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Optimized volume models of earthquake-triggered landslides.

Xu C, Xu X, Shen L, Yao Q, Tan X, Kang W, Ma S, Wu X, Cai J, Gao M, Li K - Sci Rep (2016)

Bottom Line: The samples were used to fit the conventional landslide "volume-area" power law relationship and the 3 optimized models we proposed, respectively.Two data fitting methods, i.e. log-transformed-based linear and original data-based nonlinear least square, were employed to the 4 models.Results show that original data-based nonlinear least square combining with an optimized model considering length, width, height, lithology, slope, peak ground acceleration, and slope aspect shows the best performance.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, Beijing, 100029, China.

ABSTRACT
In this study, we proposed three optimized models for calculating the total volume of landslides triggered by the 2008 Wenchuan, China Mw 7.9 earthquake. First, we calculated the volume of each deposit of 1,415 landslides triggered by the quake based on pre- and post-quake DEMs in 20 m resolution. The samples were used to fit the conventional landslide "volume-area" power law relationship and the 3 optimized models we proposed, respectively. Two data fitting methods, i.e. log-transformed-based linear and original data-based nonlinear least square, were employed to the 4 models. Results show that original data-based nonlinear least square combining with an optimized model considering length, width, height, lithology, slope, peak ground acceleration, and slope aspect shows the best performance. This model was subsequently applied to the database of landslides triggered by the quake except for two largest ones with known volumes. It indicates that the total volume of the 196,007 landslides is about 1.2 × 10(10) m(3) in deposit materials and 1 × 10(10) m(3) in source areas, respectively. The result from the relationship of quake magnitude and entire landslide volume related to individual earthquake is much less than that from this study, which reminds us the necessity to update the power-law relationship.

No MeSH data available.


Related in: MedlinePlus

Correlations between landslide volume and area (A), length (B), width (C) and height (D). Regression analysis.
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f2: Correlations between landslide volume and area (A), length (B), width (C) and height (D). Regression analysis.

Mentions: This work has obtained correlations between volume and area (A), length (L), width (W), and height (H) of the aforementioned 1,415 samples. They are projected on four panels (Fig. 2). Regression curves on the basis of the two data fitting methods, i.e. log-transformed linear least square and original data-based nonlinear least square were derived for each correlation (Fig. 2). The resultant curves from the linear least method shows low-quality regression because the method did not work well for data in multiply orders. In other words, significant errors occurred. However, the nonlinear method solved this problem properly (Fig. 2). The data points of larger landslides are closer to the red lines (nonlinear fitted method) than that to the gray lines (linear fitted method) (Fig. 2). The coefficients of determinations (R2) of the linear correlations between log-transformed landslide volume and area, length, width, and height are 0.7108, 0.3941, 0.3838, and 0.532, respectively, whereas the R2 values of the nonlinear correlations on the basis of original data are 0.777, 0.405, 0.532, and 0.267, respectively, which indicates that the correlation between landslide volume and area is better than those correlations between landslide volume and other geometric parameters. This is perhaps because one of the aforementioned constraints for sample selection is the 20–500% for the percentage of landslide volume from DEM differential to estimated volume on the basis of the “volume–area” power law relationship V = 0.106 × A1.388. The relative low R2 values of correlations between landslide volume and length, width, and height are probably because landslides with comparable volumes are of various types and have considerable differences in lengths, widths, and heights. On the other hand, it also shows that the volume of landslides may be affected by more parameters in addition to geometry parameters.


Optimized volume models of earthquake-triggered landslides.

Xu C, Xu X, Shen L, Yao Q, Tan X, Kang W, Ma S, Wu X, Cai J, Gao M, Li K - Sci Rep (2016)

Correlations between landslide volume and area (A), length (B), width (C) and height (D). Regression analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940731&req=5

f2: Correlations between landslide volume and area (A), length (B), width (C) and height (D). Regression analysis.
Mentions: This work has obtained correlations between volume and area (A), length (L), width (W), and height (H) of the aforementioned 1,415 samples. They are projected on four panels (Fig. 2). Regression curves on the basis of the two data fitting methods, i.e. log-transformed linear least square and original data-based nonlinear least square were derived for each correlation (Fig. 2). The resultant curves from the linear least method shows low-quality regression because the method did not work well for data in multiply orders. In other words, significant errors occurred. However, the nonlinear method solved this problem properly (Fig. 2). The data points of larger landslides are closer to the red lines (nonlinear fitted method) than that to the gray lines (linear fitted method) (Fig. 2). The coefficients of determinations (R2) of the linear correlations between log-transformed landslide volume and area, length, width, and height are 0.7108, 0.3941, 0.3838, and 0.532, respectively, whereas the R2 values of the nonlinear correlations on the basis of original data are 0.777, 0.405, 0.532, and 0.267, respectively, which indicates that the correlation between landslide volume and area is better than those correlations between landslide volume and other geometric parameters. This is perhaps because one of the aforementioned constraints for sample selection is the 20–500% for the percentage of landslide volume from DEM differential to estimated volume on the basis of the “volume–area” power law relationship V = 0.106 × A1.388. The relative low R2 values of correlations between landslide volume and length, width, and height are probably because landslides with comparable volumes are of various types and have considerable differences in lengths, widths, and heights. On the other hand, it also shows that the volume of landslides may be affected by more parameters in addition to geometry parameters.

Bottom Line: The samples were used to fit the conventional landslide "volume-area" power law relationship and the 3 optimized models we proposed, respectively.Two data fitting methods, i.e. log-transformed-based linear and original data-based nonlinear least square, were employed to the 4 models.Results show that original data-based nonlinear least square combining with an optimized model considering length, width, height, lithology, slope, peak ground acceleration, and slope aspect shows the best performance.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, Beijing, 100029, China.

ABSTRACT
In this study, we proposed three optimized models for calculating the total volume of landslides triggered by the 2008 Wenchuan, China Mw 7.9 earthquake. First, we calculated the volume of each deposit of 1,415 landslides triggered by the quake based on pre- and post-quake DEMs in 20 m resolution. The samples were used to fit the conventional landslide "volume-area" power law relationship and the 3 optimized models we proposed, respectively. Two data fitting methods, i.e. log-transformed-based linear and original data-based nonlinear least square, were employed to the 4 models. Results show that original data-based nonlinear least square combining with an optimized model considering length, width, height, lithology, slope, peak ground acceleration, and slope aspect shows the best performance. This model was subsequently applied to the database of landslides triggered by the quake except for two largest ones with known volumes. It indicates that the total volume of the 196,007 landslides is about 1.2 × 10(10) m(3) in deposit materials and 1 × 10(10) m(3) in source areas, respectively. The result from the relationship of quake magnitude and entire landslide volume related to individual earthquake is much less than that from this study, which reminds us the necessity to update the power-law relationship.

No MeSH data available.


Related in: MedlinePlus