Lek, S., 1998. Predicting local fish species richness in the Garonne river basin. Comptes Rendus de l'Academie des Sciences Serie III Sciences de la Vie 321, 423-428.
32. Ottaway, E.M., Carling, P.A., Clarke, A., Reader, N.A., 1981. Observation on the structure of brown trout, Salmo trutta Linnaeus, redds. Journal of Fish Biology 19, 593-607.
33. Özesmi, S.L., Özesmi, U., 1999. An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecological Modelling 116, 15-31.
34. Paruelo, J.M., Tomasel, F., 1997. Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models. Ecological Modelling 98, 173-186.
35. Ramos-Nino, M.E., Ramirez-Rodriguez, C.A., Clifford, M.N., Adams, M.R., 1997. A comparison of quantitative structure-activity relationships for the effect of benzoic and cinnamic acids on Listeria monocytogenes using multiple linear regression, artificial neural network and fuzzy systems. Journal of Applied Microbiology 82, 168-176.
36. Recknagel, F., French, M., Harkonen, P., Yabunaka, K.-I., 1997. Artificial neural network approach for modelling and prediction of algal blooms. Ecological Modelling 96, 11-28.
37. Rubin, J.-F., 1995. Estimating the success of natural spawning of salmonids in streams. Journal of Fish Biology 46, 603-622.
38. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations by backpropagation error. Nature 323, 533-536.
39. Scardi, M., Harding, L.W., 1999. Developing an empirical model of phytoplankton primary production: a neural network case study. Ecological Modelling 120 (2-3), 213-223.
40. Shirvell, C.S., Dungey, R.G., 1983. Microhabitats chosen by brown trout for feeding and spawning in rivers. Transactions of the American Fisch. Society 112, 355-367.
41. Skelton, P.H., Cambray, J.A., Lombard, A., Benn, G.A., 1995. Patterns of distribution and consevation status of freshwater fishes in South Africa. South Africa Journal of Zoology 30, 71-81.
42. Starrett, S.K., Adams, G.L., 1997. Using artificial neural networks and regression to predict percentage of applied nitrogen leached under turfgrass. Communications in Soil Science Plant Analytical 28, 497-507.
43. Sung, A.H., 1998. Ranking importance of input parameters of neural networks. Expert Systems with Applications 15, 405-411.
44. Tomassone, R., Lesquoy, E., Miller, C., 1983. La régression, Nouveaux Regards sur une ancienne méthode Statistique. INRA, Paris.
45. Tomassone, R., Dervin, C., Masson, J.P., 1993. Biométrie. Modélisation de phénomènes biologiques. Masson.
46. van Wijk, M.T., Bouten, W., 1999. Water and carbon fluxes above European coniferous forests modelled with artificial neural networks. Ecological Modelling 120, 181-197.
47. Weisberg, S., 1980. Applied Linear Regression. Wiley, New York.
48. Whitehead, P.G., Howard, A., Arulmani, C., 1997. Modelling algal growth and transport in rivers: a comparison of times series analysis, dynamic mass balance and neural network techniques. Hydrobiologia 349, 39-46.
49. Yang, C.C., Prasher, S.O., Tan, C.S., 1998. An artificial neural network model for water table management systems, pp. 250-257 in Drainage in the 21st century: food production and the environment: proceedings of the seventh International Drainage Symposiums. Larry C. Brown, symposiums proceedings Editor. St. Joseph, Mich, USA, 738 p.
50. Yao, J., Teng, N., Poh, H.L., Tan, C.L., 1998. Forecasting and analysis of marketing data using neural networks. Journal of Information Science and Engineering 14, 843-862.
51. Zurada, J.M., Malinowski, A., Cloete, I., 1994. Sensitivity analysis for minimization of input data dimension for feedforward neural network. ISCAS'94. IEEE International Symposium on circuits and Systems, vol. 6, IEE Press, London, pp. 447-450.