Macrobenthos habitat potential mapping using GIS-based artificial neural network models.
Mar Pollut Bull
; 67(1-2): 177-86, 2013 Feb 15.
Article
en En
| MEDLINE
| ID: mdl-23260647
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Monitoreo del Ambiente
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Redes Neurales de la Computación
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Ecosistema
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Sistemas de Información Geográfica
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Invertebrados
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
Mar Pollut Bull
Año:
2013
Tipo del documento:
Article