Macrobenthos habitat potential mapping using GIS-based artificial neural network models
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Title
- Macrobenthos habitat potential mapping using GIS-based artificial neural network models
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Author(s)
- Lee, Saro; Park, Inhye; Koo, Bon Joo; Ryu, Joo-Hyung; Choi, Jong-Kuk; Woo, Han Jun
- KIOST Author(s)
- Koo, Bon Joo(구본주); Ryu, Joo Hyung(유주형); Choi, Jong Kuk(최종국); Woo, Han Jun(우한준)
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Alternative Author(s)
- 구본주; 유주형; 최종국; 우한준
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Publication Year
- 2013-02-15
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Abstract
- This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats. (c) 2012 Published by Elsevier Ltd.
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ISSN
- 0025-326X
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URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/3226
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DOI
- 10.1016/j.marpolbul.2012.10.023
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Bibliographic Citation
- MARINE POLLUTION BULLETIN, v.67, no.1-2, pp.177 - 186, 2013
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Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
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Subject
- LANDSLIDE SUSCEPTIBILITY; TIDAL FLATS; USA; CLASSIFICATION; REPLACEMENT; SCALE; KOREA; RIVER; BAY
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Keywords
- Habitat mapping; Tidal flat; Artificial neural network; Geographic information system (GIS); Remote sensing
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Type
- Article
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Language
- English
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Document Type
- Article
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