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http://dx.doi.org/10.5141/JEFB.2005.28.6.405

Machine Learning Application to the Korean Freshwater Ecosystems  

Jeong, Kwang-Seuk (Department of Biology, Pusan National University)
Kim, Dong-Kyun (Department of Biology, Pusan National University)
Chon, Tae-Soo (Department of Biology, Pusan National University)
Joo, Gea-Jae (Department of Biology, Pusan National University)
Publication Information
The Korean Journal of Ecology / v.28, no.6, 2005 , pp. 405-415 More about this Journal
Abstract
This paper considers the advantage of Machine Learning (ML) implemented to freshwater ecosystem research. Currently, many studies have been carried out to find the patterns of environmental impact on dynamics of communities in aquatic ecosystems. Ecological models popularly adapted by many researchers have been a means of information processing in dealing with dynamics in various ecosystems. The up-to-date trend in ecological modelling partially turns to the application of ML to explain specific ecological events in complex ecosystems and to overcome the necessity of complicated data manipulation. This paper briefly introduces ML techniques applied to freshwater ecosystems in Korea. The manuscript provides promising information for the ecologists who utilize ML for elucidating complex ecological patterns and undertaking modelling of spatial and temporal dynamics of communities.
Keywords
Artificial neural networks; Ecological models; Evolutionary computation; Fuzzy logic; Interdisciplinary research; Korean freshwater ecosystems; Machine learning;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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