Abstract
Landslide susceptibility map** is significant for landslide prevention. Many approaches have been used for landslide susceptibility prediction, however, their performances are unstable. This study constructed a hybrid model, namely box counting dimension-based kernel logistic regression model, which uses fractal dimension calculated by box counting method as input data based on grid cells map** unit and terrain map** unit. The performance of this model was evaluated in the application in Zhidan County, Shaanxi Province, China. Firstly, a total of 221 landslides were identified and mapped, and 11 landslide predisposing factors were considered. Secondly, the landslide susceptibility maps (LSMs) of the study area were obtained by constructing the model on two different map** units. Finally, the results were evaluated with five statistical indexes, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and Accuracy. The statistical indexes of the model obtained on the terrain map** unit were larger than those based on grid cells map** unit. For training and validation datasets, the area under the receiver operating characteristic curve (AUC) of the model based on terrain map** unit were 0.9374 and 0.9527, respectively, indicating that establishing this model on the terrain map** unit was advantageous in the study area. The results show that the fractal dimension improves the prediction ability of the kernel logistic model. In addition, the terrain map** unit is a more promising map** unit in Loess areas.
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Acknowledgements
The research reported in this manuscript was funded by National Key Research and Development Program of China, Ecological Safety Guarantee Technology and Demonstration Channel and Slope Treatment Project in Loess Hilly and Gully Area (Grant No. 2017YFC0504700).
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Zhang, Ty., Mao, Za. & Wang, T. GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different map** units. J. Mt. Sci. 17, 2929–2941 (2020). https://doi.org/10.1007/s11629-020-6393-8
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DOI: https://doi.org/10.1007/s11629-020-6393-8