Prediction of Fish Yields in Lakes and Reservoirs from simple Empirical Models using Artificial Neural Network (ANN) : An Review

Authors

  • D. Karunakaran  Research Scholar, Research and Development Centre, Bharathiar University, Coimbatore, Tamilnadu, India
  • M. Balakrishnan  Principal Scientist, National Academy of Agricultural Research and Management, Hyderabad, Telangana, India

DOI:

https://doi.org//10.32628/CSEIT195110

Keywords:

Fish Yields, Artificial Neural Network, MEI, CPUE, EBP, Automata Networks

Abstract

Prediction of reservoir yield is an important for fisheries managers to use appropriate scientific management practices to increase the fishery production. Many mathematical or applied mathematics and Artificial Neural Networks models were developed to predict fish production forecast of reservoirs. Ecology of reservoirs is dynamic, extraordinarily advanced and nonlinear in nature. There are several drivers have an effect on the fisheries, both internal and external environmental parameters. Many researchers have assessed fish yield potential based on leaner models using multiple linear regressions. Accurate modelling to predict fish yield of the reservoirs and lakes helps to understand behaviour of the system and managers can formulate appropriate management practices to improve fish yield. This paper provides an in-depth review on existing model developed from simple empirical estimation to high-level non-linear model for assessing fishery potential of lakes and reservoirs.

