Landslide Forecasting: Inventory, Susceptibility, and Hazard Analyses
| dc.contributor.advisor | Tanyu, Burak F | |
| dc.contributor.author | Alimohammadlou, Yashar | |
| dc.creator | Alimohammadlou, Yashar | |
| dc.date | 2020-05-07 | |
| dc.date.accessioned | 2021-01-29T13:11:55Z | |
| dc.date.available | 2022-05-07T07:00:20Z | |
| dc.description | This dissertation has been embargoed for 2 years and will not be available until May 2022 at the earliest. | |
| dc.description.abstract | The effects of landslides have been exponentially increasing due to the rapid growth of urbanization and global climate change. However, the methods to evaluate the effects of landslides on very large areas (such as an entire County or State) are still very limited. Forecasting landslides is a complex process, which involves (at a minimum) three different steps. First, inventory maps of the area that documents the extent of the existing landslides must be developed. Second, analyses must be conducted to understand the common features within the areas of landslides. Third, the susceptibility of the areas that may or may not have landslides must be determined at the moment to have the potential to be part of a landslide in the future (identifying the zones of high potential areas). Once the inventory maps and susceptibility analyses are completed, then hazard forecasting analyses must be conducted to evaluate the effects of external (triggering) factors on initiating future landslides both within the areas that had past landslides and no landslides. The research presented in this dissertation focused on studying all of these processes (steps) and provide new methods. The outcome of the research was to create a complete methodology of landslide forecasting that may be used by decision makers (such as Counties, State agencies, insurance companies, etc.) to evaluate large regions and use this information to help communities in terms of the potential dangers that may exist now and in the future as it relates to landslides. The results of this study are utilized to develop a complete and reliable framework of inventory, susceptibility, and hazard analyses. Consequently, the outcome of this research may serve as an early warning system for landslide prone and at-risk regions throughout the world. | |
| dc.identifier.uri | https://hdl.handle.net/1920/11945 | |
| dc.language.iso | en | |
| dc.subject | Landslide | |
| dc.subject | Decision tree | |
| dc.subject | GIS | |
| dc.subject | Inventory analysis | |
| dc.subject | Susceptibility analysis | |
| dc.subject | Hazard analysis | |
| dc.title | Landslide Forecasting: Inventory, Susceptibility, and Hazard Analyses | |
| dc.type | Dissertation | |
| thesis.degree.discipline | Civil and Infrastructure Engineering | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy in Civil and Infrastructure Engineering |