title: Macrobenthos habitat potential mapping using GIS-based artificial neural network models.
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.
authors.affiliation: Geoscience Information Centre, Korea Institute of Geoscience & Mineral Resources (KIGAM), 92, Gwahang-no, Yuseong-gu, Daejeon 305-350, Republic of Korea.
authors.forename: Saro
authors.initials: S
authors.lastname: Lee
authors.forename: Inhye
authors.initials: I
authors.lastname: Park
authors.forename: Bon Joo
authors.initials: BJ
authors.lastname: Koo
authors.forename: Joo-Hyung
authors.initials: JH
authors.lastname: Ryu
authors.forename: Jong-Kuk
authors.initials: JK
authors.lastname: Choi
authors.forename: Han Jun
authors.initials: HJ
authors.lastname: Woo
mesh_terms: D000818:Animals; D059001:Aquatic Organisms / Q000254:growth & development; D017753:Ecosystem; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods*; D040362:Geographic Information Systems; D007448:Invertebrates / Q000254:growth & development*; D016571:Neural Networks, Computer