References

  1. Amarasinghe, U. S., De Silva, S. S., & Nissanka, C. (2002). Evaluation of the robustness of predictive yield models based on catchment characteristics using GIS for reservoir fisheries in Sri Lanka. Fisheries Management and Ecology, 9(5), 293-302.
  2. Aoki, I., & Komatsu, T. (1997). Analysis and prediction of the fluctuation of sardine abundance using a neural network. Oceanolica Acta, 20(1), 81-88.
  3. Aoki, I., & Komatsu, T. (1997). Use of a neural network to analyse and predict the winter catch in the Joban-Boso Seas off the Pacific coast of central Japan of young Japanese sardine (Sardinopsmelanostictus)
  4. Baran, P., Lek, S., Delacoste, M., & Belaud, A. (1996). Stochastic models that predict trout population density or biomass on a mesohabitat scale. Hydrobiologia, 337(1-3), 1-9.
  5. Bayley, P. B. (1988). Accounting for effort when comparing tropical fisheries in lakes, river‐floodplains, and lagoons. Limnology and Oceanography, 33(4part2), 963-972.
  6. Bramick, U., & Lemcke, R. (2003). Regional application of a fish yield estimation procedure to lakes in north-east Germany. Limnologica-Ecology and Management of Inland Waters, 33(3), 205-213.
  7. 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(2-3), 299-311.
  8. Brosse, S., Lek, S., & Dauba, F. (1999). Predicting fish distribution in a mesotrophic lake by hydroacoustic survey and artificial neural networks. Limnology and Oceanography, 44(5), 1293-1303.
  9. Bulon, V. V., &Vinberg, G. G. (1981). Proportions between primary production and fish production in different waters. The basis of freshwater ecosystem investigation, 5-10.
  10. Crul, R. C. (1992). Models for estimating potential fish yields of African inland waters. CIFA Occasional Paper (FAO). no. 16.
  11. D'Angelo, D. J., Meyer, J. L., Howard, L. M., Gregory, S. V., & Ashkenas, L. R. (1995). Ecological uses for genetic algorithms: predicting fish distributions in complex physical habitats. Canadian Journal of Fisheries and Aquatic Sciences, 52(9), 1893-1908.
  12. De Silva, S. S., Amarasinghe, U. S., Nissanka, C., Wijesooriya, W. A. D. D., & Fernando, M. J. J. (2001). Use of geographical information systems as a tool for predicting fish yield in tropical reservoirs: case study on Sri Lankan reservoirs. Fisheries Management and Ecology, 8(1), 47-60.
  13. De Silva, S.S. and Amarasinghe, U.S. (eds.). (2009) Status of reservoir fisheries in five Asian countries. NACA Monograph No.2. Network of Aquaculture Centres in Asia-Pacific, Bangkok, Thailand. 116p.
  14. Dillon, P. J., &Rigler, F. H. (1974). The phosphorus‐chlorophyll relationship in lakes 1, 2. Limnology and oceanography, 19(5), 767-773.
  15. Downing, J. A., & Plante, C. (1993). Production of fish populations in lakes. Canadian Journal of Fisheries and Aquatic Sciences, 50(1), 110-120.
  16. Downing, J. A., Plante, C., & Lalonde, S. (1990). Fish production correlated with primary productivity, not the morphoedaphic index. Canadian Journal of Fisheries and Aquatic Sciences, 47(10), 1929-1936.
  17. Fausch, K. D., Hawkes, C. L., & Parsons, M. G. (1988). Models that predict standing crop of stream fish from habitat variables: 1950-85. Gen. Tech. Rep. PNW-GTR-213. Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research Station. 52 p, 213.
  18. Hanson, J. M., & Leggett, W. C. (1982). Empirical prediction of fish biomass and yield. Canadian Journal of Fisheries and Aquatic Sciences, 39(2), 257-263.
  19. Hasan, M. R., Mondal, M. A. W., Miah, M. I., & Kibria, M. G. (2001). Water quality study of some selected Oxbow Lakes with special emphasis on chlorophyll-a. In. Reservoir and culture-based fisheries: Biology and management. De Siva, S. S. (Ed.)2001. 384 pp.
  20. Hayes, F. R. (1957). On the variation in bottom fauna and fish yield in relation to trophic level and lake dimensions. Journal of the Fisheries Board of Canada, 14(1), 1-32.
  21. Henderson, H. F., & Welcomme, R. L. (1974). The relationship of Morphoedaphic Index and numbers of fishermen in African inland fisheries. Committee for Inland Fisheries of Africa. Occasional Paper I, FAO, Rome, Italy.
  22. Henderson, H. F., &Welcomme, R. L. (1974). The relationship of yield to morpho-edaphic index and numbers of fishermen in African inland fisheries. Food and Agriculture Organization of the United Nations.
  23. Hinton, G. E. (1992). How neural networks learn from experience. Scientific American, 267(3), 144-151.
  24. http://www.fao.org/docrep/005/y3610e/y3610E30.htm
  25. Jenkins, R. M., & Oglesby, R. T. (1982). The morphoedaphic index - concepts and practices: The morphoedaphic index and reservoir fish production. Transactions of the American Fisheries Society, 111(2), 133-140.
  26. Jensen, M. C., & Meckling, W. H. (1994). The nature of man. Journal of applied corporate finance, 7(2), 4-19.
  27. Jhingran, A.G., 1986. Reservoir Fisheries Management in India. Bulletin No. 45, Central Inland Fisheries Research Institute, Barrackpore.
