Chapter 7
Modelling Ecologicallnterrelations in Running
Water Ecosystems with Artificial Neural Networks
I.M. Schleiter . M. Obach· R. Wagner· H. Wemer· H.-H. Schmidt·
D. Borchardt
7.1
Introduction
The assessment of properties and processes in running water ecosystems is a
major issue in basic and applied aquatic science and has consequences for
environmental management. However, knowledge of the system functions, e.g.
temporal and spatial dynamics of physical, chemical, hydro-morphological and
biological processes, and species-habitat interrelations are still insufficient. An
integrative and prognostic ecological assessment of running waters thus is
presently not available (e.g. Bayerisches Landesamt für Wasserwirtschaft 1998;
Resh et al. 1994; Statzner et al. 1994; Townsend and Hildrew 1994; Townsend
1989; Vannote et al. 1980).
The analysis of running water ecosystems and prediction with deterministic and
stochastic models are limited. However, studies on water quality assessment have
improved the methodology, which can also be applied to basic science.
The high complexity and the spatial and temporal system dynamics are
examples of typical non-linear relationships of abiotic and biotic variables with
often low amounts of non-normally distributed data. This limits the application of
traditional statistics. Artificial neural networks (ANNs) provide an alternative tool
to analyse and model ecological relationships. Their most important features are
multi-dimensionality, non-linearity, the ability to learn from examples and to
generalise.
General aims of our modelling approach are:
Application and development of ANNs to
1. visualise and test data reliability
2. model ecological relations
3. test the suitability of different ANN types and pre-processing methods
4. detect the most important input variables
5. visualise the network status and the activation of neurons
6. develop neural modelling techniques initiating further research on bioindication
and ecological prediction
In basic and applied running water ecology detection and description of unknown
interrelations and generation of hypotheses identification of the most relevant
Modelling Ecologicallnterrelations in Running
Water Ecosystems with Artificial Neural Networks
I.M. Schleiter . M. Obach· R. Wagner· H. Wemer· H.-H. Schmidt·
D. Borchardt
7.1
Introduction
The assessment of properties and processes in running water ecosystems is a
major issue in basic and applied aquatic science and has consequences for
environmental management. However, knowledge of the system functions, e.g.
temporal and spatial dynamics of physical, chemical, hydro-morphological and
biological processes, and species-habitat interrelations are still insufficient. An
integrative and prognostic ecological assessment of running waters thus is
presently not available (e.g. Bayerisches Landesamt für Wasserwirtschaft 1998;
Resh et al. 1994; Statzner et al. 1994; Townsend and Hildrew 1994; Townsend
1989; Vannote et al. 1980).
The analysis of running water ecosystems and prediction with deterministic and
stochastic models are limited. However, studies on water quality assessment have
improved the methodology, which can also be applied to basic science.
The high complexity and the spatial and temporal system dynamics are
examples of typical non-linear relationships of abiotic and biotic variables with
often low amounts of non-normally distributed data. This limits the application of
traditional statistics. Artificial neural networks (ANNs) provide an alternative tool
to analyse and model ecological relationships. Their most important features are
multi-dimensionality, non-linearity, the ability to learn from examples and to
generalise.
General aims of our modelling approach are:
Application and development of ANNs to
1. visualise and test data reliability
2. model ecological relations
3. test the suitability of different ANN types and pre-processing methods
4. detect the most important input variables
5. visualise the network status and the activation of neurons
6. develop neural modelling techniques initiating further research on bioindication
and ecological prediction
In basic and applied running water ecology detection and description of unknown
interrelations and generation of hypotheses identification of the most relevant