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1.
Many GIS-based landslide models require detailed datasets that are ideally collected from field measurements, which can incur
high costs for carrying out surveys. Even when the data is on hand, implementing physics-based slope stability techniques
can be difficult. Common research practice uses differential equations to characterize the dynamic flow of a landslide, but
it is often laborious without making substantial simplifications. A possible solution is to implement a cellular automata
modeling approach, which represents both spatial and temporal components, to simulate the dynamics of the landslide propagation
process. In this study, a simplified cellular automata model is developed for the effective prediction of landslide runouts,
where the data requirement is a high resolution digital elevation model (DEM). Parameters, such as slope and slope curvature
features, are derived from the DEM and coupled with logistic regression. The developed model is implemented on the Patrick
and Dawson-Chu Slide in North Vancouver, Canada. The results from this study site were favorable, given almost 90% agreement
between simulated landslides and data obtained for real landslides. In addition, sensitivity analysis was performed on the
initiation sites to test the model logic and outputs of the landslide flow. 相似文献
2.
Zhongqiang Liu Graham Gilbert Jose Mauricio Cepeda Asgeir Olaf Kydland Lysdahl Luca Piciullo Heidi Hefre Suzanne Lacasse 《地学前缘(英文版)》2021,12(1):385-393
This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them to the spatial modelling of shallow landslides near Kvam in Norway.In the development of the ML models,a total of 11 significant landslide controlling factors were selected.The controlling factors relate to the geomorphology,geology,geo-environment and anthropogenic effects:slope angle,aspect,plan curvature,profile curvature,flow accumulation,flow direction,distance to rivers,water content,saturation,rainfall and distance to roads.It is observed that slope angle was the most significant controlling factor in the ML analyses.The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic(ROC)analysis.The results show that the‘ensemble'GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides,with a 95%probability of landslide detection and 87%prediction efficiency. 相似文献
3.
Binh Thai Pham Dieu Tien Bui M. B. Dholakia Indra Prakash Ha Viet Pham 《Geotechnical and Geological Engineering》2016,34(6):1807-1824
The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards. 相似文献
4.
In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models. 相似文献
5.
Zonghu Liao Yang Hong Dalia Kirschbaum Robert F. Adler Jonathan J. Gourley Rick Wooten 《Natural Hazards》2011,58(1):325-339
The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models
that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex
terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction
model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In
this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue
Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane
season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined
radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological
Survey is used to evaluate the MaTRIGRS’ predictive skill for the landslide locations and timing, identifying predictions
within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan’s passage in September 2004. Results show
that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted
but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected
with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for
landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to
serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The
validation can be further improved with additional landslide information including the exact time of failure for each landslide
and the landslide’s extent and run out length. 相似文献
6.
7.
Landslides of subaerial and submarine origin may generate tsunamis with locally extreme amplitudes and runup. While the landslides themselves are dangerous, the hazards are compounded by the generation of tsunamis along coastlines, in enclosed water bodies, and off continental shelves and islands. Tsunamis generated by three-dimensional deformable granular landslides were studied on planar and conical hill slopes in the three-dimensional NEES tsunami wave basin at Oregon State University based on the generalized Froude similarity. A unique pneumatic landslide tsunami generator (LTG) was deployed to control the kinematics and acceleration of the naturally rounded river gravel and cobble landslides to simulate broad ranges of landslide shapes and velocities along the slope. Lateral and overhead cameras are used to measure the landslide shapes and kinematics, while acoustic transducers provide the shape of the subaqueous deposits. The subaerial landslide shape is extracted from the camera images as the landslide propagates under gravity down the hill slope, and surface reconstruction of the landslide is conducted using the stereo particle image velocimetry (PIV) system on the conical hill slope. Subaerial landslide surface velocities are measured with a planar PIV system on the planar hill slope and stereo PIV system on the conical hill slope. The submarine deposits are characterized by the runout distances and the deposit thickness distributions. Larger cobbles are observed producing hummock type features near the maximum runout length. These unique laboratory landslide experiments serve to validate deformable landslide models as well as provide the source characteristics for tsunami generation. 相似文献
8.
Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models 总被引：2，自引：0，他引：2
Research on the dynamics of landslide displacement forms the basis for landslide hazard prevention. This paper proposes a novel data-driven approach to monitor and predict the landslide displacement. In the first part, autoregressive moving average time series models are constructed to analyze the autocorrelation of landslide triggering factors. A linear ensemble-based extreme learning machine using the least absolute shrinkage and selection operator is applied in predicting the displacement of landslides. Five benchmarking data-driven models, the support vector machine, neural network, random forest, k-nearest neighbor, and the classical extreme learning machine, are considered as baseline models for validating the ensemble-based extreme learning machines. Numerical experiments demonstrated that the proposed prediction model produces the smallest prediction errors among all the algorithms tested. In the second part, parametric copula models are fitted on the predicted displacement, to investigate the relationship between the triggering factors and landslide displacement values. The Gumbel-Hougaard copula model performs best, which indicates strong upper tail correlation between the triggering factors and displacement values. Thresholds for the triggering factors can be obtained by monitoring the landslide moving patterns with large displacement values. The effectiveness and utility of the proposed data-driven approach have been confirmed with the landslide case study in the region of the Three Gorges Reservoir. 相似文献
9.
The mobility of long-runout landslides 总被引：17，自引：0，他引：17
Several issues relevant to the mobility of long-runout landslides are examined. A central idea developed in this paper is that the apparent coefficient of friction (ratio of the fall height to the runout distance) commonly used to describe landslide mobility is physically meaningless. It is proposed that the runout distance depends primarily on the volume and not on the fall height, which just adds scatter to the correlation. The negative correlation observed between the apparent coefficient of friction and the volume is just due to the fact that, on the gentle slopes on which landslides travel and come to rest, a large increase in runout distance due to a large volume corresponds to a small increase in the total fall height, hence to a decrease in the apparent coefficient of friction.
It is shown that the spreading of a fluid-absent, granular flow is not able to explain the large runout distances of landslides, and in particular does not allow the centre of mass to travel further than expected for a sliding block. This contrasts with the behaviour of natural landslides, for which the centre of mass is shown to travel much further than expected from a simple Coulomb model. The presence of an interstitial fluid which can partly or entirely support the load of particles allows the effective coefficient of solid friction to be reduced or even suppressed. Air is not efficient for fluidising large landslides and a loose debris cannot slide over a basal layer of entrapped and compressed air, as air would rapidly pass through the debris in the form of bubbles during batch sedimentation. Water is much more efficient as a fluidising medium due to its higher density and viscosity, and its incompressibility. As water is known to enhance the mobility of the saturated debris flows, it is proposed that water is also responsible for the long runout of landslides. This is consistent with the fact that the increase in runout with volume is similar for debris flows and landslides. Field evidence suggests that most landslides are unsaturated with water but not dry, even on Mars.
Comparison of the velocity of well-documented landslides with that predicted by fluid-absent, granular models shows that these models predict landslides that are much faster and less responsive to topography than natural ones. The relatively low velocities of landslides suggest that energy dissipation is dominated by a velocity-dependent stress and that the coefficient of solid friction is very low. This is consistent with the physics of fluidised or partly fluidised debris and suggests that landslide velocity may be controlled by local slope and flow thickness rather than by the initial fall height. In the absence of a supply of fluid at the base, fluidisation requires a net downward flux of sediment, implying some deposition at the base of landslides, which may thus progressively run out of material. In such a model, the spreading of the portion of a landslide beyond a certain distance would primarily depend on the volume passing this distance and not on the total volume of the landslide. Landslide deposits may therefore have self-similar shapes, in which the area covered beyond a certain distance is a constant function of the volume beyond that distance. It is shown that the shape of some well-documented landslide deposits is in reasonable agreement with this prediction. One consequence is that, as recently proposed for debris flows, assessment of hazards related to landslides should be based on the correlation between the volume and the area covered by the deposit, rather than on the apparent coefficient of friction. 相似文献
10.