  28. Jones, J. R., & Hoyer, M. V. (1982). Sportfish harvest predicted by summer chlorophyll-α concentration in Midwestern lakes and reservoirs. Transactions of the American Fisheries Society, 111(2), 176-179.
  29. Jowett, I. G. (1993). A method for objectively identifying pool, run, and riffle habitats from physical measurements. New Zealand journal of marine and freshwater research, 27(2), 241-248.
  30. Joy, M. K., & Death, R. G. (2004). Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks. Freshwater Biology, 49(8), 1036-1052.
  31. Kılıç, H., Soyupak, S., Tüzün, İ., İnce, o.,& Başaran, G. (2007). An automata networks based pre-processing technique for artificial neural network modelling of primary production levels in reservoirs. Ecological modelling, 201(3-4), 359-368.
  32. Knosche, R., & Barthelmes, D. (1998). A new approach to estimate lake fisheries yield from limnological basic parameters and first results. Limnologica, 28(2), 133-144.
  33. Kolding, J., & van Zwieten, P. A. (2012). Relative lake level fluctuations and their influence on productivity and resilience in tropical lakes and reservoirs. Fisheries Research, 115, 99-109.
  34. Kuo, J. T., Hsieh, M. H., Lung, W. S., & She, N. (2007). Using artificial neural network for reservoir eutrophication prediction. Ecological modelling, 200(1-2), 171-177.
  35. Lae, R., Lek, S., & Moreau, J. (1999). Predicting fish yield of African lakes using neural networks. Ecological modelling, 120(2-3), 325-335.
  36. Leach, J. H., Dickie, L. M., Shuter, B. J., Borgmann, U., Hyman, J., &Lysack, W. (1987). A review of methods for prediction of potential fish production with application to the Great Lakes and Lake Winnipeg. Canadian Journal of Fisheries and Aquatic Sciences, 44(S2), s471-s485.
  37. Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., &Aulagnier, S. (1996). Application of neural networks to modelling nonlinear relationships in ecology. Ecological modelling, 90(1), 39-52.
  38. Liang, Y., Melack, J. M., & Wang, J. (1981). Primary production and fish yields in Chinese ponds and lakes. Transactions of the American Fisheries Society, 110(3), 346-350.
  39. Machena, C., & Fair, P. (1986). Comparison of fish yields from prediction models between Lakes Tanganyika and Kariba. Hydrobiologia, 137(1), 29-32.
  40. Maravelias, C. D., Haralabous, J., & Papaconstantinou, C. (2003). Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Marine ecology progress series, 255, 249-258.
  41. Matuszek, J. E. (1978). Empirical predictions of fish yields of large North American lakes. Transactions of the American Fisheries Society, 107(3), 385-394.
  42. McConnell, W. J., Lewis, S., & Olson, J. E. (1977). Gross photosynthesis as an estimator of potential fish production. Transactions of the American Fisheries Society, 106(5), 417-423.
  43. Mehrotra, S. N., &Jhingran, A. G. (1986). Evaluation of limnological characteristics and trophic status of Gulariya Reservoir. Proc. Nat. Acad. Sci. India, 53, 204-210
  44. Melack, J. M. (1976). Primary productivity and fish yields in tropical lakes. Transactions of the American Fisheries Society, 105(5), 575-580.
  45. Moreau, J., & De Silva, S. S. (1991). Predictive fish yield models for lakes and reservoirs of the Philippines, Sri Lanka and Thailand (No. 319). Food & Agriculture Org.
  46. Natarajan, A.V., 1976. Ecology and state of fishery development in some of the man made reservoirs of India. In: Symposium on the development and utilisationof Inland Fishery resources, Colombo, 27-29 October 1976.
  47. Natarajan, A.V., 1977.Fifth Workshop of AICP on Ecology and Fisheries of Freshwater Reservoirs. Ukai, 7-8 September, 1977.
  48. Natarajan, A.V., 1979. Ecosystem oriented approach for reservoir fisheries development in India. Souvenir on the ICAR Golden Jubilee Year, CIFRI, Barrackpore, West Bengal, Part I, 107-111.
  49. Nissanka, C., Amarasinghe, U. S., & De Silva, S. S. (2000). Yield predictive models for Sri Lankan reservoir fisheries. Fisheries Management and Ecology, 7(5), 425-436.
  50. Northcote, T. G., & Larkin, P. A. (1956). Indices of productivity in British Columbia lakes. Journal of the Fisheries Board of Canada, 13(4), 515-540.
  51. Nürnberg, G. K. (1996). Trophic state of clear and coloured, soft-and hard water lakes with special consideration of nutrients, anoxia, phytoplankton and fish. Lake and Reservoir Management, 12(4), 432-447
  52. Oglesby, R. T. (1977). Relationships of fish yield to lake phytoplankton standing crop, production, and morphoedaphic factors. Journal of the Fisheries Board of Canada, 34(12), 2271-2279.
  53. Olden, J. D., & Jackson, D. A. (2001). Fish–habitat relationships in lakes: gaining predictive and explanatory insight by using artificial neural networks. Transactions of the American Fisheries Society, 130(5), 878-897.
