Rainfall-induced Landslide Hazard Research Articles

Rainfall-Induced Landslide Hazard Assessment along NH 1D from Ganiwan to Gumri (J & K), India

Rainfall-Induced Landslide Hazard Assessment along NH 1D from Ganiwan to Gumri (J & K), India

A probabilistic model for rainfall—induced shallow landslide prediction at the regional scale

This paper presents a new probabilistic physically-based computational model (called PG_TRIGRS) for the probabilistic analysis of rainfall-induced landslide hazard at a regional scale. The model is based on the deterministic approach implemented in the original TRIGRS code, developed by Baum et al. (USGS Open File Report 02–424, 2002) and Baum et al. (USGS Open File Report 08–1159, 2008). Its key innovative features are: (a) the application of Ordinary Kriging for the estimation of the spatial distributions of the first two statistical moments of the probability density functions of the relevant soil properties over the entire area, based on limited available information gathered from available information from limited site investigation campaigns, and (b) the use of Rosenblueth’s Point Estimate method as a more efficient alternative to the classical Monte Carlo method for the reliability analysis performed at the single-cell level to obtain the probability of failure associated to a given rainfall event. The application of the PG_TRIGRS code to a selected study area located in the Umbria Region for different idealized but realistic rainfall scenarios has demonstrated the computational efficiency and the accuracy of the proposed methodology, assessed by comparing predicted landslide densities with available field observations reported by the IFFI project. In particular, while the model might fail to identify all individual landslide events, its predictions are remarkably good in identifying the areas of higher landslide density.

Spatio-temporal analysis and simulation on shallow rainfall-induced landslides in China using landslide susceptibility dynamics and rainfall I-D thresholds

An empirical simulation method to simulate the possible position of shallow rainfall-induced landslides in China has been developed. This study shows that such a simulation may be operated in real-time to highlight those areas that are highly prone to rainfall-induced landslides on the basis of the landslide susceptibility index and the rainfall intensity-duration (I-D) thresholds. First, the study on landslide susceptibility in China is introduced. The entire territory has been classified into five categories, among which high-susceptibility regions (Zone 4- ‘High’ and 5-‘Very high’) account for 4.15% of the total extension of China. Second, rainfall is considered as an external triggering factor that may induce landslide initiation. Real-time satellite-based TMPA 3B42 products may provide real rainfall spatial and temporal patterns, which may be used to derive rainfall duration time and intensity. By using a historical record of 60 significant past landslides, the rainfall I-D equation has been calibrated. The rainfall duration time that may trigger a landslide has resulted between 3 hours and 45 hours. The combination of these two aspects can be exploited to simulate the spatiotemporal distribution of rainfall-induced landslide hazards when rainfall events exceed the rainfall I-D thresholds, where the susceptibility category is ‘high’ or ‘very high’. This study shows a useful tool to be part of a systematic landslide simulation methodology, potentially providing useful information for a theoretical basis and practical guide for landslide prediction and mitigation throughout China.

Prediction of shallow landslide occurrence: Validation of a physically-based approach through a real case study

In recent years, physically-based numerical models have frequently been used in the framework of early-warning systems devoted to rainfall-induced landslide hazard monitoring and mitigation. For this reason, in this work we describe the potential of SLIP (Shallow Landslides Instability Prediction), a simplified physically-based model for the analysis of shallow landslide occurrence. In order to test the reliability of this model, a back analysis of recent landslide events occurred in the study area (located SW of Messina, northeastern Sicily, Italy) on October 1st, 2009 was performed. The simulation results have been compared with those obtained for the same event by using TRIGRS, another well-established model for shallow landslide prediction. Afterwards, a simulation over a 2-year span period has been performed for the same area, with the aim of evaluating the performance of SLIP as early warning tool. The results confirm the good predictive capability of the model, both in terms of spatial and temporal prediction of the instability phenomena. For this reason, we recommend an operating procedure for the real-time definition of shallow landslide triggering scenarios at the catchment scale, which is based on the use of SLIP calibrated through a specific multi-methodological approach.

Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence

Landslide hazard mapping is a fundamental tool for disaster management activities in mountainous terrains. The main purpose of this study is to evaluate the predictive power of weights-of-evidence modelling in landslide hazard assessment in the Lesser Himalaya of Nepal. The modelling was performed within a geographical information system (GIS), to derive a landslide hazard map of the south-western marginal hills of the Kathmandu Valley. Thematic maps representing various factors (e.g., slope, aspect, relief, flow accumulation, distance to drainage, soil depth, engineering soil type, landuse, geology, distance to road and extreme one-day rainfall) that are related to landslide activity were generated, using field data and GIS techniques, at a scale of 1:10,000. Landslide events of the 1970s, 1980s, and 1990s were used to assess the Bayesian probability of landslides in each cell unit with respect to the causative factors. To assess the accuracy of the resulting landslide hazard map, it was correlated with a map of landslides triggered by the 2002 extreme rainfall events. The accuracy of the map was evaluated by various techniques, including the area under the curve, success rate and prediction rate. The resulting landslide hazard value calculated from the old landslide data showed a prediction accuracy of > 80%. The analysis suggests that geomorphological and human-related factors play significant roles in determining the probability value, while geological factors play only minor roles. Finally, after the rectification of the landslide hazard values of the new landslides using those of the old landslides, a landslide hazard map with > 88% prediction accuracy was prepared. The methodology appears to have extensive applicability to the Lesser Himalaya of Nepal, with the limitation that the model's performance is contingent on the availability of data from past landslides.

Rainfall‐induced landslide hazard assessment using artificial neural networks

AbstractIn Japan, landslides triggered by heavy rainfall tend to occur during the annual rainy season from early June until the middle of July; these landslides constitute a major hazard causing significant property damage and loss of life. This paper proposes the use of back propagation neural networks (BPNN) to predict the probability of landslide occurrence for a scenario of heavy rainfall in the Minamata area of southern Kyushu Island, Japan. All of the landslides were detected from aerial photographs taken in 1999, 2001 and 2003, and a geospatial database of lithology, topography, soil characteristics, land use and precipitation was constructed using geographical information systems (GIS). The training sample consists of 602 cells that include landslide activity and 1600 cells in stable areas. Using the trained BPNN with 49 input nodes, three hidden layers, and one output node, 239 589 cells were processed to produce a map of landslide probability for a maximum daily precipitation of 329 mm and a maximum cumulative precipitation of 581 mm for an incessant, intense rainfall event in the future. The resultant hazard map was classified into four hazard levels; it can be referenced for land‐use planning and decision‐making for community development. Copyright © 2005 John Wiley & Sons, Ltd.

Uncertainties in rainfall-induced landslide hazard

The paper addresses the main uncertainties associated with the occurrence of rainfall induced landslides. Spatial variability of site conditions, local geology and rainfall contributes significantly to the uncertainty of landslide hazard. Urban landslide problems require the management of slopes of marginal stability. Assessment of hazard, vulnerability and risk require the use of observational approaches, the analysis of rainfall data and the relationship between rainfall magnitudes on the one hand and slope movement on the other. Details of research carried out in the Illawarra area of New South Wales, Australia are provided. Reference is made to the comprehensive database enabling the determination of landslide frequencies. The concept of antecedent rainfall percentage exceedence time (ARPET) is explained. The use of inferred threshold rainfall magnitudes for real-time prediction and warning is explained. Uncertainties concerned with this approach are explored with particular reference to rainfall distributions in the study area.