2016 Fiscal Year Final Research Report
Predictive fish habitat modelling using biotelemetry and computational intelligence
Project/Area Number |
25712026
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Research Category |
Grant-in-Aid for Young Scientists (A)
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Allocation Type | Partial Multi-year Fund |
Research Field |
Rural environmental engineering/Planning
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Research Institution | Tokyo University of Agriculture and Technology |
Principal Investigator |
Fukuda Shinji 東京農工大学, (連合)農学研究科(研究院), 助教 (70437771)
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Project Period (FY) |
2013-04-01 – 2017-03-31
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Keywords | 生態影響解析 / 生態水理 / 環境保全 / 水域ネットワーク / 移動分散 |
Outline of Final Research Achievements |
This project aimed at the application of biotelemtry for fish tracking as well as advanced computational intelligence such as random forests and other machine learning methods for predictive habitat modelling. We established a 430-m study reach in a river flowing through agriculture-dominated landscape in Fukuoka, Japan. Totaled 40 fish individuals, consisted of 4 species, were used as test fish. We successfully obtained movement tracks based on which habitat analyses have been done to identify diurnal changes in spatial distribution of each individual. The high-resolution fish positioning can be used in a small river, contributing to a better understanding and assessment of aquatic ecosystems for sustainable development.
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Free Research Field |
地域環境工学・計画学
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