Evaluation of different techniques for generating landslide susceptibility map

Landslide is a complex natural phenomenon, which may cause loss of lives and properties around the world. In Iran, for example, most landslide occurrences are shallow, and mainly occur around the western and northern parts of the country. In particular, the Cheshme Kabud rural district, which is loc...

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Main Author:	Mirnazari, Javad
Format:	Thesis
Language:	English
Published:	2015
Subjects:	G70.39-70.6 Remote sensing
Online Access:	http://eprints.utm.my/id/eprint/54827/1/JavadMirnazariPFGHT2015.pdf
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spelling	my-utm-ep.548272020-11-09T05:10:49Z Evaluation of different techniques for generating landslide susceptibility map 2015-06 Mirnazari, Javad G70.39-70.6 Remote sensing Landslide is a complex natural phenomenon, which may cause loss of lives and properties around the world. In Iran, for example, most landslide occurrences are shallow, and mainly occur around the western and northern parts of the country. In particular, the Cheshme Kabud rural district, which is located in the western part of Iran, is a region of frequent landslide occurrence as a consequence of inherent and triggering factors. As such, this study seeks to assess the accuracy of the different methods used to generate landslide susceptibility maps. This study also aims to predict the landslide extension to the existing areas in the future. The methods used for the generation of landslide susceptibility maps in the study were Moderation, Artificial Neural Network (ANN) and regressions (logistic, spatial and Geographically Weighted Regression (GWR)). Extension of the existing landslide areas was predicted using Geographically Altitudinal Weighted Regression (GAWR) method. In this study, GeoEye-1 and IKONOS satellite images were used for providing landslide inventory. Nine landslide conditioning factors namely slope, aspect, landuse, lithology, soil type, erosion, distance to roads, distance to rivers, and distance to faults were considered in the analysis. In Moderation method, all the classes of factors were weighted. In this way, the final weighted classes generated a landslide susceptibility map of the Chesme Kabud rural district. The lack of weather stations in the study area posed a significant limitation to the data collection, considering the effect of rain on landslide susceptibility mapping in the area for all the methods. By validating the three methods using the receiver operating characteristic (ROC) technique, the result showed that the Moderation method showed the best performance with a 95% prediction accuracy. The result of the GAWR indicates that, in general, the areas of small landslides will experience more extension than larger landslides in the future. 2015-06 Thesis http://eprints.utm.my/id/eprint/54827/ http://eprints.utm.my/id/eprint/54827/1/JavadMirnazariPFGHT2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:95655 phd doctoral Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate Faculty of Geoinformation and Real Estate
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	G70.39-70.6 Remote sensing
spellingShingle	G70.39-70.6 Remote sensing Mirnazari, Javad Evaluation of different techniques for generating landslide susceptibility map
description	Landslide is a complex natural phenomenon, which may cause loss of lives and properties around the world. In Iran, for example, most landslide occurrences are shallow, and mainly occur around the western and northern parts of the country. In particular, the Cheshme Kabud rural district, which is located in the western part of Iran, is a region of frequent landslide occurrence as a consequence of inherent and triggering factors. As such, this study seeks to assess the accuracy of the different methods used to generate landslide susceptibility maps. This study also aims to predict the landslide extension to the existing areas in the future. The methods used for the generation of landslide susceptibility maps in the study were Moderation, Artificial Neural Network (ANN) and regressions (logistic, spatial and Geographically Weighted Regression (GWR)). Extension of the existing landslide areas was predicted using Geographically Altitudinal Weighted Regression (GAWR) method. In this study, GeoEye-1 and IKONOS satellite images were used for providing landslide inventory. Nine landslide conditioning factors namely slope, aspect, landuse, lithology, soil type, erosion, distance to roads, distance to rivers, and distance to faults were considered in the analysis. In Moderation method, all the classes of factors were weighted. In this way, the final weighted classes generated a landslide susceptibility map of the Chesme Kabud rural district. The lack of weather stations in the study area posed a significant limitation to the data collection, considering the effect of rain on landslide susceptibility mapping in the area for all the methods. By validating the three methods using the receiver operating characteristic (ROC) technique, the result showed that the Moderation method showed the best performance with a 95% prediction accuracy. The result of the GAWR indicates that, in general, the areas of small landslides will experience more extension than larger landslides in the future.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mirnazari, Javad
author_facet	Mirnazari, Javad
author_sort	Mirnazari, Javad
title	Evaluation of different techniques for generating landslide susceptibility map
title_short	Evaluation of different techniques for generating landslide susceptibility map
title_full	Evaluation of different techniques for generating landslide susceptibility map
title_fullStr	Evaluation of different techniques for generating landslide susceptibility map
title_full_unstemmed	Evaluation of different techniques for generating landslide susceptibility map
title_sort	evaluation of different techniques for generating landslide susceptibility map
granting_institution	Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate
granting_department	Faculty of Geoinformation and Real Estate
publishDate	2015
url	http://eprints.utm.my/id/eprint/54827/1/JavadMirnazariPFGHT2015.pdf
_version_	1747817736527937536

Evaluation of different techniques for generating landslide susceptibility map

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