2019 CSDMS meeting-057: Difference between revisions
(Created page with "{{CSDMS meeting personal information template-2019 |CSDMS meeting first name=Erkan |CSDMS meeting last name=Istanbulluoglu |CSDMS meeting institute=University of Washington |C...") |
Eistanbull (talk | contribs) No edit summary |
||
| Line 25: | Line 25: | ||
}} | }} | ||
{{CSDMS meeting abstract yes no 2019 | {{CSDMS meeting abstract yes no 2019 | ||
|CSDMS meeting abstract submit= | |CSDMS meeting abstract submit=Yes | ||
}} | |||
{{CSDMS meeting abstract title temp2019 | |||
|CSDMS meeting abstract title=A new approach to mapping landslide hazards: a probabilistic integration of empirical and process-based models | |||
}} | |||
{{CSDMS meeting authors template | |||
|CSDMS meeting coauthor first name abstract=Ronda | |||
|CSDMS meeting coauthor last name abstract=Strauch | |||
|CSDMS meeting coauthor institute / Organization=Seattle City Lights | |||
|CSDMS meeting coauthor town-city=Seattle | |||
|CSDMS meeting coauthor country=United States | |||
|State=Washington | |||
|CSDMS meeting coauthor email address=Ronda.Strauch@seattle.gov | |||
}} | |||
{{CSDMS meeting authors template | |||
|CSDMS meeting coauthor first name abstract=Jon | |||
|CSDMS meeting coauthor last name abstract=Riedel | |||
|CSDMS meeting coauthor institute / Organization=National Park Service | |||
|CSDMS meeting coauthor town-city=Sedro-Woolley | |||
|CSDMS meeting coauthor country=United States | |||
|State=Washington | |||
|CSDMS meeting coauthor email address=Jon_Riedel@nps.gov | |||
}} | |||
{{CSDMS meeting abstract template 2019 | |||
|CSDMS meeting abstract=A new approach for mapping landslide hazard is developed by combining probabilities of landslide probability derived from a data-driven statistical approach and process-based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes on observed landslides using a frequency ratio method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. For each landslide type the frequency ration (FR) classification is converted into a Stability Index (SI), mapped across our study domain in the North Cascades National Park, WA. Using distributed landslide observations a continuous function is developed to relate local SI values to landslide probability. This probability is combined with spatially distributed probability of landsliding obtained from Landlab using a two-dimensional binning method that employs empirical and modeled based probabilities as indices and calculates empirical probability of landsliding at the intersections of bin ranges of the empirical and process-based probability domains. Based on this we developed a probabilistic correction factor to modeled local landslide probability. Improvements in distinguishing potantially unstable domain with the proposed model is quantified statistically. | |||
}} | }} | ||
{{blank line template}} | {{blank line template}} | ||
Latest revision as of 23:57, 2 April 2019
Log in (or create account for non-CSDMS members)
Forgot username? Search or email:CSDMSweb@colorado.edu
Browse abstracts
A new approach to mapping landslide hazards: a probabilistic integration of empirical and process-based models
A new approach for mapping landslide hazard is developed by combining probabilities of landslide probability derived from a data-driven statistical approach and process-based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes on observed landslides using a frequency ratio method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. For each landslide type the frequency ration (FR) classification is converted into a Stability Index (SI), mapped across our study domain in the North Cascades National Park, WA. Using distributed landslide observations a continuous function is developed to relate local SI values to landslide probability. This probability is combined with spatially distributed probability of landsliding obtained from Landlab using a two-dimensional binning method that employs empirical and modeled based probabilities as indices and calculates empirical probability of landsliding at the intersections of bin ranges of the empirical and process-based probability domains. Based on this we developed a probabilistic correction factor to modeled local landslide probability. Improvements in distinguishing potantially unstable domain with the proposed model is quantified statistically.
