KEYWORDS: Landslide (networking), Data mining, Calibration, Data modeling, Hazard analysis, Data analysis, Knowledge discovery, Geographic information systems, Remote sensing, Meteorology
Due to the particular geographical location and geological condition, Taiwan suffers from many natural hazards which
often cause series property damages and life losses. To reduce the damages and casualty, an effective real-time system
for hazard prediction and mitigation is necessary. In this study, a case study for Landslide Hazard Zonation (LHZ) is
tested in accordance with Spatial Data Mining and Knowledge Discovery (SDMKD) from database. Many different
kinds of geospatial data, such as the terrain elevation, land cover types, the distance to roads and rivers, geology maps,
NDVI, and monitoring rainfall data etc., are collected into the database for SDMKD. In order to guarantee the data
quality, the spatial data cleaning is essential to remove the noises, errors, outliers, and inconsistency hiding in the input
spatial data sets. In this paper, the Kriging interpolation is used to calibrate the QPESUMS rainfall data to the rainfall
observations from rain gauge stations to remove the data inconsistency. After the data cleaning, the artificial neural
networks (ANNs) is applied to generate the LHZ map throughout the test area. The experiment results show that the
accuracy of LHZ is about 92.3% with the ANNs analysis, and the landslides induced by heavy-rainfall can be mapped
efficiently from remotely sensed images and geospatial data using SDMKD technologies.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.