Abstract
The main purpose of this study was to establish a data-driven approach and assess its potential for shallow landslide susceptibility mapping of the Shennongjia Forestry District, China. For the data processing, Fisher segmenting was used for the classification of topographical factors (gradient, relief amplitude, plan curvature, and normalized difference vegetation index) and the improved Otsu method was used to determine the buffer length thresholds of the locational factors (proximity to roads, rivers and faults). The computations show that these two objective methods help to avoid the uncertainty caused by the subjective judgments and the obtained thresholds are more consistent with actual conditions. Considering the different data types, the locational and topographical factors were evaluated by fuzzy comprehensive evaluation while the geological factors were evaluated by frequency index, and then the three separate evaluations were combined using radar chart analysis. The validation result shows that the susceptibility map based on a data-driven approach has a high accuracy of prediction and this approach will be salutary for risk mitigation and land-use management in mountainous regions.
Highlights
Due to the complex geographical conditions and expanding human construction, landslides often occur during the rainy season in the mountainous regions of central and southern China
The first step is the data processing, which includes the classification of topographic factors by Fisher segmenting and the determination of buffer length thresholds for locational factors by the improved Otsu method
This approach could be roughly divided into a three-step procedure: (i) data processing, which includes the classification of topographic factors by Fisher segmenting and the determination of buffer length thresholds for locational factors by the improved Otsu method; (ii) separate evaluations using fuzzy
Summary
Due to the complex geographical conditions and expanding human construction, landslides often occur during the rainy season in the mountainous regions of central and southern China. Among these landslides, shallow landslides have a wide distribution and occur frequently, which poses serious threats to residents and causes huge economic losses. As the comparison analysis revealed, with the same data processing, there is little difference in the prediction accuracy between the above methods (Melchiorre et al 2008; Zhang et al 2016)
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