Using spatial point process to identify the influential factors for causing landslides in Shimen Reservoir Catchment Area
| Autor: | CHENG, WEI-CHIN, 鄭維晉 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Taiwan is located in the earthquake zone and the earthquake occurs frequently. Since Taiwan have many more than 1000-meters mountains, the river has the steep slope. This special topography and the interaction between earthquake and heavy rainfall cause the upstream region of the catchment area becomes fragile and easy to collapse and the river channel continues to withstand erosion. Thereby the life of the reservoir is affected. The Shimen Reservoir is one of the major reservoirs in northern Taiwan. If the area with high collapse potential in the catchment area can be found and prevent effectively early, it will not only prolong the life of the reservoir, but also reduce the government's fiscal expenditure. This thesis uses the intensity function of the point process to model the landslide event points in the catchment area of Shimen Reservoir and identify potential risk factors associated with the landslide event. Firstly, the quadrate counting test is used to examine the spatial point pattern in the catchment area of Shimen Reservoir. Secondly, the summary functions of the point process including K-function, pair correlation function, F-function and G-function, are used to investigate the clustering effect in the point process when the process is not complete spatial randomness. Thirdly, Log-Gaussian Cox Process (LGCP), Thomas Cluster Process, the Matérn Cluster Process are used to model the landslide event data in the study region. This research find that the study region is not complete spatial randomness. Based on 4 summary functions, we further find the clustering effect exists in the study region. 3 clustering process are then used to model the clustering effect in the landslide event data in the catchment area of Shimen Reservoir. Among them, we find that LGCP has the best fit. The result of this research can be used to identify the high risk cluster in the study area. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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