Landslide Hazard Mapping Based on Slope Stability Analysis Considering Rainfall Spatial Uncertainty

被引：0

作者：

Tozato K.

Sasaki S.

Dolojan N.L.J.

Moriguchi S.

Terada K.

机构：

来源：

Transactions of the Japan Society for Computational Engineering and Science | 2022年 / 2022卷

基金：

日本学术振兴会;

关键词：

Green-Ampt model; Landslide; Proper orthogonal decomposition; Rainfall uncertainty; Slope stability analysis;

D O I：

10.11421/jsces.2022.20220018

中图分类号：

学科分类号：

摘要：

This study presents a mapping method for evaluating landslide hazards in consideration of the spatial uncertainty of rainfall. Spatial modes of the rainfall in a target area are extracted using a mode decomposition technique, and virtual rainfall events that may occur in the target area is generated using the spatial modes. Slope stability analysis with the assumption of the infinite slope is then performed using the virtual rainfall data, and a landslide hazard map is created. Based on the obtained results, the influence of the rainfall uncertainty on landslide hazard mapping is discussed. © 2022 by the Japan Society for Computational Engineering and Science.

引用

共 34 条

[1]

Guzzetti F., Peruccacci S., Rossi M., Stark C. P., The rainfall intensity-duration control of shallow landslides and debris ﬂows: An update, Landslides, 5, pp. 3-17, (2008)

[2]

Brunetti M. T., Peruccacci S., Rossi M., Luciani S., Valigi D., Guzzetti F., Rainfall thresholds for the possible occurrence of landslides in italy, Natu- ral Hazards and Earth System Sciences, 10, pp. 447-458, (2010)

[3]

Li W. Y., Liu C., Scaioni M., Sun W. W., Chen Y., Yao D. J., Chen S., Hong Y., Zhang K. H., Cheng G. D., Spatio-temporal analysis and simula- tion on shallow rainfall-induced landslides in china using landslide susceptibility dynamics and rainfall i-d thresholds, Science China Earth Sciences 2017, 60, 4, pp. 720-732, (2017)

[4]

Rosi A., Segoni S., Canavesi V., Monni A., Gal- lucci A., Casagli N., Deﬁnition of 3d rain- fall thresholds to increase operative landslide early warning system performances, Landslides, 18, pp. 1045-1057, (2021)

[5]

Osanai N., Shimizu T., Kuramoto K., Kojima S., Noro T., Japanese early-warning for debris ﬂows and slope failures using rainfall indices with radial basis function network, Landslides, 7, pp. 325-338, (2010)

[6]

Posner J., Georgakakos K. P., Soil moisture and precipitation thresholds for real-time landslide prediction in el salvador, Landslides, 12, pp. 1179-1196, (2015)

[7]

Bui D. T., Pradhan B., Lofman O., Revhaug I., Dick O. B., Regional prediction of landslide hazard using probability analysis of intense rainfall in the hoa binh province, vietnam, Natural Hazards, 66, pp. 707-730, (2013)

[8]

Shahri A., Spross J., Johansson F., Lars- son S., Landslide susceptibility hazard map in southwest sweden using artiﬁcial neural network, CATENA, 183, (2019)

[9]

Bordoni M., Vivaldi V., Lucchelli L., Ciabatta L., Brocca L., Galve J. P., Meisina C., Develop- ment of a data-driven model for spatial and tem- poral shallow landslide probability of occurrence at catchment scale, Landslides, 18, pp. 1209-1229, (2021)

[10]

Chen W., Xie X., Wang J., Pradhan B., Hong H., Bui D. T., Duan Z., Ma J., A comparative study of logistic model tree, random forest, and classiﬁcation and regression tree models for spa- tial prediction of landslide susceptibility, CATENA, 151, pp. 147-160, (2017)

← 1 2 3 4 →