dc.contributor.author	Mirnazari, Javad
dc.date.accessioned	2023-04-19T01:05:25Z
dc.date.available	2023-04-19T01:05:25Z
dc.date.issued	2015-06
dc.description	Thesis (Ph.D (Remote Sensing))
dc.description.abstract	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
dc.description.sponsorship	Faculty of Geoinformation and Real Estate
dc.identifier.uri	http://openscience.utm.my/handle/123456789/240
dc.language.iso	en
dc.publisher	Universiti Teknologi Malaysia
dc.subject	Geoinformation and real estate
dc.subject	Environment
dc.title	Evaluation of different techniques for generating landslide susceptibility map
dc.type	Thesis
dc.type	Dataset

Evaluation of different techniques for generating landslide susceptibility map

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