Biomedical subjects

Young-Seuk Park

Publications and source records attributed to Young-Seuk Park.

4 recordsLinked to original sources

PubMed2006

Stream fish assemblages and basin land cover in a river network.

This study focused on characterizing fish assemblages in the Adour-Garonne basin and identifying the relative influences of landscape-scale features on observed patterns in stream fish assemblages. Two different artificial neural network algorithms were used: a self-organizing map (SOM) and a multilayer perceptron (MLP). A SOM was applied to determine fish assemblage types, and a MLP was used to predict the fish assemblage types defined by the SOM. Thirty four species were collected at 191 sampling sites in a major river-system, the Adour-Garonne basin, and topographical factors, namely altitude, distance from source and surface area of drainage basin were measured. Using GIS, land cover types (agricultural land, forests and urbanized artificial surface) were calculated for each site and expressed as percentage of the surface area of basin. These variables were introduced to the MLP and factorial discriminant analysis for the prediction of assemblage types. As a result, the SOM distinguished three fish assemblage types according to the differences of species composition, and the assemblage types were better predicted with landscape-scale features by MLP than discriminant analysis. The percentages of agricultural land and the surface area of a basin showed the greatest influence on assemblage types 1 and 2, and distance from source was the most important factor to determine assemblage type 3.

Agriculture↗

PubMed2005

Computational characterization of behavioral response of medaka (Oryzias latipes) treated with diazinon.

The behavior of indicator specimens in response to sub-lethal doses of toxic substances has been used to detect contamination in aquatic ecosystems. Changes in the movement behaviors of medaka (Oryzias latipes) were analyzed after being treated with diazinon at a concentration of 0.1 mg/l. The movement tracks of medaka were continuously recorded in two-dimension by a digital image processing system both before and after the treatments. Subsequently, two computational methods--two-dimensional fast Fourier transform (2D FFT) and self-organizing map (SOM), were implemented to extract information from the movement data. The differences in the shapes of the movement tracks before and after the treatments were clearly manifested through 2D FFT. The short-distance, irregular turnings in the movement tracks observed after the treatments in the time domain were characteristically transformed to circular or ellipsoidal patterns in the frequency domain. The amplitudes of 2D FFT were efficiently classified by SOM, demonstrating the effects of the different treatments. To evaluate the feasibility of information extraction by 2D FFT, SOM was similarly carried out on the parameters (speed, meander, stop duration, etc.) conventionally used for characterizing the movement tracks. 2D FFT was more efficient in information extraction from the movement data than the parameters. The 2D FFT and SOM were useful as computational methods for automatically detecting response behaviors of indicator specimens exposed to toxic chemicals in aquatic ecosystems.

Animals↗

PubMed2004

Hierarchical community classification and assessment of aquatic ecosystems using artificial neural networks.

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) was subsequently applied to the SOM to further classify the groups in different scales. Hierarchical grouping in community data efficiently reflected the impact of the environmental factors such as topographic conditions, levels of pollution, and sampling location and time across different scales. New community data not included in the training process were used to test the trained network model. The input data were appropriately grouped at different hierarchical levels by the trained networks, and correspondingly revealed the impact of environmental disturbances and temporal dynamics of communities. The hierarchical clusters based on a two-level classification method could be useful for assessing ecosystem quality and community variations caused by environmental disturbances.

Animals↗

PubMed2003

Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network.

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 the CPN, the sampling sites were classified into five groups and the classification was mainly related to pollution status and habitat type of the sampling sites. By visualizing environmental variables and diversity indices on the map of the trained model, the relationships between variables were evaluated. The trained CPN serves as a 'look-up table' for finding the corresponding values between environmental variables and community indices. The output of the model fitted SH and SR well showing a high accuracy of the prediction (r>0.90 and 0.67 for learning and testing process, respectively) for both SH and SR. Finally, the results of this study, which uses the capability of the CPN for patterning and predicting ecological data, suggest that the CPN can be effectively used as a tool for assessing ecological status and predicting water quality of target ecosystems.

Animals↗