Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity
AbstractIn this paper, we propose a methodology for validating landslide susceptibility results in the Pinggu district (Beijing, China). A landslide inventory including 169 landslides was prepared, and eight factors correlated to landslides (lithology, tectonic faults, topographic elevation, slope gradient, aspect, slope curvature, land use, and road network) were processed, integrating two techniques, namely the frequency ratio (FR) and the certainty factor (CF), in a geographic information system (GIS) environment. The area under the curve (success rate curve and prediction curve) analysis was used to evaluate model compatibility and predictability. Validation results indicated that the values of the area under the curve for the FR model and the CF model were 0.769 and 0.768, respectively. Considering spatial correlation, an alternative complementary method for validating landslide susceptibility maps was introduced. The spatially approximate maps could be discriminated from their matrices which carry structural information, and the structural similarity index (SSI) was then proposed to quantify the similarity. As a specific example, the SSI value of the FR (74.15%) scored higher than that of the CF model (69.36%), demonstrating its promise in validating different landslide susceptibility maps. These results show that the FR model outperforms the CF model in producing a landslide susceptibility map in the study area. View Full-Text
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Deng, X.; Li, L.; Tan, Y. Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity. ISPRS Int. J. Geo-Inf. 2017, 6, 103.
Deng X, Li L, Tan Y. Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity. ISPRS International Journal of Geo-Information. 2017; 6(4):103.Chicago/Turabian Style
Deng, Xiaolong; Li, Lihui; Tan, Yufang. 2017. "Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity." ISPRS Int. J. Geo-Inf. 6, no. 4: 103.
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