Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China

نویسندگان

چکیده

Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a assessment using multiple logistic regression (MLR) algorithm. Field-investigated landslides non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points intersect these polygons the intersected divided two parts, training set (TS) validation (VT) ratio 7 3. Thirteen geo-environmental factors, including elevation, slope, distance from roads employed hazard-causative which by TS create random point (RP)-based dataset. The next step was compute certainty factor (CF) each constitute CF-based MLR applied datasets modeling. probability then calculated pixel, maps produced. overall accuracy models versus VS 91.5% 90.4% with Kappa coefficient 0.814 0.782, respectively. RP-based modeling achieved more reliable predictions its map seems plausible providing technical support implementing disaster prevention measures Guixi.

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ژورنال

عنوان ژورنال: Sustainability

سال: 2021

ISSN: ['2071-1050']

DOI: https://doi.org/10.3390/su13094830