Monthly catch forecasting of anchovy Engraulis ringens in the north area of Chile: Non-linear univariate approach

Fisheries Research

Volumn 86, Issue 2-3, 2007, Pages 188-200

  Gutiérrez Estrada, Juan Carlos ^a   Silva, Claudio ^b   Yáñez, Eleuterio ^b   Rodríguez, Nibaldo ^b   Pulido Calvo, Inmaculada ^a  

^a UNIVERSITY OF HUELVA   (Spain)

^b PONTIFICIA UNIVERSIDAD CATÓLICA DE VALPARAÍSO   (Chile)

Author keywords

ARIMA model; Catch prediction; Computational neural network; Elman model; Hybrid model; Recurrent neural network

Indexed keywords

ENGRAULIDAE; ENGRAULIS RINGENS;

EID: 34547838136     PISSN: 01657836     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fishres.2007.06.004     Document Type: Article

References (63)

1. Abrahart R.J., and See L. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol. Process. 14 (2000) 2157-2172
2. Alheit J., and Ñiquen M. Regime shift in the Humboldt Current ecosystem. Prog. Oceanogr. 60 (2004) 201-222
3. Anctil F., and Rat A. Evaluation of neural network streamflow forecasting on 47 watersheds. J. Hydrol. Eng. 10 1 (2005) 85-88
4. Bakum A., McLain D.R., and Mayo F.V. The mean annual cycle of coastal upwelling off western North America as observed from surface measurements. Fish. Bull. 72 3 (1974) 843-844
5. Belgrano A., Malmgren B.A., and Lindahl O. Application of artificial neural networks (ANN) to primary production time-series data. J. Plankton Res. 23 6 (2001) 651-658
6. Box G.E.P., and Jenkins G.M. Time Series Analysis: Forecasting and Control (1976), Holden-Day, Oakland, California
7. Cañón J.R. Distribución de anchoveta (Engraulis ringens) en el norte de Chile en relación a determinadas condiciones oceanográficas. Investigación Pesquera 30 (1986) 1-122
8. Castillo, J., Córdova, J., Saavedra, A., Espejo, M., Gálvez, P., Barbieri, M.A., 2002. Evaluación acústica de la biomasa, abundancia, distribución especial y caracterización de cardúmenes de anchoveta en el período de reclutamiento. Primavera 2001. Evaluación del reclutamiento de anchoveta en la I y II Regiones, temporada 2001-2002. Informes Técnicos FIP-IT/2001-11, 207 pp.
9. Chen D.G., and Ware D.M. A neural network model form forecasting fish stock recruitment. Can. J. Fish. Aquat. Sci. 56 (1999) 2385-2396
10. Chen D.G., Hargreaves N.B., Ware D.M., and Liu Y. A fuzzy logic model with genetic algorithm for analysing fish stock-recruitment relationships. Can. J. Fish. Aquat. Sci. 57 9 (2000) 1878-1887
11. Chen D.G., and Hare S.R. Neural network and fuzzy logic models for pacific halibut recruitment analysis. Ecol. Model. 195 (2006) 11-19
12. Cubillos L.A., Bucarey D.A., and Canales M. Monthly abundance estimation for common sardine Strangomera bentincki and anchovy Engraulis ringens in the central-southern area off Chile (34-40°S). Fish. Res. 57 (2002) 117-130
13. De Vries B., and Principe J.C. A theory for neural networks with time delays. Advances in Neural Information Processing Systems vol. 3 (1991), Morgan Kaufmann Publishers, California
14. Elman J.L. Finding structure in time. Cognitive Sci. 14 (1990) 179-211
15. Fréon P., Mullon C., and Voisin B. Investigating remote synchronous patterns in fisheries. Fish. Oceanogr. 12 4/5 (2003) 443-457
16. Garrick M., Cunnane C., and Nash J.E. A criterion of efficiency for rainfall-runoff models. J. Hydrol. 36 3/4 (1978) 375-381
17. Griñó R. Neural networks for univariate time series forecasting and their application to water demand prediction. Neural Network World 2 5 (1992) 437-450
