Hazards analysis and prediction from remote sensing and GIS using spatial data mining and knowledge discovery: a case study for landslide hazard zonation

Pai-Hui Hsu; Wen-Ray Su; Chy-Chang Chang

doi:10.1117/12.897997

26 October 2011 Hazards analysis and prediction from remote sensing and GIS using spatial data mining and knowledge discovery: a case study for landslide hazard zonation

Pai-Hui Hsu, Wen-Ray Su, Chy-Chang Chang

Author Affiliations +

Proceedings Volume 8181, Earth Resources and Environmental Remote Sensing/GIS Applications II; 81810R (2011) https://doi.org/10.1117/12.897997
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

Abstract

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.

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Pai-Hui Hsu, Wen-Ray Su, and Chy-Chang Chang "Hazards analysis and prediction from remote sensing and GIS using spatial data mining and knowledge discovery: a case study for landslide hazard zonation", Proc. SPIE 8181, Earth Resources and Environmental Remote Sensing/GIS Applications II, 81810R (26 October 2011); https://doi.org/10.1117/12.897997

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KEYWORDS

Landslide (networking)

Data mining

Calibration

Data modeling

Data analysis

Hazard analysis

Knowledge discovery

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