A spatiotemporal probabilistic modelling of storm‐induced shallow landsliding using aerial photographs and logistic regression

Abstract

Landslides constitute one of the major natural hazards that could cause significant losses of life and property. Mapping or delineating areas prone to landsliding is therefore essential for land-use activities and management decision making in hilly or mountainous regions. A landslide hazard map can be constructed by a qualitative combination of maps of site conditions, including geology, topography and geomorphology, by statistical methods through correlating landslide occurrence with geologic and geomorphic factors, or by using safety factors from stability analysis. A landslide hazard map should provide information on both the spatial and temporal probabilities of landsliding in a certain area. However, most previous studies have focused on susceptibility mapping, rather than on hazard mapping in a spatiotemporal context. This study aims at developing a predictive model, based on both quasi-static and dynamic variables, to determine the probability of landsliding in terms of space and time. The study area selected is about 13 km 2 in North Lantau, Hong Kong. The source areas of the landslides caused by the rainstorms of 18 July 1992 and 4-5 November 1993 were interpreted from multi-temporal aerial photographs. Landslide data, lithology, digital elevation model data, land cover, and rainfall data were digitized into a geographic information system database. A logistic regression model was developed using lithology, slope gradient, slope aspect, elevation, slope shape, land cover, and rolling 24 h rainfall as independent variables, since the dependent variable could be expressed in a dichotomous way. This model achieved an overall accuracy of 87Ð2%, with 89Ð5% of landslide grid cells correctly classified and found to be performing satisfactorily. The model was then applied to rainfalls of a variety of periods of return, to predict the probability of landsliding on natural slopes in space and time. It is observed that the modelling techniques described here are useful for predicting the spatiotemporal probability of landsliding and can be used by land-use planners to develop effective management strategies.

Statistical approach to storm event-induced landslides susceptibility

“…Dai and Lee (2003) used two event-based landslide inventories (for two different years) to build a susceptibility model. Only one specific event was used in training the susceptibility model in the present study.…”

“…Most shallow slope failures during or after a rainstorm are triggered by an increase in pore pressure and a corresponding reduction in effective stress in the soil (Dai and Lee, 2003). Shallow soils are often underlain by a relatively low permeability layer or an impeding layer.…”

“…To prepare better landslide susceptibility maps using the EB-LSA model we should replace the event rainfall data by the rainfall data for a particular return period, such as 10, 20, 50 or 100 years so as to produce a more uniform temporal probability map of the region, such as that done by Dai and Lee (2003). The temporal probability of a storm event may be obtained by frequency analysis of the rainfall (Chow et al, 1988).…”

“…In recent years, Dai and Lee (2003) used the rolling 24-h rainfall as an independent variable for the building of a storm-induced shallow landslide probabilistic model. Chang et al (2007) used the maximum 3-h rainfall and rainfall duration in their logit model to model the rainfall conditions critical for triggering landslides.…”

Geohazards (floods and landslides) in the Ndop plain, Cameroon volcanic line

“…The role of relief in slope instability has been disputed with some authors [49][50][51][52] arguing that altitude is a good indicator conditioning slope movements, while others [53] do not see any changes in slope movements between low altitudes and the high altitudes. The three dimensional representation of the relief of the area was done using the DEM, from which vertices of points with spatial references were generated with the Global Mapper 13 software.…”