The 5.12 Wenchuan Earthquake in 2008 induced hundreds of large-scale landslides. This paper systematically analyzes 112 large-scale landslides (surface area > 50000 m2), which were identified by interpretation of r...The 5.12 Wenchuan Earthquake in 2008 induced hundreds of large-scale landslides. This paper systematically analyzes 112 large-scale landslides (surface area > 50000 m2), which were identified by interpretation of remote sensing imagery and field investigations. The analysis suggests that the distribution of large-scale landslides is affected by the following four factors: (a) distance effect: 80% of studied large-scale landslides are located within a distance of 5 km from the seismic faults. The farther the distance to the faults, the lower the number of large-scale landslides; (b) locked segment effect: the large-scale landslides are mainly located in five concentration zones closely related with the crossing, staggering and transfer sections between one seismic fault section and the next one, as well as the end of the NE fault section. The zone with the highest concentration was the Hongbai-Chaping segment, where a great number of large-scale landslides including the two largest landslides were located. The second highest concentration of large-scale landslides was observed in the Nanba-Donghekou segment at the end of NE fault, where the Donghekou landslide and the Woqian landslide occurred; (c) Hanging wall effect: about 70% of the large-scale landslides occurred on the hanging wall of the seismic faults; and (d) direction effect: in valleys perpendicular to the seismic faults, the density of large-scale landslides on the slopes facing the seismic wave is obviously higher than that on the slopes dipping in the same direction as the direction of propagation of the seismic wave. Meanwhile, it is found that the sliding and moving directions of large-scale landslides are related to the staggering direction of the faults in each section. In Qingchuan County where the main fault activity was horizontal twisting and staggering, a considerable number of landslides showed the feature of sliding and moving in NE direction which coincides with the staggering direction of the seismic faults.展开更多
The object of the research is to compare the model performance and explain the error source of original logistic regression landslide susceptibility model(abbreviated as or-LRLSM) and landslide ratio-based logistic re...The object of the research is to compare the model performance and explain the error source of original logistic regression landslide susceptibility model(abbreviated as or-LRLSM) and landslide ratio-based logistic regression landslide susceptibility model(abbreviated as lr-LRLSM) in the Chishan watershed with a serious landslide disaster after 2009 Typhoon Morakot. The landslide inventory induced by 2009 Typhoon Morakot in South Taiwan is the main research material, while the Chishan watershed is the research area. Six variables, including elevation, slope, aspect, geological formation, accumulated rainfall, and bank erosion, were included in the two models. The performance of lr-LRLSM is better than that of or-LRLSM. The Cox & Snell R2, Nagelkerke R2 value, and the area under the relative operating characteristic curve(abbreviated as AUC) of lrLRLSM is larger than those of or-LRLSM, and the average correct ratio for the lr-LRLSM to predict landslide or non-landslide is larger than that of orLRLSM by 5.0%. The increase of the average correct ratio(abbreviated as ACR) difference from or-LRLSM to lr-LRLSM shows in slope, revised accumulated rainfall, aspect, geological formation and bank erosion variables, and only light decreases in elevation variable. The error sources of continuous variables in building the or-LRLSM is the dissimilarity between the distribution of landslide ratio and production of coefficient and characteristic values, while those of categorical variables is due to low correlation of landslide ratio and the coefficient value of each parameter. Using the classification of landslide ratio as the database to build logistic regression landslide susceptibility model(abbreviated as LRLSM) can revise the errors. The comparison of or-LRLSM and lr-LRLSM in the Chishan watershed also shows that building the landslide susceptibility model(abbreviated as LSM) by using lr-LRLSM is practical and of better performance than that by using the or-LRLSM.展开更多
基金sponsored by the project of the Chinese National Key Basic Research Program on "The failure mechanism and distribution rule of slopes under strong earthquakes" (Grant No. 2008CB425801)the Education Department Innovation Research Team Program (Grant No. IRT0812)
文摘The 5.12 Wenchuan Earthquake in 2008 induced hundreds of large-scale landslides. This paper systematically analyzes 112 large-scale landslides (surface area > 50000 m2), which were identified by interpretation of remote sensing imagery and field investigations. The analysis suggests that the distribution of large-scale landslides is affected by the following four factors: (a) distance effect: 80% of studied large-scale landslides are located within a distance of 5 km from the seismic faults. The farther the distance to the faults, the lower the number of large-scale landslides; (b) locked segment effect: the large-scale landslides are mainly located in five concentration zones closely related with the crossing, staggering and transfer sections between one seismic fault section and the next one, as well as the end of the NE fault section. The zone with the highest concentration was the Hongbai-Chaping segment, where a great number of large-scale landslides including the two largest landslides were located. The second highest concentration of large-scale landslides was observed in the Nanba-Donghekou segment at the end of NE fault, where the Donghekou landslide and the Woqian landslide occurred; (c) Hanging wall effect: about 70% of the large-scale landslides occurred on the hanging wall of the seismic faults; and (d) direction effect: in valleys perpendicular to the seismic faults, the density of large-scale landslides on the slopes facing the seismic wave is obviously higher than that on the slopes dipping in the same direction as the direction of propagation of the seismic wave. Meanwhile, it is found that the sliding and moving directions of large-scale landslides are related to the staggering direction of the faults in each section. In Qingchuan County where the main fault activity was horizontal twisting and staggering, a considerable number of landslides showed the feature of sliding and moving in NE direction which coincides with the staggering direction of the seismic faults.
基金Ministry of Science and Technology in Taiwan for providing budget for my project (project number:NSC 103-2313-B-035-001)
文摘The object of the research is to compare the model performance and explain the error source of original logistic regression landslide susceptibility model(abbreviated as or-LRLSM) and landslide ratio-based logistic regression landslide susceptibility model(abbreviated as lr-LRLSM) in the Chishan watershed with a serious landslide disaster after 2009 Typhoon Morakot. The landslide inventory induced by 2009 Typhoon Morakot in South Taiwan is the main research material, while the Chishan watershed is the research area. Six variables, including elevation, slope, aspect, geological formation, accumulated rainfall, and bank erosion, were included in the two models. The performance of lr-LRLSM is better than that of or-LRLSM. The Cox & Snell R2, Nagelkerke R2 value, and the area under the relative operating characteristic curve(abbreviated as AUC) of lrLRLSM is larger than those of or-LRLSM, and the average correct ratio for the lr-LRLSM to predict landslide or non-landslide is larger than that of orLRLSM by 5.0%. The increase of the average correct ratio(abbreviated as ACR) difference from or-LRLSM to lr-LRLSM shows in slope, revised accumulated rainfall, aspect, geological formation and bank erosion variables, and only light decreases in elevation variable. The error sources of continuous variables in building the or-LRLSM is the dissimilarity between the distribution of landslide ratio and production of coefficient and characteristic values, while those of categorical variables is due to low correlation of landslide ratio and the coefficient value of each parameter. Using the classification of landslide ratio as the database to build logistic regression landslide susceptibility model(abbreviated as LRLSM) can revise the errors. The comparison of or-LRLSM and lr-LRLSM in the Chishan watershed also shows that building the landslide susceptibility model(abbreviated as LSM) by using lr-LRLSM is practical and of better performance than that by using the or-LRLSM.
