Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

Predicting the abundance of minnow Phoxinus phoxinus (Cyprinidae) in the River Ariège (France) using artificial neural networks

Sylvain Mastrorillo, Sovan Lek, Francis Dauba

Aquat. Living Resour., 10 3 (1997) 169-176

Published online: 1997-05-15

DOI: https://doi.org/10.1051/alr:1997018

PDF (656.5 KB)

This article has been cited by the following article(s):

Eco-hydrologic model cascades: Simulating land use and climate change impacts on hydrology, hydraulics and habitats for fish and macroinvertebrates

Björn Guse, Jochem Kail, Johannes Radinger, et al.
Science of The Total Environment 533 542 (2015)
https://doi.org/10.1016/j.scitotenv.2015.05.078

Neural networks in fisheries research

Iragavarapu Suryanarayana, Antonio Braibanti, Rupenaguntla Sambasiva Rao, et al.
Fisheries Research 92 (2-3) 115 (2008)
https://doi.org/10.1016/j.fishres.2008.01.012

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Peter L. M. Goethals, Andy P. Dedecker, Wim Gabriels, Sovan Lek and Niels De Pauw
Aquatic Ecology 41 (3) 491 (2007)
https://doi.org/10.1007/s10452-007-9093-3

Neural Networks for Hydrological Modeling

Pauline Kneale
Neural Networks for Hydrological Modeling 197 (2004)
https://doi.org/10.1201/9780203024119.ch11

Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks

Michael K. Joy and Russell G. Death
Freshwater Biology 49 (8) 1036 (2004)
https://doi.org/10.1111/j.1365-2427.2004.01248.x

Age at maturation predicted from routine scale measurements in Norwegian spring-spawning herring (Clupea harengus) using discriminant and neural network analyses

Georg H Engelhard, Ulf Dieckmann and Olav Rune Godø
ICES Journal of Marine Science 60 (2) 304 (2003)
https://doi.org/10.1016/S1054-3139(03)00017-1

Population Viability in Plants

J. Elith and M. A. Burgman
Ecological Studies, Population Viability in Plants 165 203 (2003)
https://doi.org/10.1007/978-3-662-09389-4_8

Using artificial neural networks to model the suitability of coastline for breeding by New Zealand fur seals (Arctocephalus forsteri)

Corey J.A Bradshaw, Lloyd S Davis, Martin Purvis, Qingqing Zhou and George L Benwell
Ecological Modelling 148 (2) 111 (2002)
https://doi.org/10.1016/S0304-3800(01)00425-2

Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks

Julian D Olden and Donald A Jackson
Ecological Modelling 154 (1-2) 135 (2002)
https://doi.org/10.1016/S0304-3800(02)00064-9

Assessing the Ecological Integrity of Running Waters

R. Wagner, T. Dapper and H.-H. Schmidt
Assessing the Ecological Integrity of Running Waters 143 (2000)
https://doi.org/10.1007/978-94-011-4164-2_11

Quantitative Methods for Conservation Biology

Jane Elith
Quantitative Methods for Conservation Biology 39 (2000)
https://doi.org/10.1007/0-387-22648-6_4

Predicting local fish species richness in the garonne river basin

Sylvain Mastrorillo, Francis Dauba, Thierry Oberdorff, Jean-Francois Guégan and Sovan Lek
Comptes Rendus de l'Académie des Sciences - Series III - Sciences de la Vie 321 (5) 423 (1998)
https://doi.org/10.1016/S0764-4469(98)80307-7