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

Authors(2) :-D. Karunakaran, M. Balakrishnan

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-01-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 88-100
Manuscript Number : CSEIT195110
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

D. Karunakaran, M. Balakrishnan, "Prediction of Fish Yields in Lakes and Reservoirs from simple Empirical Models using Artificial Neural Network (ANN) : An Review", International 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
Journal URL : http://ijsrcseit.com/CSEIT195110

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