Abstract
Abstract. Spatial information technologies and data can be used effectively to investigate and monitor natural disasters contiguously and to support policy- and decision-making for hazard prevention, mitigation and reconstruction. However, in addition to the vastly growing data volume, various spatial data usually come from different sources and with different formats and characteristics. Therefore, it is necessary to find useful and valuable information that may not be obvious in the original data sets from numerous collections. This paper presents the preliminary results of a research in the validation and risk assessment of landslide events induced by heavy torrential rains in the Shimen reservoir watershed of Taiwan using spatial analysis and data mining algorithms. In this study, eleven factors were considered, including elevation (Digital Elevation Model, DEM), slope, aspect, curvature, NDVI (Normalized Difference Vegetation Index), fault, geology, soil, land use, river and road. The experimental results indicate that overall accuracy and kappa coefficient in verification can reach 98.1% and 0.8829, respectively. However, the DT model after training is too over-fitting to carry prediction. To address this issue, a mechanism was developed to filter uncertain data by standard deviation of data distribution. Experimental results demonstrated that after filtering the uncertain data, the kappa coefficient in prediction substantially increased 29.5%.The results indicate that spatial analysis and data mining algorithm combining the mechanism developed in this study can produce more reliable results for verification and forecast of landslides in the study site.
Highlights
Taiwan has complicated geological conditions, high density of population and other potential factors making it vulnerable to natural hazards
Some factors that can provide advanced information after Spatial Analysis (SA), such as aspect, curvature and slope were derived from DEM; NDVI was produced from original satellite images and normalized with Pseudo Invariant Features (PIFs); distance information about each pixel to the nearest target was generated from GIS poly-lines of rivers and roads
The omission (100%-PA) and commission (100%-UA) of landslides are too high in the prediction phase to obtain acceptable results
Summary
Taiwan has complicated geological conditions, high density of population and other potential factors making it vulnerable to natural hazards. The geological structures have become very fracture after the 1999 Chichi earthquake. Typhoons and other extreme weathers frequently happen in this region. The heavy rainfall often triggers serious landslides and debris flows, and causing human casualties and property damages. The World Bank listed Taiwan as one of the countries that is most vulnerable to natural disasters in the world in terms of lands and population exposing to the danger. To prevent and mitigate natural hazards such as landslides has become an important issue in Taiwan
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