18. Gutiérrez-Estrada J.C., de Pedro-Sanz E., López-Luque R., and Pulido-Calvo I. Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla anguilla L.) intensive rearing system. Aquacult. Eng. 31 (2004) 183-203
19. Hansen J.V., and Nelson R.D. Neural networks and traditional time series methods: a synergistic combination in state economic forecasts. IEEE Trans. Neural Networks 8 4 (1997) 863-873
20. Hardman-Mountford N.J., Richardson A.J., Boyer D.C., Kreiner A., and Boyer H.J. Relating sardine recruitment in the North Benguela to satellite-derived sea surface height using a neural network pattern recognition approach. Prog. Oceanogr. 59 (2003) 241-255
21. Hare S.R., Minobe S., Wooster W.S., and McKinnell S. An introduction to the PICES symposium on the nature and impacts of North Pacific climate regime shifts. Prog. Oceanogr. 47 (2000) 99-102
22. Hilborn R., and Walters C.J. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty (1992), Chapman and Hall, New York
23. Holton-Wilson J., and Keating B. Previsiones en los negocios (1996), IRWIN, Madrid
24. Huse G., and Gjosaeter H. A neural network approach for predicting stock abundance of the Barents Sea capelin. Sarsia 84 5/6 (1999) 457-464
25. Huse G., and Ottersen G. Forecasting recruitment and stock biomass of Northeast Artic cod using neural networks. Sci. Mar. 67 (2003) 325-335
26. Hutching J.A. Collapse and recovery of marine fishes. Nature 406 (2000) 882-885
27. Hyun K., Song M.Y., Kim S., and Chon T.S. Using an artificial neural network to patternize long-term fisheries data from South Korea. Aquat. Sci. 67 (2005) 382-389
28. Iyer M.S., and Rhinehart R.R. A method to determine the required number of neural-network training repetitions. IEEE Trans. Neural Networks 10 2 (1999) 427-432
29. Kitanidis P.K., and Bras R.L. Real time forecasting with a conceptual hydrological model. 2. Applications and results. Water Resour. Res. 16 6 (1980) 1034-1044
30. Komatsu T., Aoki I., Mitani I., and Ishii T. Prediction of the catch of japanese sardine larvae in sagami bay using a neural-network. Fish. Sci. 60 4 (1994) 385-391
31. Lassen, H., Medley, P., 2001. Virtual Population Analysis. A practical manual for stock assessment. FAO Fish. Tech. Paper 400.
32. Legates D.R., and McCabe Jr. G.J. Evaluating the use of 'goodness-of-fit' measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35 1 (1999) 233-241
33. Lloret J., Lleonart J., and Solé I. Time series modelling of landing in Northwest Mediterranean Sea. ICES J. Mar. Sci. 57 (2000) 171-184
34. Makridakis S., Wheelwrighy S., and McGee V. Forecasting: Methods and Applications (1983), John Wiley and Sons, New York
35. Maravelias C.D., Haralaobus J., and Papaconstantinou C. Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Mar. Ecol. Prog. Ser. 255 (2003) 249-258
36. McLaughlin R.L. Forecasting models: sophisticated or naïve?. J. Forecasting 2 3 (1983) 274-276
37. Montecinos A., Purca S., and Pizarro O. Interannual-to-interdecadal sea surface temperature variability along the westerns coast of South America. Geophys. Res. Lett. 30 (2003) 1570
38. Myers R.A., Barrowman N.J., Hutching J.A., and Rosemberg A.A. Population dynamics of exploited fish stocks at low populations levels. Science 269 (1995) 1106-1108
39. Nash J.E., and Sutcliffe J.V. River flow forecasting through conceptual models. I. A discussion of principles. J. Hydrol. 10 (1970) 282-290
40. Park H.H. Analysis and prediction of walleye pollock (Theragrana chalcogramma) landings in Korea by time series analysis. Fish. Res. 38 (1998) 1-7
