Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium)

Dedecker, Andy; Goethals, Peter; Gabriëls, Wim; Pauw, Niels De

doi:10.1016/j.ecolmodel.2004.01.003

Cited by 93 publications

(51 citation statements)

References 35 publications

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“…Similarly, Dissolved Oxygen (DO) is a variable that in most cases is present to predict the occurrence of macroinvertebrates [51,56,57]. Nonetheless, DO was not a variable of the three GLMs in this research, an observation that was not expected in our research hypothesis.…”

Section: Analysis Of the Explanatory Variables In Relation To Responscontrasting

confidence: 47%

A Methodology to Model Environmental Preferences of EPT Taxa in the Machangara River Basin (Ecuador)

Jerves-Cobo

Everaert

Iñiguez-Vela³

et al. 2017

Water

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Rivers have been frequently assessed based on the presence of the EphemeropteraPlecoptera-Trichoptera (EPT) taxa in order to determine the water quality status and develop conservation programs. This research evaluates the abiotic preferences of three families of the EPT taxa Baetidae, Leptoceridae and Perlidae in the Machangara River Basin located in the southern Andes of Ecuador. With this objective, using generalized linear models (GLMs), we analyzed the relation between the probability of occurrence of these pollution-sensitive macroinvertebrates families and physicochemical water quality conditions. The explanatory variables of the constructed GLMs differed substantially among the taxa, as did the preference range of the common predictors. In total, eight variables had a substantial influence on the outcomes of the three models. For choosing the best predictors of each studied taxa and for evaluation of the accuracy of its models, the Akaike information criterion (AIC) was used. The results indicated that the GLMs can be applied to predict either the presence or the absence of the invertebrate taxa and moreover, to clarify the relation to the environmental conditions of the stream. In this manner, these modeling tools can help to determine key variables for river restoration and protection management.

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Section: Analysis Of the Explanatory Variables In Relation To Responscontrasting

confidence: 47%

A Methodology to Model Environmental Preferences of EPT Taxa in the Machangara River Basin (Ecuador)

Jerves-Cobo

Everaert

Iñiguez-Vela³

et al. 2017

Water

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“…Algunas de las técnicas aplicadas han sido: los árboles de clasificación (Miller y Franklin 2002, Dzeroski y Drumm 2003, Fukuda et al 2013, análisis de correspondencias canónicas (Guisan et al 1999), distintos métodos de regresión (Lehmann et al 2002ay b, Manel et al 2001, Li y Wang 2013, modelos específicos como BIOCLIM (Busby 1986,1991, Aragon et al 2013, FLORAMAP (Jones y Gladkov 1999, Erre et al 2009), o DOMAIN (Walker y Cocks 1991, Ortega-Huerta y Peterson 2008) o técnicas de inteligencia artificial como las redes neuronales (Moisen y Frescino 2002, Dedecker et al 2004, Fukuda et al 2013). …”

Section: Introductionunclassified

Redes bayesianas: una herramienta probabilística en los modelos de distribución de especies

Ropero¹,

Aguilera²,

Fernández³

et al. 2014

ECOS

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“…To determine the response of these models to each individual input variable, a range of variation of a single independent input variable was applied to the model while the other input variables were kept constant. In this manner it was possible to detect the relationship between the input variables and the presence or absence of each taxon (Laë et al 1999, Dedecker et al 2004.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…In ANN models several parameters have to be considered, while adjusting any of the parameters usually influences the predictive capability of the network. Therefore, the optimisation and development of different neural networks is necessary to obtain the best model configuration for a given problem (Dedecker et al 2004). In the present study, the ANNs were built using the machine learning software package WEKA (Witten & Frank 2000).…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…In this context, Artificial Neural Networks (ANNs) have been recognized by several authors as valuable tools for predicting macroinvertebrates (e.g. Walley & Fontama 1998, Schleiter et al 1999, Wagner et al 2000, Hoang et al 2001, Park et al 2001, Arab et al 2004, Dedecker et al 2004, 2005a,b, 2006. On the other hand, ANN models have been labelled as 'black boxes' (Olden & Jackson 2002) because they do not provide direct insight in the habitat preferences of the taxa.…”

Section: Introductionmentioning

confidence: 99%

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Development of artificial neural network models predicting macroinvertebrate taxa in the river Axios (Northern Greece)

Dakou

Goethals

D'Heygere

et al. 2006

Ann. Limnol. - Int. J. Lim.

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Artificial Neural Network models (ANNs) were used to predict habitat suitability for 12 macroinvertebrate taxa, using environmental input variables. This modelling technique was applied to a dataset of 102 measurement series collected in 31 sampling sites in the Greek river Axios. The database consisted of seven physical-chemical and seven structural variables, as well as abundances of 90 macroinvertebrate taxa. A seasonal variable was included to allow the description of potential temporal changes in the macroinvertebrate communities. The induced models performed well for predicting habitat suitability of the macroinvertebrate taxa. Senso-nets and sensitivity analyses revealed that dissolved oxygen concentration and the substrate composition always played a crucial role in predicting habitat suitability of the macroinvertebrates. Although ANNs are often referred to as black box prediction techniques, it was demonstrated that ANNs combined with sensitivity analyses can provide insight in the relationship between river conditions and the occurrence of macroinvertebrates, and thus deliver new ecological knowledge. Consequently, these models can be useful in decision-making for river restoration and conservation management.

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Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium)

Cited by 93 publications

References 35 publications

A Methodology to Model Environmental Preferences of EPT Taxa in the Machangara River Basin (Ecuador)

A Methodology to Model Environmental Preferences of EPT Taxa in the Machangara River Basin (Ecuador)

Redes bayesianas: una herramienta probabilística en los modelos de distribución de especies

Development of artificial neural network models predicting macroinvertebrate taxa in the river Axios (Northern Greece)

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