The University of Southampton
University of Southampton Institutional Repository

A comparison of Artificial Neural Networks and Random Forests to predict native fish species richness in Mediterranean rivers

A comparison of Artificial Neural Networks and Random Forests to predict native fish species richness in Mediterranean rivers
A comparison of Artificial Neural Networks and Random Forests to predict native fish species richness in Mediterranean rivers
Machine learning (ML) techniques have become important to support decision making in management and conservation of freshwater aquatic ecosystems. Given the large number of ML techniques and to improve the understanding of ML utility in ecology, it is necessary to perform comparative studies of these techniques as a preparatory analysis for future model applications. The objectives of this study were (i) to compare the reliability and ecological relevance of two predictive models for fish richness, based on the techniques of artificial neural networks (ANN) and random forests (RF) and (ii) to evaluate the conformity in terms of selected important variables between the two modelling approaches. The effectiveness of the models were evaluated using three performance metrics: the determination coefficient (R2), the mean squared error (MSE) and the adjusted determination coefficient (R2adj and both models were developed using a k-fold crossvalidation procedure. According to the results, both techniques had similar validation performance (R2 = 68% for RF and R2 = 66% for ANN). Although the two methods selected different subsets of input variables, both models demonstrated high ecological relevance for the conservation of native fish in the Mediterranean region. Moreover, this work shows how the use of different modelling methods can assist the critical analysis of predictions at a catchment scale
1961-9502
Olaya-Marín, E.J
7fe497fb-2503-4d49-8e03-24863671aac8
Martínez-Capel, F
d30cf615-0379-4dd4-8d6e-2a77fe41e1e9
Vezza, Paolo
feba4aab-3d89-4d3e-826d-ca439261a285
Olaya-Marín, E.J
7fe497fb-2503-4d49-8e03-24863671aac8
Martínez-Capel, F
d30cf615-0379-4dd4-8d6e-2a77fe41e1e9
Vezza, Paolo
feba4aab-3d89-4d3e-826d-ca439261a285

Olaya-Marín, E.J, Martínez-Capel, F and Vezza, Paolo (2013) A comparison of Artificial Neural Networks and Random Forests to predict native fish species richness in Mediterranean rivers. Knowledge and Management of Aquatic Ecosystems, 409 (7). (doi:10.1051/kmae/2013052).

Record type: Article

Abstract

Machine learning (ML) techniques have become important to support decision making in management and conservation of freshwater aquatic ecosystems. Given the large number of ML techniques and to improve the understanding of ML utility in ecology, it is necessary to perform comparative studies of these techniques as a preparatory analysis for future model applications. The objectives of this study were (i) to compare the reliability and ecological relevance of two predictive models for fish richness, based on the techniques of artificial neural networks (ANN) and random forests (RF) and (ii) to evaluate the conformity in terms of selected important variables between the two modelling approaches. The effectiveness of the models were evaluated using three performance metrics: the determination coefficient (R2), the mean squared error (MSE) and the adjusted determination coefficient (R2adj and both models were developed using a k-fold crossvalidation procedure. According to the results, both techniques had similar validation performance (R2 = 68% for RF and R2 = 66% for ANN). Although the two methods selected different subsets of input variables, both models demonstrated high ecological relevance for the conservation of native fish in the Mediterranean region. Moreover, this work shows how the use of different modelling methods can assist the critical analysis of predictions at a catchment scale

This record has no associated files available for download.

More information

Published date: 2013
Organisations: Water & Environmental Engineering Group

Identifiers

Local EPrints ID: 403162
URI: http://eprints.soton.ac.uk/id/eprint/403162
ISSN: 1961-9502
PURE UUID: 54a3074e-d13e-481c-9276-295f9c886aef

Catalogue record

Date deposited: 28 Nov 2016 14:17
Last modified: 15 Mar 2024 03:36

Export record

Altmetrics

Contributors

Author: E.J Olaya-Marín
Author: F Martínez-Capel
Author: Paolo Vezza

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×