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A Geographically Weighted Regression Approach to Landslide Susceptibility Modeling.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Geographically Weighted Regression Approach to Landslide Susceptibility Modeling./
Author:	Matsche, Daniel T.
Description:	1 online resource (85 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-03.
Contained By:	Masters Abstracts International56-03(E).
Subject:	Geography. -
Online resource:	click for full text (PQDT)
ISBN:	9781369538823

A Geographically Weighted Regression Approach to Landslide Susceptibility Modeling.
Matsche, Daniel T.

A Geographically Weighted Regression Approach to Landslide Susceptibility Modeling. - 1 online resource (85 pages)

Source: Masters Abstracts International, Volume: 56-03.

Thesis (M.S.)--University of Idaho, 2017.

Includes bibliographical references

Landslide activity in Oregon causes more than $1 billion in property damage every year, and has resulted in several casualties over the past decades. The steep topography of the region, high-intensity precipitation events during the winter months, and easily weathered parent material, contribute to frequent slope failures in western Oregon. This study conducted a statistical landslide susceptibility assessment to evaluate the effects of geologic, morphologic, physical, and anthropogenic factors on landslide occurrence. Slope, erosion potential, hydrologic soil classes, volcanic and sedimentary geologic material, aspect, and curvature were identified as important predictors. A comparative analysis of traditional logistic regression (LR) and geographically weighted logistic regression (GWLR) was completed for the study area. The regression results from the LR and GWLR models were compared based on AIC, percentage of deviance explained, and prediction accuracy. The outputs demonstrated that GWLR outperformed standard LR in all models. GWLR improved prediction accuracy by 6.2% compared to traditional LR.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369538823Subjects--Topical Terms:

654331
Geography.
Index Terms--Genre/Form:

554714
Electronic books.

A Geographically Weighted Regression Approach to Landslide Susceptibility Modeling.
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245 1 2 $a A Geographically Weighted Regression Approach to Landslide Susceptibility Modeling.
264 0 $c 2017
300 $a 1 online resource (85 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 56-03.
500 $a Adviser: Karen Humes.
502 $a Thesis (M.S.)--University of Idaho, 2017.
504 $a Includes bibliographical references
520 $a Landslide activity in Oregon causes more than $1 billion in property damage every year, and has resulted in several casualties over the past decades. The steep topography of the region, high-intensity precipitation events during the winter months, and easily weathered parent material, contribute to frequent slope failures in western Oregon. This study conducted a statistical landslide susceptibility assessment to evaluate the effects of geologic, morphologic, physical, and anthropogenic factors on landslide occurrence. Slope, erosion potential, hydrologic soil classes, volcanic and sedimentary geologic material, aspect, and curvature were identified as important predictors. A comparative analysis of traditional logistic regression (LR) and geographically weighted logistic regression (GWLR) was completed for the study area. The regression results from the LR and GWLR models were compared based on AIC, percentage of deviance explained, and prediction accuracy. The outputs demonstrated that GWLR outperformed standard LR in all models. GWLR improved prediction accuracy by 6.2% compared to traditional LR.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Geography. $3 654331
655 7 $a Electronic books. $2 local $3 554714
690 $a 0366
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Idaho. $b Geography. $3 1195100
773 0 $t Masters Abstracts International $g 56-03(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10248368 $z click for full text (PQDT)