  54. Peters, R.H. (1986). The role of prediction in limnology.Limnol.Oceanogr. 31: 1143-1159.
  55. Quiros, R. (1990). Predictors of relative fish biomass in lakes and reservoirs of Argentina. Canadian Journal of Fisheries and Aquatic Sciences, 47(5), 928-939.
  56. Raju, M. M., Srivastava, R. K., Bisht, D., Sharma, H. C., & Kumar, A. (2011). Development of artificial neural-network-based models for the simulation of spring discharge. Advances in Artificial Intelligence, 2011.
  57. Ramakrishnaiah, M., & Sarkar, S. K. (1982). Plankton productivity in relation to certain hydrobiological factors in Konar reservoir (Bihar). Journal of the Inland Fisheries Society of India, 14(1), 58-68.
  58. Ramakrishniah, M. (1990, October). Morphodrainage index, a fish yield predictor for Indian reservoirs. In 2nd Asian Reservoir Workshop, Hangzhou, PR China (pp. 15-19).
  59. Rawson, D. S. (1952). Mean depth and the fish production of large lakes. Ecology, 33(4), 513-521.
  60. Rempel, R. S., & Colby, P. J. (1991). A statistically valid model of the morphoedaphic index. Canadian Journal of Fisheries and Aquatic Sciences, 48(10), 1937-1943.
  61. Rigler, F. H. (1982). Recognition of the possible: an advantage of empiricism in ecology. Canadian Journal of Fisheries and Aquatic Sciences, 39(9), 1323-1331.
  62. Ryder, R. A. (1965). A method for estimating the potential fish production of north‐temperate lakes. Transactions of the American Fisheries Society, 94(3), 214-218
  63. Ryder, R. A. (1982). The morphoedaphic index—use, abuse, and fundamental concepts. Transactions of the American Fisheries Society, 111(2), 154-164.
  64. Ryder, R. A., Kerr, S. R., Loftus, K. H., &Regier, H. A. (1974). The morphoedaphic index, a fish yield estimator—review and evaluation. Journal of the Fisheries Board of Canada, 31(5), 663-688.
  65. Scardi, M. (1996). Artificial neural networks as empirical models for estimating phytoplankton production. Marine Ecology Progress Series, 139, 289-299.
  66. Schlesinger, D. A., & Regier, H. A. (1982). Climatic and morphoedaphic indices of fish yields from natural lakes. Transactions of the American Fisheries Society, 111(2), 141-150.
  67. Sinha, M. 1999. Vision of inland fisheries of India of twenty first century. p. 154-168. In: S.H. Abidi, N.K. Thakur, R.S. Birader and L. Shenoy.(eds.) Vision on Indian Fisheries of 21st Century. Central Institute of Fisheries Education, Bombay.
  68. Toews, D. R., & Griffith, J. S. (1979). Empirical estimates of potential fish yield for the Lake Bangweulu system, Zambia, Central Africa. Transactions of the American Fisheries Society, 108(3), 241-252.
  69. Unni, K.S., 1993.Limnology of Central Indian lakes, reservoirs and ponds – a review. In: Mishra, P.C. and Trivedy, R.K. (eds.). Ecology and pollution of Indianlakes and reservoirs. Ashish Publishing House, New Delhi.
  70. Vollenweider, R.A., 1969.A manual on methods for measuring primary productivity in aquatic environment. IBP Hand Book No. 12. Blackwell ScientificPub., U.K. 213 pp.
  71. Weimin, M., Liu, J., Li, Z., Xie, S., Vass, K.K., Sugunan, V.V., Pradhan, G.B., Amarasinghe, U.S., Weerakoon, D.E.M. and Jutagate, T., 2006. Status of Reservoir Fisheries in Five Asian Countries.
  72. Welcomme, R. L. (1976). Approaches to resource evaluation and management in tropical inland waters. In FAO, Rome (Italy). Fisheries Dept. Indo-Pacific Fisheries Council. Symposium on the Development and Utilization of Inland Fishery Resources. Colombo (Sri Lanka). 27 Oct 1976.
  73. Welcomme, R.L.,1972. The inland waters of Africa. FAO, CIFA Technical Paper, 1, 117pp. Publ. FAO, Rome.
  74. Wijeyaratne, M. J. S., &Amarasinghe, U. S. (1987). Estimation of maximum sustainable fish yield and stocking densities of fish fingerlings in fresh water lakes and reservoirs.
  75. Wijeyaratne, M. R. S., &Amarasinghe, U. S. (1984). Estimations of maximum sustainable fish yields and stocking densities of inland reservoirs of Sri Lanka. Journal of the National Aquatic Resources Agency, 31, 65-72.
  76. Youngs, W. D., & Heimbuch, D. G. (1982). Another consideration of the morphoedaphic index. Transactions of the American Fisheries Society, 111(2), 151-153.
  77. Zhang, H., & Zimba, P. V. (2017). Analysing the effects of estuarine freshwater fluxes on fish abundance using artificial neural network ensembles. Ecological modelling, 359, 103-116.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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2019-01-30

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Volume 5, Issue 1, January-February-2019

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[1]
D. Karunakaran, M. Balakrishnan, " Prediction of Fish Yields in Lakes and Reservoirs from simple Empirical Models using Artificial Neural Network (ANN) : An Review, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.88-100, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT195110