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An expert knowledge-based approach t...

The University of Wisconsin - Madison.

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An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic./
Author:	Wang, Rongxun.
Description:	160 p.
Notes:	Adviser: A-Xing Zhu.
Contained By:	Dissertation Abstracts International69-05A.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3314311
ISBN:	9780549634553

An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic.
Wang, Rongxun.

An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. - 160 p.

Adviser: A-Xing Zhu.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2008.

This dissertation presents an expert knowledge-based approach to circumvent the major deficiencies of data-driven approaches to landslide susceptibility mapping. This approach consists of three generic steps: (1) extraction of knowledge on relationships between landslide susceptibility and predisposing factors from domain experts; (2) characterization of predisposing factors using GIS techniques; and (3) prediction of landslide susceptibility under fuzzy logic. The developed approach was tested in two study areas---the Kaixian study area and the Three Gorges study area, China. The Kaixian study area was used to develop the method and to evaluate the validity of the developed approach. The Three Gorges study area was used to test the portability of the developed approach and the applicability of the developed approach for mapping landslide susceptibility over large study areas. The results from the case studies demonstrate that the knowledge-based approach is capable of effectively capturing the landslide susceptibility and that it is a better or more appropriate approach to landslide susceptibility mapping than the statistical approaches. However, it must be pointed out that the accuracy of the predicted landslide susceptibility depends on the sufficiency of the extracted knowledge and the quality of the characterized predisposing factors.

ISBN: 9780549634553Subjects--Topical Terms:

769149
Artificial Intelligence.

An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic.
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005 20100702
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020 $a 9780549634553
035 $a (UMI)AAI3314311
035 $a AAI3314311
040 $a UMI $c UMI
100 1 $a Wang, Rongxun. $3 1020246
245 1 3 $a An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic.
300 $a 160 p.
500 $a Adviser: A-Xing Zhu.
500 $a Source: Dissertation Abstracts International, Volume: 69-05, Section: A, page: 1936.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2008.
520 $a This dissertation presents an expert knowledge-based approach to circumvent the major deficiencies of data-driven approaches to landslide susceptibility mapping. This approach consists of three generic steps: (1) extraction of knowledge on relationships between landslide susceptibility and predisposing factors from domain experts; (2) characterization of predisposing factors using GIS techniques; and (3) prediction of landslide susceptibility under fuzzy logic. The developed approach was tested in two study areas---the Kaixian study area and the Three Gorges study area, China. The Kaixian study area was used to develop the method and to evaluate the validity of the developed approach. The Three Gorges study area was used to test the portability of the developed approach and the applicability of the developed approach for mapping landslide susceptibility over large study areas. The results from the case studies demonstrate that the knowledge-based approach is capable of effectively capturing the landslide susceptibility and that it is a better or more appropriate approach to landslide susceptibility mapping than the statistical approaches. However, it must be pointed out that the accuracy of the predicted landslide susceptibility depends on the sufficiency of the extracted knowledge and the quality of the characterized predisposing factors.
590 $a School code: 0262.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
650 4 $a Geography. $3 524010
650 4 $a Physical Geography. $3 893400
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710 2 $a The University of Wisconsin - Madison. $3 626640
773 0 $t Dissertation Abstracts International $g 69-05A.
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790 1 0 $a Zhu, A-Xing, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3314311