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한국생태학회 / v.23, no.2, 2000년, pp.89-100 
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( Pattern Recognition of Long-term Ecological Data in Community Changes by Using Artificial Neural Networks: Benthic Macroinvertebrates and Chironomids in a Polluted Stream ) |
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| 초 록 |
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| On community data. sampled in regular intervals on a long-term basis. artificial neural networks were implemented to extract information on characterizing patterns of community changes. The Adaptive Resonance Theory and Kohonen Network were both utilized in learning benthic macroinvertebrate communities in the Soktae Stream of the Suyong River collected monthly for three years. Initially, by regarding each monthly collection as a separate sample unit, communities were grouped into similar patterns after training with the networks. Subsequently, changes in communities in a sequence of samplings (e.g., two-month, four-month, etc.) were given as input to the networks. After training, it was possible to recognize new data set in line with the sampling procedure. Through the comparative study on benthic macroinvertebrates with these learning processes, patterns of community changes in chironomids diverged while those of the total benthic macro-invertebrates tended to be more stable. |
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| 키워드 |
| Adaptive Resonance Theory;Artificial neural network;Benthic macroinvertebrates, Chironomids;Kohonen network;Patterning community changes; |
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The Korean Journal of Ecology / v.23, no.2, 2000년, pp.89-100
한국생태학회
ISSN : 1225-0317
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO200011921335995)
언어 : 영어 |
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| 논문 제공 : KISTI 한국과학기술정보연구원 |
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