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Community Patterning of Benthic Macroinvertebrates in Urbanized Streams by Utilizing an Artificial Neural Network  

Kim, Jwa-Kwan (Department of Environmental Engineering, Catholic University of Pusan)
Chon, Tae-Soo (Division of Biological Sciences, Pusan National University)
Kwak, Inn-Sil (Brain Science Institute, The Institute of Physical and Chemical Research)
Publication Information
Korean Journal of Ecology and Environment / v.36, no.1, 2003 , pp. 29-37 More about this Journal
Abstract
Benthic macro-invertebrates were seasonally collected in the Onchen Stream in Pusan, from July 2001 to March 2002. Generally 4 phylum 5 class 10 order 19 family 23 species were observed in the study sites. Ephemeroptera, Plecoptera and various species appeared in headwater stream while Oligochaeta and Chironomidae were dominated in downstream sites. Community abundance patterns, especially the dominant taxa, Oligochaeta and Chironomidae, appeared to be different depending upon the sampling months. Oligochaeta was usually observed in July, December and March while Chironomidae was appeared in September. The biological indices, TBI(Trent Biotic Index), BS (Biotic Score), BMWP (Biological Monitoring Working Party)were calculated with the appeared communities of the sampling sites through the survey months. TBI showed 1 to 8, BMWP was 1 to 93 and CBI appeared 9 to 387 in the different sites. The biological indices decreased from headstream to downstream sites, We implemented the unsupervised Kohonen network for patterning of community abundance of the sampling sites. The patterning map by the Kohonen network was well represented community abundance of the sampling sites. Also, we conducted RTRN (Real Time Recurrent Neural Network) for predicting of the biological indices in the different sites. The results appeared that the predicting values by RTRN were well matched field data (correlation coefficient of TBI, BMWP and CBI were 0.957, 0.979 and 0.967, respectively).
Keywords
benthic macroinvertebrates; artificial neural network; community pattern; predicting; biological indices;
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