41. Parrish R.H., Bakum A., Husby D.M., and Nelson C.S. Comparative climatology of selected environmental processes in relation to Eastern boundary current pelagic fish reproduction. FAO Fish. Rep. 291 3 (1983) 731-777
42. Prybutok V.R., Yi J., and Mitchell D. Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations. Eur. J. Oper. Res. 122 (2000) 31-40
43. Pulido-Calvo I., Roldán J., López-Luque R., and Gutiérrez-Estrada J.C. Demand forecasting for irrigation water distribution system. J. Irrig. Drain. Eng. 129 6 (2003) 422-431
44. Pulido-Calvo I., and Portela M.M. Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. J. Hydrol. 332 1/2 (2007) 1-15
45. Qi M., and Zhang G.P. An investigation of model selection criteria for neural network time series forecasting. Eur. J. Oper. Res. 132 (2001) 666-680
46. Rumelhart D.E., Hinton G.E., and Williams R.J. 'Learning' representations by backpropagation errors. Nature 323 (1986) 533-536
47. SAG. Anuarios Estadísticos de Pesca. Servicio Agrícola y Ganadero (1963-1977), Ministerio de Agricultura, Chile
48. Santander H., and Flores R. Los desoves y distribución larval de cuatro especies pelágicas y sus relaciones con las variaciones del medio ambiente marino frente al Perú. FAO Inf. Pesca 292 3 (1983) 835-867
49. SERNAPESCA. Anuarios Estadísticos de Pesca. Servicio Nacional de Pesca (1978-2004), Ministerio de Economía, Fomento y Reconstrucción, Chile
50. Shepherd A.J. Second-order Methods for Neural Networks (1997), Springer, New York
51. Shepherd J.G. Extended survivors analysis: an improved method for analysis of catch-at-age data and abundance indices. ICES J. Mar. Sci. 56 (1999) 584-591
52. Stergiou K.I., Christou E.D., and Petrakis G. Modelling and forecasting monthly fisheries catches: comparison of regression, univariate and multivariate time series methods. Fish. Res. 29 (1997) 55-95
53. Tan Y., and van Cauwenberghe A. Neural-network-based d-step-ahead predictors for nonlinear systems with time delay. Eng. Appl. Artif. Intell. 12 1 (1999) 21-25
54. Tsoukalas L.H., and Uhrig R.E. Fuzzy and Neural Approaches in Engineering (1997), Wiley Interscience, New York
55. Velo-Suárez L., and Gutiérrez-Estrada J.C. Artificial neural network approaches to one-step weekly prediction of Dinophysis acuminata blooms in Huelva (Western Andalucía, Spain). Harmful Algae 6 (2007) 361-371
56. Ventura S., Silva M., Pérez-Bendito D., and Hervás C. Artificial neural networks for estimation of kinetic analytical parameters. Anal. Chem. 67 9 (1995) 1521-1525
57. Wedding II D.K., and Cios K.J. Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model. Neurocomputing 10 (1996) 149-168
58. Willmott C.J., Ackleson S.G., Davis R.E., Feddema J.J., Klink K.M., Legates D.R., O'Donnell J., and Rowe C.M. Statistics for the evaluation and comparison of models. J. Geophys. Res. 90 (1985) 8995-9005
59. Yáñez E., Barbieri M.A., and Barra O. Evaluación de los principales recursos pelágicos explotados en la zona norte de Chile entre 1957 y 1985. In: Arana P. (Ed). La pesca en Chile. Escuela de Ciencias del Mar (1986), UCV, Valparaíso 183-194
60. Yáñez E., Barbieri M.A., Silva C., Nieto K., and Espíndola F. Climate variability and pelagic fisheries in northern Chile. Prog. Oceanogr. 49 (2001) 581-596
61. Yapo P.O., Gupta H.V., and Sorooshian S. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. J. Hydrol. 181 (1996) 23-48
62. Zeng Z., Yan H., and Fu A.M.N. Time-series prediction based on pattern classification. Artif. Intell. Eng. 15 (2001) 61-69
63. Zhang G.P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (2003) 159-175