Details
| Original language | English |
|---|---|
| Pages (from-to) | 151-165 |
| Number of pages | 15 |
| Journal | Cartography and Geographic Information Science |
| Volume | 41 |
| Issue number | 2 |
| Publication status | Published - 13 Jan 2014 |
Abstract
In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearsons chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.
Keywords
- chi-square, clustering, hydrographic model generalization, neural networks, selection, self-organizing maps
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Social Sciences(all)
- Geography, Planning and Development
- Business, Management and Accounting(all)
- Management of Technology and Innovation
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In: Cartography and Geographic Information Science, Vol. 41, No. 2, 13.01.2014, p. 151-165.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Model generalization of two different drainage patterns by self-organizing maps
AU - Sen, Alper
AU - Gokgoz, Turkay
AU - Sester, Monika
N1 - Copyright: Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014/1/13
Y1 - 2014/1/13
N2 - In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearsons chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.
AB - In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearsons chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.
KW - chi-square
KW - clustering
KW - hydrographic model generalization
KW - neural networks
KW - selection
KW - self-organizing maps
UR - http://www.scopus.com/inward/record.url?scp=84894063781&partnerID=8YFLogxK
U2 - 10.1080/15230406.2013.877231
DO - 10.1080/15230406.2013.877231
M3 - Article
AN - SCOPUS:84894063781
VL - 41
SP - 151
EP - 165
JO - Cartography and Geographic Information Science
JF - Cartography and Geographic Information Science
SN - 1523-0406
IS - 2
ER -