滑坡启动机理研究是滑坡防治的前提条件。应用三维连续介质动态数值模型方法,采用摩擦模型、Voellmy模型2种流变模型,对滑坡启动过程进行分析求解,开展滑坡启动机理数值模拟研究。结果表明,不论是限制坡面(渠道型)还是无限制坡面,在2种流变模型和侵蚀率下,随着坡度逐渐增加,地形所提供给滑坡体的能量进一步增大,物质运动距离进一步增加,其相应的平均速度、最大速度(前端速度)和总动能也会进一步加大,经历了从缓慢蠕变至快速增加的过程。根据不同坡度下滑坡的启动、运动规律、堆积过程及各坡度下动力参数延程变化规律,可以将滑坡的启动坡度设定为25°~30°。该滑坡启动坡度的设定可为地质灾害防治措施和监测预警提供技术参考。 相似文献
11.
Rapid debris flows are among the most destructive natural hazards in steep mountainous terrains. Prediction of their path and impact hinges on knowledge of initiation location and the size and constitution of the released mass. To better link mass release initiation with debris flow paths and runout lengths, we propose to capitalize on a newly developed model for rainfall-induced landslide initiation (“Catchment-scale Hydro-mechanical Landslide Triggering” CHLT model, von Ruette et al. 2013) and couple it with simple estimates of debris flow runout distances and pathways. Landslide locations and volumes provided by the CHLT model are used as inputs to simulate debris flow runout distances with two empirical- and two physically-based models. The debris flow runout models were calibrated using two landslide inventories in the Swiss Alps obtained following a large rainfall event in 2005. We first fitted and tested the models for the “Prättigau” inventory, where detailed information on runout path was available, and then applied the models to landslides inventoried from a different catchment (“Napf”). The predicted debris flow runout distances (emanating from CHLT simulated landslide positions) were well in the range of observed values for the physically-based approaches. The empirical approaches tend to overestimate runout distances relative to observations. These preliminary results demonstrate the added value of linking shallow landslide triggering models with predictions of debris flow runout pathways for a range of soil states and triggering events, thus providing a more complete hazard assessment picture for debris flow exposure at the catchment scale. 相似文献
12.
本文总结了当前有关高速远程滑坡远程机制的研究现状,探讨了滑坡地貌学和沉积学对于揭示高速远程滑坡运动学机理的重要意义。通过梳理前人关于高速远程滑坡堆积形态的分类,分析了高速远程滑坡主要的形态学特征、蕴含的运动学信息以及高速远程滑坡的脱落块体、丘体、脊结构、断层等的地貌结构特征,并进一步指出这些地貌结构的形成主要是滑坡体物质不均匀运动的结果,其受地形和内聚力的影响。高速远程滑坡的沉积学特征明显受到岩性、地形和基底等因素的影响,本文对高速远程滑坡的沉积相模式及其运动学意义进行了分析,并对地层不变性与反序结构两个重要堆积特征进行了讨论。最后,指出高速远程滑坡地貌学与沉积学研究当前主要存在的一些问题,建议将大型物理模拟与数值模拟方法相结合,加强对高速远程滑坡地貌微形态和堆积结构的形成机制研究。 相似文献
13.
