Abstract
This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.
Highlights
Typhoon Morakot made landfall in Taiwan on 8 August in 2009
Chiang et al (2012) mentioned that the original DEM contained surface irregularities caused by isolated tree heights and discontinuous streams
Chiang et al (2012) manually edited the original DEM according to the hillslope and slope maps
Summary
Typhoon Morakot made landfall in Taiwan on 8 August in 2009. The heavy rainfall induced catastrophic landslides as well as debris flows. Several studies have concentrated on detecting (e.g. Mondidi and Chang, 2014), characterizing (e.g. Tsai et al, 2010) and modelling (e.g. Chang et al, 2014) a catastrophic landslide event over Xiaolin (or Shiaolin, Hsiaolin) village and Kaoping watershed in southern Taiwan in order to prevent and mitigate similar disaster effects in the future. GIS-based models with geo-spatial data (van Westen et al, 2008; Wang et al, 2005) and event-based landslide inventory (Guzzetti et al, 2012; Lee et al, 2008) have been emphasized and discussed in recent years. Spectral and temporal resolutions of remote sensing imageries, landslide areas can be automatically or semi-automatically detected during a single triggering event using pixel-based (e.g. Mondidi and Chang, 2014) and object-oriented (e.g. Wang and Niu, 2009) strategies for generating landslide inventories. Event-based landslide susceptibility analysis can be conducted for further assessing landslide hazard, vulnerability and risk (Guzzetti et al, 2012)
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