Data Mining For Knowledge-Based Approach for Landslide Susceptibility Mapping
Abstract
Statistical approaches to landslide susceptibility mapping can be an effective tool for
determining areas of high risk, but have also had problems with portability, reliability, stability,
and impracticality as they often require large amounts of data in order to be effective. The
knowledge based approach has been proven to address these issues, but the necessary preparation
required for obtaining domain knowledge on landslide susceptibility and predisposing factors
demands time and effort from the analyst. This paper proposes a knowledge based data mining
approach: data mining techniques for defining the knowledge on the relationship between
landslide susceptibility and predisposing factors and the knowledge based approach for mapping
landslide susceptibility. A set of environmental clusters were generated through fuzzy
classification of key environmental layers describing spatial variation of predisposing factors.
These clusters were then hardened and later ranked according to density of landslides. The
cluster centroid values are ordered in the order of landslide density for hardened classes and used
as control points for the construction of fuzzy membership functions on knowledge of
relationships between landslide susceptibility and predisposing factors. The fuzzy membership
functions are then used through a knowledge-based approach to map landslide susceptibility.
The accuracy of the so generated map in the Kaixian area is comparable with that from a logistic
regression model. However, the generated fuzzy membership functions are more portable than
those in the logistic model. It was also found that the combination layer was correlated strongly
with landslide instances, and could serve as a useful input for future studies. The results of this
study provide a useful illustration for potential in the construction of fuzzy membership maps
from field observation data.
Subject
Data mining
Landslide susceptibility mapping
Kaixian area
Permanent Link
http://digital.library.wisc.edu/1793/78912Type
Thesis
Description
Includes Equations, Figures, Imagery, Graphs, Tables, Maps and Bibliography.