Landslides are natural disasters often activated by interaction of different controlling environmental factors, especially in mountainous terrains. In this research, the landslide susceptibility map was developed for the Sarkhoun catchment using Index of Entropy (IoE) and Dempster–Shafer (DS) models. For this purpose, 344 landslides were mapped in GIS environment. 241 (70%) out of the landslides were selected for the modeling and the remaining (30%) were employed for validation of the models. Afterward, 10 landslide conditioning factor layers were prepared including land use, distance to drainage, slope gradient, altitude, lithology, distance to roads, distance to faults, slope aspect, Topography Wetness Index, and Stream Power Index. The relationship between the landslide conditioning factors and landslide inventory maps was determined using the IoE and DS models. In order to verify the models, the results were compared with validation landslide data not employed in training process of the models. Accordingly, Receiver Operating Characteristic (ROC) curves were applied, and Area Under the Curve (AUC) was calculated for the obtained susceptibility maps using the success (training data) and prediction (validation data) rate curves. The land use was found to be the most important factor in the study area. The AUC are 0.82, and 0.81 for success rates of the IoE, and DS models, respectively, while the prediction rates are 0.76 and 0.75. Therefore, the results of the IoE model are more accurate than the DS model. Furthermore, a satisfactory agreement is observed between the generated susceptibility maps by the models and true location of the landslides. 相似文献
14.
Comparing the performance of TRIGRS and TiVaSS in spatial and temporal prediction of rainfall-induced shallow landslides 总被引：1，自引：0，他引：1
This study compares the performance of transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model and time-variant slope stability (TiVaSS) model in the prediction of rainfall-induced shallow landslides. TRIGRS employs one-dimensional (1-D) subsurface flow to simulate the infiltration rate, whereas a three-dimensional (3-D) model is utilized in TiVaSS. The former has been widely used in landslide modeling, while the latter was developed only recently. Both programs are used for the spatiotemporal prediction of shallow landslides caused by rainfall. This study uses the July 2011 landslide event that occurred in Mt. Umyeon, Seoul, Korea, for validation. The performance of the two programs is evaluated by comparison with data of the actual landslides in both location and timing by using a landslide ratio for each factor of safety class (\({\text{LR}}_{\text{class}}\) index), which was developed for addressing point-like landslide locations. Moreover, the influence of surface flow on landslide initiation is assessed. The results show that the shallow landslides predicted by the two models are highly consistent with those of the observed sliding sites, although the performance of TiVaSS is slightly better. Overland flow affects the buildup of the pressure head and reduces the slope stability, although this influence was not significant in this case. A slight increase in the predicted unstable area from 19.30 to 19.93% was recorded when the overland flow was considered. It is concluded that both models are suitable for application in the study area. However, although it is a well-established model requiring less input data and shorter run times, TRIGRS produces less accurate results. 相似文献
15.
16.
Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS 总被引：10，自引：0，他引：10
The purpose of this study is to assess the susceptibility of landslides in parts of Western Ghats, Kerala, India, using a
geographical information system (GIS). Landslide inventory of the area was made by detailed field surveys and the analysis
of the topographical maps. The landslide triggering factors are considered to be slope angle, slope aspect, slope curvature,
slope length, distance from drainage, distance from lineaments, lithology, land use and geomorphology. ArcGIS version 8.3
was used to manipulate and analyse all the collected data. Probabilistic-likelihood ratio was used to create a landslide susceptibility
map for the study area. The result was validated using the Area under Curve (AUC) method and temporal data of landslide occurrences.
The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.
As the result, the success rate of the model was (84.46%) and the prediction rate of the model was (82.38%) shows high prediction
accuracy. In the reclassified final landslide susceptibility zone map, 5.68% of the total area is classified as critical in
nature. The landslide susceptibility map thus produced can be used to reduce hazards associated with landslides and to land
cover planning. 相似文献
17.
