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RISS 인기검색어
- 검색키워드
- 저자 : Kazuhide Sawada (검색결과 2 건)
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Liang-Jie Wang,Min Guo,Kazuhide Sawada,Jie Lin,Jinchi Zhang 한국지질과학협의회 2016 Geosciences Journal Vol.20 No.1
For the purpose of comparing susceptibility mapping methods in Mizunami City, Japan, the landslide inventory was partitioned into three groups as various training and test datasets to identify the most appropriate method for creating a landslide susceptibility map. A total of fifteen landslide susceptibility maps were produced using frequency ratio, logistic regression, decision tree, weights of evidence and artificial neural network models, and the results were assessed using existing test landside points and areas under the relative operative characteristic curve (AUC). The validation results indicated that the logistic regression model could provide the highest AUC value (0.865), and a relatively high percentage of landslide points fell in the high and very high landslide susceptibility classes in this study. Furthermore, the paper also suggested that the model performances would be increased if appropriate landslide points were used for the calculation.
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Lei Chu,Liang-Jie Wang,Jiang Jiang,Xia Liu,Kazuhide Sawada,Jinchi Zhang 한국지질과학협의회 2019 Geosciences Journal Vol.23 No.2
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.
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