Predicting fish yield of African lakes using neural networks

Predicting fish yield of African lakes using neural networks Artificial neural network (ANN) approaches to modelling and prediction of fish yield as related to the environmental characteristics were developed from the combination of six variables: catchment area over maximum area, fishing effort, conductivity, depth, altitude and latitude. For a total of 59 lakes studied, the correlation coefficients obtained between the estimated and observed values of abundance were significantly high with the neural network procedure ( r adjusted=0.95, P <0.01). The predictive power of the ANN models was determined by the leave one out cross-validation procedures. This is an appropriate testing method when the data set is quite small and/or when each sample is likely to have ‘unique information’ that is relevant to the model. Fish yields estimated with this method were significantly related to the observed fish yields with the correlation coefficient reaching 0.83 ( P <0.01). Our study shows the advantages of the backpropagation procedure of the neural network in stochastic approaches to fisheries ecology. Using the specific algorithm, we can identify the factor influencing the fish yield and the mode of action of each factor. The limitations of the neural network approaches as well as statistical and ecological perspectives are discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Predicting fish yield of African lakes using neural networks

Ecological Modelling, Volume 120 (2) – Aug 17, 1999

Loading next page...
 
/lp/elsevier/predicting-fish-yield-of-african-lakes-using-neural-networks-nCCM6eGQsQ
Publisher
Elsevier
Copyright
Copyright © 1999 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
DOI
10.1016/S0304-3800(99)00112-X
Publisher site
See Article on Publisher Site

Abstract

Artificial neural network (ANN) approaches to modelling and prediction of fish yield as related to the environmental characteristics were developed from the combination of six variables: catchment area over maximum area, fishing effort, conductivity, depth, altitude and latitude. For a total of 59 lakes studied, the correlation coefficients obtained between the estimated and observed values of abundance were significantly high with the neural network procedure ( r adjusted=0.95, P <0.01). The predictive power of the ANN models was determined by the leave one out cross-validation procedures. This is an appropriate testing method when the data set is quite small and/or when each sample is likely to have ‘unique information’ that is relevant to the model. Fish yields estimated with this method were significantly related to the observed fish yields with the correlation coefficient reaching 0.83 ( P <0.01). Our study shows the advantages of the backpropagation procedure of the neural network in stochastic approaches to fisheries ecology. Using the specific algorithm, we can identify the factor influencing the fish yield and the mode of action of each factor. The limitations of the neural network approaches as well as statistical and ecological perspectives are discussed.

Journal

Ecological ModellingElsevier

Published: Aug 17, 1999

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off