Comparison of landslide susceptibility maps using random forest and multivariate adaptive regression spline models in combination with catchment map units View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Lei Chu, Liang-Jie Wang, Jiang Jiang, Xia Liu, Kazuhide Sawada, Jinchi Zhang

ABSTRACT

Landslide susceptibility mapping (LSM) is a critical tool for mitigating the damages caused by geologic disasters. The selection of map units and mathematical models greatly affects the efficiency of LSM. To obtain the most appropriate combination of map units and mathematical models, four scales of catchment map units (CMUs) were analyzed and random forest (RF) and multivariate adaptive regression spline (MARSpline) models were applied in Gero City, Japan. The percentage of correctly identified landslides and the areas under the relative operating characteristic (ROC) curve were used to evaluate the model performances. The results indicate that the RF model had higher prediction accuracy than the MARSpline model, especially when the size of the CMU was 0.09 km2. A relatively high percentage of landslides fell into the high and very high landslide susceptibility classes (73%) and the lowest percentage of landslides fell into the very low landslide susceptibility classes (0.82%). The prediction-area (P-A) plots indicated that the prediction rates were higher for the RF model than the MARSpline model. The results of this study also suggest that the model accuracy can be increased if the appropriate CMU size is used. Therefore, the potential benefits of using the RF model in combination with the appropriate CMU size should be further explored using additional landslide-conditioning factors and other models. More... »

PAGES

341-355

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    URI

    http://scigraph.springernature.com/pub.10.1007/s12303-018-0038-8

    DOI

    http://dx.doi.org/10.1007/s12303-018-0038-8

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    https://app.dimensions.ai/details/publication/pub.1106542688


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