Predicting fish yield of African lakes using neural networks

Ecological Modelling

Volumn 120, Issue 2-3, 1999, Pages 325-335

  Laë, Raymond ^a   Lek, Sovan ^b   Moreau, Jacques ^c  

^a IRD Centre de Bretagne   (France)

^b CNRS   (France)

^c GRENOBLE INSTITUTE OF TECHNOLOGY   (France)

Author keywords

African lakes; Fish yield; Fisheries; Multiple regression; Predictive modelling

Indexed keywords

ARTIFICIAL NEURAL NETWORK; FISHERY PRODUCTION; LAKE; PREDICTION; YIELD;

AFRICA;

EID: 0033578393     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3800(99)00112-X     Document Type: Article

References (53)

1. Albiol J., Campmajo C., Casas C., Poch M. Biomass estimation in plant cell cultures: a neural network approach. Biotechnol. Prog. 11:1995;88-92.
2. Bayley P.B. Accounting for effort when comparing tropical fisheries in lakes, river-floodplains, and lagoons. Limnol. Oceanogr. 33:1988;963-972.
3. Bernacsek, G.M., Lopes, S., 1984. Mozambique. Investigations into the fisheries and limnology of Cahora Bassa Reservoir seven years after dam closure. FAO Mozambique, GCP-006-SWE, Field Document. 9, Rome, p. 145.
4. Burgis, M.J., Symoens, J.J., 1987. African wetlands and shallow water bodies. Travaux et Documents 211, ORSTOM Paris, p. 651.
5. Crul, R.C.M., 1992. Models for estimating potential fish yields of African inland waters. FAO, CIFA Occasional Paper 16, p. 22.
6. Crul, R.C.M., Roest, F.C., 1995. Current status of fisheries and fish stocks of the four largest African reservoirs Kainji, Kariba, Nasser/Nubia and Volta. FAO, CIFA Technical Paper 30, p. 134.
7. De Silva S.S., Moreau J., Amarasinghe U.S., Chookajorn T., Guerrero R.D. A comparative assessment of the fisheries in lacustrine inland waters in three Asian countries based on catch and effort data. Fish. Res. 11:1991;177-189.
8. Dimopoulos Y., Bourret P., Lek S. Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Process. Lett. 2(6):1995;1-4.
9. Efron B. Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc. 78:1983;316-330.
10. Ehrman J.M., Clair T.A., Bouchard A. Using neural networks to predict pH changes in acidified Eastern Canadian lakes. Artif. Intell. Appl. 10:1996;1-8.
11. Faraggi D., Simon R. A neural network model for survival data. Stat. Med. 14:1995;73-82.
12. Fausch, K.D., Hawkes, C.L., Parsons, M.G., 1988. Models that predict the standing crop of stream fish from habitat variables. U.S. Forest Service General Technical Report PNW-GTR, p. 213.
13. Garson G.D. Interpreting neural-network connection weights. Artif. Intell. Expert. 6:1991;47-51.
14. Geman S., Bienenstock E., Doursat R. Neural networks and the bias/variance dilemma. Neural Comput. 4:1992;1-58.
15. Goh A.T.C. Back-propagation neural networks for modelling complex systems. Artif. Intell. Eng. 9:1995;143-151.
16. Hanson J.M., Legget W.C. Empirical prediction of fish biomass and yield. Can. J. Fish. Aquat. Sci. 39:1982;257-263.
17. Henderson, H.F., Welcomme, R.L., 1974. The relationship of yield to morpho-edaphic index and numbers of fishermen in African inland fisheries. FAO, CIFA Occasional Paper 1, p. 19.
18. Jain A.K., Dube R.C., Chen C. Bootstrap techniques for error estimation. IEEE Trans. Patt. Anal. Mach. Intell. PAMI. 9:1987;628-633.
19. James F.C., McCulloch C.E. Multivariate analysis in ecology and systematics: panacea or Pandora's box? Ann. Rev. Ecol. Syst. 21:1990;129-166.
20. Jowett, 1993. A method for objectively identifying pool, run, and riffle habitats from physical measurements. N.Z. J. Mar. Freshw. Res. 27, 241-248.
21. Kohavi, R., 1995. A study of cross-validation and bootstrap for estimation and model selection. Proceeding of the 14th International Joint Conference on Artificial Intelligence, Morgan Kaufmann Publishers, pp. 1137-1143.
22. Laë R. Influence de l'hydrologie sur les pêcheries du Delta Central du Niger de 1966 à 1989. Aquat. Living Resour. 5:1992;115-126.
23. Laë R. Estimation des rendements de pêche des lacs Africains au moyen de modèles empiriques. Aquat. Living Resour. 10:1997;83-92.
24. Laë, R., Weigel, J.Y., 1995a. Diagnostic halieutique et propositions d'aménagement: l'exemple de la retenue de Sélingué (Mali). FAO-PAMOS, p. 73.
25. Laë, R., Weigel, J.Y., 1995b. La retenue de Manantali au Mali, diagnostic halieutique et propositions d'aménagement. FAO-PAMOS, p. 65.
