The use of artificial neural networks to predict the presence of small‐bodied fish in a river

The use of artificial neural networks to predict the presence of small‐bodied fish in a river 1. Discriminant factorial analysis (DFA) and artificial neural networks (ANN) were used to develop models of presence/absence for three species of small‐bodied fish (minnow, Phoxinus phoxinus, gudgeon, Gobio gobio, and stone loach, Barbatula barbatula). 2. Fish and ten environmental variables were sampled using point abundance sampling by electrofishing in the Ariège River (France) at 464 sampling points. 3. Using DFA, the percentage of correct assignments, expressed as the percentage of individuals correctly classified over the total number of examined individuals, was 62.5% for stone loach, 66.6% for gudgeon and 78% for minnow. With back‐propagation of ANN, the recognition performance obtained after 500 iterations was: 82.1% for stone loach, 87.7% for gudgeon and 90.1% for minnow. 4. The better predictive performance of the artificial neural networks holds promise for other situations with non‐linearly related variables. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Freshwater Biology Wiley

The use of artificial neural networks to predict the presence of small‐bodied fish in a river

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Publisher
Wiley
Copyright
Blackwell Science Ltd, Oxford
ISSN
0046-5070
eISSN
1365-2427
DOI
10.1046/j.1365-2427.1997.00209.x
Publisher site
See Article on Publisher Site

Abstract

1. Discriminant factorial analysis (DFA) and artificial neural networks (ANN) were used to develop models of presence/absence for three species of small‐bodied fish (minnow, Phoxinus phoxinus, gudgeon, Gobio gobio, and stone loach, Barbatula barbatula). 2. Fish and ten environmental variables were sampled using point abundance sampling by electrofishing in the Ariège River (France) at 464 sampling points. 3. Using DFA, the percentage of correct assignments, expressed as the percentage of individuals correctly classified over the total number of examined individuals, was 62.5% for stone loach, 66.6% for gudgeon and 78% for minnow. With back‐propagation of ANN, the recognition performance obtained after 500 iterations was: 82.1% for stone loach, 87.7% for gudgeon and 90.1% for minnow. 4. The better predictive performance of the artificial neural networks holds promise for other situations with non‐linearly related variables.

Journal

Freshwater BiologyWiley

Published: Oct 1, 1997

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