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Artificial neural networks (ANNs) are non-linear mapping structures based on the function of the human brain. They have been shown to be universal and highly flexible function approximators for any data. These make powerful tools for models, especially when the underlying data relationships are unknown. In this reason, the international workshop on the(More)
Processes governing patterns of richness of riverine fish species at the global level can be modelled using artificial neural network (ANN) procedures. These ANNs are the most recent development in computer-aided identification and are very different from conventional techniques 1,2. Here we use the potential of ANNs to deal with some of the persistent(More)
The aim of the present work is to propose a model for the estimation of lead concentration in grasses using urban descriptors easily accessible and to study the specific effect of each descriptor on lead concentration. Six descriptors were considered: the density of vegetation, the vegetation height, wind velocity, height of building, distance of adjacent(More)
The present work describes a comparison of the ability of multiple linear regression (MLR) and artificial neural networks (ANN) to predict fish spatial occupancy and abundance in a mesotrophic reservoir. Models were run and tested with 306 observations obtained by the sampling point abundance method using electrofishing. For each of the 306 samples, the(More)
Habitat fragmentation affects the integrity of many species, but little is known about species-specific sensitivity to fragmentation. Here, we compared the genetic structure of four freshwater fish species differing in their body size (Leuciscus cephalus; Leuciscus leuciscus; Gobio gobio and Phoxinus phoxinus) between a fragmented and a continuous(More)
Ecological communities consist of a large number of species. Most species are rare or have low abundance, and only a few are abundant and/or frequent. In quantitative community analysis, abundant species are commonly used to interpret patterns of habitat disturbance or ecosystem degradation. Rare species cause many difficulties in quantitative analysis by(More)
A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers, canals, ditches, lakes, and pools in The Netherlands. By training(More)
Benthic macroinvertebrate communities in stream ecosystems were assessed hierarchically through two-level classification methods of unsupervised learning. Two artificial neural networks were implemented in combination. Firstly, the self-organizing map (SOM) was used to reduce the dimension of community data, and secondly, the adaptive resonance theory (ART)(More)
One of the most fundamental feature of freshwater systems is the spatiotemporal structure of their communities. In the present study, we used an artificial neural network model, i.e., self-organizing mapping, together with a likelihood ratio χ 2 statistic for proportions to investigate the influence of each factor of a complex sampling scheme (i.e., site,(More)