Applications of artificial neural networks predicting macroinvertebrates in freshwaters |
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Authors: | Peter L M Goethals Andy P Dedecker Wim Gabriels Sovan Lek Niels De Pauw |
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Institution: | (1) Department of Applied Ecology and Environmental Biology, Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, J. Plateaustraat 22, Ghent, 9000, Belgium;(2) CESAC UMR 5576, CNRS-University Paul Sabatier, 118, route de Narbonne, Toulouse cedex, 31062, France |
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Abstract: | To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can
be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen
their time-efficient development and relatively high reliability. However, specialized and technical literature on neural
network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This
may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This
paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group
commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development
and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates.
This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial
steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment
and application in ecological river management are presented. In particular, data pre-processing determines to a high extent
the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need
to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract
knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input
variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology,
physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to
enable decision support in water management based on ANN models. |
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Keywords: | Data driven models Decision support systems Ecological modelling Habitat suitability models Knowledge extraction Water management |
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