This study aims to develop a Geographic Information Systems (GIS) and Remote
Sensing (RS) Based systematic quantitative landslide risk assessment methodology
for regional and local scales. Each component of risk, i.e., hazard assessment,
vulnerability, and consequence analysis, is quantitatively assessed for both scales.
The developed landslide risk assessment methodology is tested at Kumluca
watershed, which covers an area of 330 km2, in Bartin province of the Western Black
Sea Region, Turkey.
GIS and RS techniques are used to create landslide factor maps, to obtain
susceptibility maps, hazard maps, elements at risk and risk maps, and additionally to
compare the obtained maps.
In this study, the effect of mapping unit and mapping method upon susceptibility
mapping method, and as a result the effect upon risk map, is evaluated. Susceptibility
maps are obtained by using two different mapping units, namely slope unit-based and grid-based mapping units. When analyzing the effect of susceptibility mapping
method, this study attempts to extend Logistic Regression (LR) and Artificial Neural
Network (ANN) by implementing Geographically-Weighted Logistic Regression
(GWR) and spatial regression (SR) techniques for landslide susceptibility
assessment.
In addition to spatial probability of occurrence of damaging events, landslide hazard
calculation requires the determination of the temporal probability. Precipitation
triggers the majority of landslides in the study region. The critical rainfall thresholds
were estimated by using daily and antecedent rainfalls and landslide occurrence dates
based on three different approaches: Time Series, Gumble Distribution and Intensity
Duration Curves.
Different procedures are adopted to obtain the element at risk values and
vulnerability values for local and regional scale analyses. For regional scale analysis,
the elements at risk were obtained from existing digital cadastral databases and
vulnerabilities are obtained by adopting some generalization approaches. On the
other hand, on local scale the elements at risk are obtained by high resolution remote
sensing images by the developed algorithms in an automatic way.
It is found that risk maps are more similar for slope unit-based mapping unit than
grid&ndash / based mapping unit.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/3/12611314/index.pdf |
| Date | 01 December 2009 |
| Creators | Erener, Arzu |
| Contributors | Duzgun, Sebnem |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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