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.
Park
Young-Seuk
YS
LADYBIO, CNRS-University Paul Sabatier, 118 Route de Narbonne, 31062 Toulouse, France. park@cict.fr
Chon
Tae-Soo
TS
Kwak
Inn-Sil
IS
Lek
Sovan
S
D000818:Animals; D017753:Ecosystem; D004784:Environmental Monitoring; D004787:Environmental Pollution; D005843:Geography; D007448:Invertebrates / Q000145:classification*; D007723:Korea; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D045483:Rivers; D013997:Time Factors