26. Lek S., Belaud A., Dimopoulos I., Lauga J., Moreau J. Improved estimation, using neural networks, of the food consumption of fish populations. Mar. Freshw. Res. 46:1995;1229-1236.
27. Lek S., Belaud A., Baran P., Dimopoulos I., Delacoste M. Role of some environmental variables in trout abundance models using neural networks. Aquat. Liv. Res. 9:1996;23-29.
28. Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., Aulagnier S. Application of neural networks to modelling nonlinear relationships in ecology. Ecol. Model. 90:1996;39-52.
29. Lerner, B., Guterman, H., Dinstein, I., Romem, Y., 1994. Feature selection and chromosome classification using a multilayer perceptron neural network. Proceedings of the IEEE International Conference on Neural Networks, Orlando, FL, pp. 3540-3545.
30. Lilliefors, 1967. On the Kolmogorov-Smirnov test for normality with mean and variance unknown, J. Am. Stat. Assoc., 62: 399-402.
31. Marshall, B.E., 1984. Towards predicting ecology and fish yields in African reservoirs from pre-impoundment physico-chemical data. FAO, CIFA Technical Paper 12, p. 36.
32. Melack J.M. Primary productivity and fish yields in tropical lakes. Trans. Am. Fish. Soc. 105:1976;575-580.
33. Moreau, J., De Silva, S.S., 1991. Predictive fish yield models for lakes and reservoirs of the Philippines, Sri Lanka and Thailand. FAO, Fisheries Technical Paper, 319, p. 42.
34. Payne A.I., Harvey M.J. An assessment of the Prochilodus platensis Holmberg population in the Pilcomayo river fishery, Bolivia using scale-based and computer-assisted methods. Aquac. Fish. Manag. 20:1989;233-248.
35. Payne, A.I., Crombie, J., Halls, A.S., Temple, S.A., 1993. Synthesis of simple predictive models for tropical river fisheries, London, MRAG Ltd, p. 92.
36. Rawson D.S. Mean depth and the fish production of large lakes. Ecology. 33:1952;513-521.
37. Rumelhart D.E., Hinton G.E., Williams R.J. Learning representations by back-propagating error. Nature. 323:1986;533-536.
38. Ryder R.A, Kerr S.R., Loftus K.H., Regier H.A. The morpho-edaphic index, a fish yield estimator. Review and evaluation. J. Fish. Res. Board Can. 31:1974;663-688.
39. Ryder R.A. The morpho-edaphic index - use, abuse and fundamental concepts. Trans. Am. Fish. Soc. 111:1982;154-164.
40. Scardi M. Artificial neural networks as empirical models for estimating phytoplankton production. Mar. Ecol. Prog. Ser. 139:1996;289-299.
41. Schlesinger D.A., Regier H.A. Climatic and morpho-edaphic indices of fish yields from natural waters. Trans. Am. Fish. Soc. 111:1982;141-150.
42. Smith, M., 1994. Neural networks for statistical modelling. Van Nostrand Reinhold, New York, p. 235.
43. Smits J.R.M., Breedveld L.W., Derksen M.W.J., Katerman G., Balfoort H.W., Snoek J., Hofstraat J.W. Pattern classification with artificial neural networks: classification of algae, based upon flow cytometer data. Anal. Chim. Acta. 258:1992;11-25.
44. Tomassone, R., Lesquoy, E., Miller, C., 1983. La régression, nouveaux regards sur une ancienne méthode statistique. INRA (Activités scientifiques et agronomique no. 13), Paris, France, p. 188.
45. van der Knaap, M., 1994. Status of fish stocks and fisheries of thirteen medium-sized African reservoirs. FAO, CIFA Technical Paper, 26, p. 107.
46. Vanden Bossche, J.P., Bernacsek, G.M., 1990a. Source book of the inland fishery resources of Africa, FAO, CIFA Technical Paper 18/1, p. 411.
47. Vanden Bossche, J.P., Bernacsek, G.M., 1990b. Source book of the inland fishery resources of Africa, FAO, CIFA Technical Paper 18/2, p. 240.
48. Vanden Bossche, J.P. and Bernacsek, G.M., 1991. Source book of the inland fishery resources of Africa, FAO, CIFA Technical Paper 18/3, p. 219.
49. Verner, J., Morrison, M.L., Ralph, C.J., 1986. Wildlife 2000: modelling habitat relationships of terrestrial vertebrates. Univ. Wisconsin Press, Madison, WI, p. 470.
50. Weisberg, S., 1980. Applied linear regression. Wiley, New York, p. 324.
51. Welcomme, R. L., 1985. River fisheries. FAO Fisheries Technical Paper 262, p. 330.
52. Welcomme R.L. The effects of the Sahelian drought on the fishery of the central delta of the Niger river. Aquac. Fish. Manag. 17:1986;147-154.
53. Youngs W.D., Heimbuch D.G. Another consideration of the morpho-edaphic index. Trans. Am. Fish. Soc. 111:1982;151-153.