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Solving Capelin Time Series Ecosystem Problem using Hybrid Artificial Neural Networks- Genetic Algorithms Model

by Karam M. Eghnam, Sulieman Bani-Ahmad, Alaa F. Sheta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 2
Year of Publication: 2011
Authors: Karam M. Eghnam, Sulieman Bani-Ahmad, Alaa F. Sheta
10.5120/2336-3046

Karam M. Eghnam, Sulieman Bani-Ahmad, Alaa F. Sheta . Solving Capelin Time Series Ecosystem Problem using Hybrid Artificial Neural Networks- Genetic Algorithms Model. International Journal of Computer Applications. 19, 2 ( April 2011), 8-12. DOI=10.5120/2336-3046

@article{ 10.5120/2336-3046,
author = { Karam M. Eghnam, Sulieman Bani-Ahmad, Alaa F. Sheta },
title = { Solving Capelin Time Series Ecosystem Problem using Hybrid Artificial Neural Networks- Genetic Algorithms Model },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 2 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume19/number2/2336-3046/ },
doi = { 10.5120/2336-3046 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:05:55.844199+05:30
%A Karam M. Eghnam
%A Sulieman Bani-Ahmad
%A Alaa F. Sheta
%T Solving Capelin Time Series Ecosystem Problem using Hybrid Artificial Neural Networks- Genetic Algorithms Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 2
%P 8-12
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Capelin stock in the Barents Sea is the largest in the world. It maintained a fishery with annual catches of up to 3 million tons. The Capelin stock problem has an impact in fish stock development. In this paper, the stock prediction problem of the Barents Sea capelin is attacked using Artificial Neural Network (ANNs) and Multiple Linear model Regression (MLR) model. The weights of ANNs are adapted using the Genetic Algorithm (GA).The models are compared against each other and empirical work has shown that the ANN-GA model can have better overall accuracy over (MLR). It performs 21% over MLR model.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Forecasting Capelin stock Neural Networks Genetic Algorithm Ecosystem

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