Landslide susceptibility mapping methodologies for the Kaoping River basin, Taiwan
Date Issued
2015
Date
2015
Author(s)
Huang, Ya-Chiao
Abstract
On average, three to four typhoons attack Taiwan each year. Although typhoon rainfall is an important source of water resources, the heavy rainfall brought by typhoons frequently result in serious disasters. Landslide is one of the most destructive slope disasters. Therefore, to establish a landslide susceptibility model, which can efficiently mitigate the disaster, is always an important task of slope disaster management. In this study, three methods are employed to construct landslide susceptibility models for the Kaoping River basin in southern Taiwan, and then the model performances of these three models are compared. The three methods include the conventional logistic regression (LR) and two novel machine learning methods, namely, Support Vector Machine (SVM) and Improved Self-organizing Linear Output Map (ISOLO). Landslide events from 2008 to 2011 are collected. The first three-year data from 2008 to 2010 are used in the training phase of the models, and the remaining data are for testing. Moreover, fourteen landslide-related factors are used in the landslide susceptibility analysis, such as slope, slope aspect, elevation, curvature, profile curvature, plan curvature, slope length, topographic wetness index, distance to river, distance to road, distance to fault, 24-hour rainfall and 48-hour rainfall. The performances of three models are checked by the accuracy and the area under the receiver operating characteristic curve (AUC). The results show that the ISOLO model outperforms over the LR and SVM models in the study area. Landslide susceptibility maps obtained from the proposed model are expected to be helpful to local administrations and decision makers in disaster planning.
Subjects
Landslide
landslide susceptibility model
Logistic regression
Support Vector Machine
Improved Self-organizing Linear Output Map
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-104-R02521309-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):603d46056264df226408bd5e6d7302b8