Modelling population dynamics of aquatic insects with artificial neural networks

Obach, Michael; Wagner, Rüdiger; Werner, Heinrich; Schmidt, Hans-Heinrich

doi:10.1016/s0304-3800(01)00307-6

Cited by 57 publications

(29 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, the variables have to be scaled in such a way as to be commensurate with the limits of the activation functions used in the output layer (Maier and Dandy 2000). Several authors (Chon et al , 2002Gabriels et al 2007;Obach et al 2001;Park et al 2003a, b;Schleiter et al 1999;Schleiter et al 2001;Wagner et al 2000) proportionally normalized the data between zero and one [0 1] in the range of the maximum and minimum values, while Dedecker et al (2004Dedecker et al ( , 2005a and Gabriels et al (2002) used the interval [À1 1]. Moreover the division of the dataset in folds for cross-validation is crucial for a good model training and evaluation.…”

Section: Data Processingmentioning

confidence: 99%

“…However, these methods can merely be applied when large datasets are available. Beauchard et al (2003), Obach et al (2001), Schleiter et al (1999Schleiter et al ( , 2001) used a stepwise procedure to identify the most influential variables. In this approach, separate networks are trained for each input variable.…”

Section: Input Variable Selectionmentioning

confidence: 99%

“…Disadvantages of these approaches are that they are computationally intensive and that they are unable to capture the importance of certain combinations of variables that might be insignificant on their own. Obach et al (2001), Schleiter et al (1999Schleiter et al ( , 2001 and Wagner et al (2000) applied a special variant of the backpropagation network type, the socalled senso-net, to determine the most important input variables (sensitivity analysis). Senso-nets include an additional weight for each input neuron representing the relevance (sensitivity) of the corresponding input parameter for the neural model.…”

Section: Input Variable Selectionmentioning

confidence: 99%

“…A third frequently used technique is genetic algorithms (e.g. D'heygere et al 2006;Obach et al 2001;Schleiter et al 2001). This technique automatically selects the relevant input variables (Goldberg 1989).…”

Section: Input Variable Selectionmentioning

confidence: 99%

See 3 more Smart Citations

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

View full text Add to dashboard Cite

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.

show abstract

Section: Data Processingmentioning

confidence: 99%

Section: Input Variable Selectionmentioning

confidence: 99%

Section: Input Variable Selectionmentioning

confidence: 99%

Section: Input Variable Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

View full text Add to dashboard Cite

show abstract

“…During the training phase, parameters of the models are tuned automatically by the learning algorithm in order to obtain the best classiWcation performances on the training set (Bergeron 2003). Such methods which are based on machine learning algorithms have been applied with success in other biological disciplines, particularly molecular biology (King and Sternberg 1990;Muggleton et al 1992), drug design (King et al , 1993, neurology (Zhang et al 2005a, b;Mitchell 2005, Bogacz andBrown 2003) and ecology (Stockwell 2006;Recknagel 2001;Obach et al 2001;Schleiter et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Classification of dog barks: a machine learning approach

Molnár

Kaplan

Roy³

et al. 2008

Anim Cogn

View full text Add to dashboard Cite

In this study we analyzed the possible contextspeciWc and individual-speciWc features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six diVerent communicative situations was used as the sound sample. The algorithm's task was to learn which acoustic features of the barks, which were recorded in diVerent contexts and from diVerent individuals, could be distinguished from another. The program conducted this task by analyzing barks emitted in previously identiWed contexts by identiWed dogs. After the best feature set had been obtained (with which the highest identiWcation rate was achieved), the eYciency of the algorithm was tested in a classiWcation task in which unknown barks were analyzed. The recognition rates we found were highly above chance level: the algorithm could categorize the barks according to their recorded situation with an eYciency of 43% and with an eYciency of 52% of the barking individuals. These Wndings suggest that dog barks have context-speciWc and individual-speciWc acoustic features. In our opinion, this machine learning method may provide an eYcient tool for analyzing acoustic data in various behavioral studies.

show abstract

References

2011

Central European Stream Ecosystems

View full text Add to dashboard Cite

Modelling population dynamics of aquatic insects with artificial neural networks

Cited by 57 publications

References 19 publications

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Classification of dog barks: a machine learning approach

References

Contact Info

Product

Resources

About