Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13150
Title: A data-mining approach to determine the spatio-temporal relationship between environmental factors and fish distribution
Authors: Su, F
Zhou, C
Lyne, V
Du, Y
Shi, W 
Keywords: Ecological association rule
Fish assembling
Fish distribution
Geographical Information System(GIS)
Spatiotemporal assignment
Issue Date: 2004
Source: Ecological modelling, 2004, v. 174, no. 4, p. 421-431 How to cite?
Journal: Ecological Modelling 
Abstract: The interaction between environmental factors and the spatiotemporal dynamics of living organism is an important aspect in ecology. We describe here a data-mining approach - the spatiotemporal assignment mining model (STAMM) - to extract the spatiotemporal pattern, or assignment of environmental factors, which control the distribution of a living organism. In STAMM, the spatiotemporal assignment of environmental factors is expressed via neighbourhood rules which will reflect the fuzzy or uncertain prior knowledge about the relationship. The values of cells or points in the neighbourhood and the relationships are used to construct a decision table. Indices expressing the probabilities of the ecological association rules are recursively processed in order to determine the spatiotemporal assignment. These rules are objective assessments of our prior knowledge and they refine our knowledge and understanding of the ecosystem. As a case study, we used this model to study the temperature pattern which controls the assembling of fish in the Dasha area of the Yellow Sea in China.
URI: http://hdl.handle.net/10397/13150
ISSN: 0304-3800
DOI: 10.1016/j.ecolmodel.2003.10.006
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

41
Last Week
0
Last month
0
Citations as of Mar 22, 2019

WEB OF SCIENCETM
Citations

28
Last Week
0
Last month
1
Citations as of Mar 23, 2019

Page view(s)

87
Last Week
1
Last month
Citations as of Mar 21, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.