Researchers have long attempted to determine the amount of rainfall needed to trigger slope failures, yet relatively little progress has been reported on the effects of climate change on landslide initiation. Indeed, some relationships between landslides and climate change have been highlighted, but sign and magnitude of this correlation remain uncertain and influenced by the spatial and temporal horizon considered. This work makes use of statistically adjusted high-resolution regional climate model simulations, to study the expected changes of landslides frequency in the eastern Esino river basin (Central Italy). Simulated rainfall was used in comparison with rainfall thresholds for landslide occurrence derived by two observation-based statistical models (1) the cumulative event rainfall–rainfall duration model, and (2) the Bayesian probabilistic model. Results show an overall increase in projected landslide occurrence over the twenty-first century. This is especially confirmed in the high-emission scenario representative concentration pathway 8.5, where according to the first model, the events above rainfall thresholds frequency shift from ~?0.025 to ~?0.05 in the mountainous sector of the study area. Moreover, Bayesian analysis revealed the possible occurrence of landslide-triggering rainfall with a magnitude never occurred over the historical period. Landslides frequency change signal presents also considerable seasonal patterns, with summer displaying the steepest positive trend coupled to the highest inter-model spread. The methodological chain here proposed aims at representing a flexible tool for future landslide-hazard assessment, applicable over different areas and time horizons (e.g., short-term climate projections or seasonal forecasts). 相似文献
18.
Chao Zhou Kunlong Yin Ying Cao Emanuele Intrieri Bayes Ahmed Filippo Catani 《Landslides》2018,15(11):2211-2225
Landslide displacement prediction is an essential component for developing landslide early warning systems. In the Three Gorges Reservoir area (TGRA), landslides experience step-like deformations (i.e., periods of stability interrupted by abrupt accelerations) generally from April to September due to the influence of precipitation and reservoir scheduled level variations. With respect to many traditional machine learning techniques, two issues exist relative to displacement prediction, namely the random fluctuation of prediction results and inaccurate prediction when step-like deformations take place. In this study, a novel and original prediction method was proposed by combining the wavelet transform (WT) and particle swarm optimization-kernel extreme learning machine (PSO-KELM) methods, and by considering the landslide causal factors. A typical landslide with a step-like behavior, the Baishuihe landslide in TGRA, was taken as a case study. The cumulated total displacement was decomposed into trend displacement, periodic displacement (controlled by internal geological conditions and external triggering factors respectively), and noise. The displacement items were predicted separately by multi-factor PSO-KELM considering various causal factors, and the total displacement was obtained by summing them up. An accurate prediction was achieved by the proposed method, including the step-like deformation period. The performance of the proposed method was compared with that of the multi-factor extreme learning machine (ELM), support vector regression (SVR), backward propagation neural network (BPNN), and single-factor PSO-KELM. Results show that the PSO-KELM outperforms the other models, and the prediction accuracy can be improved by considering causal factors. 相似文献
19.
Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets 总被引：10，自引：2，他引：8
Zonghu Liao Yang Hong Jun Wang Hiroshi Fukuoka Kyoji Sassa Dwikorita Karnawati Faisal Fathani 《Landslides》2010,7(3):317-324
An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The
prototyped early warning system integrates three major components: (1) a susceptibility mapping and hotspot identification
component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide
inventory, etc.); (2) a satellite-based precipitation monitoring system () and a precipitation forecasting model (i.e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide
prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for
the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex
terrains. In use, the land-surface “where” information will be integrated with the “when” rainfall triggers by the landslide
prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data
are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation
model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and
Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts.
The system’s prediction performance has been evaluated using a local landslide inventory, and results show that the system
successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of
spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further
develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia. 相似文献
20.
Analysis of the relationship of landslide occurrence with rainfall: a case study of Wudu County,China 总被引：3，自引：0，他引：3
Shibiao Bai Jian Wang Benni Thiebes Chen Cheng Yipeng Yang 《Arabian Journal of Geosciences》2014,7(4):1277-1285
Landslides are a significant hazard in many parts of the world and represent an important geohazard in China. Rainfall is the primary triggering agent for landslides and often used for prediction slope failures. However, the relationship between rainfall and landslide occurrences is very complex. Great efforts have been made on the study of regional rainfall-induced landslide forecasting models in recent years; still, there is no commonly accepted method for rainfall-induced landslide prediction. In this paper, the quantitative antecedent soil water status (ASWS) model is applied to investigate the influence of daily and antecedent rainfall on the triggering of landslides and debris flows. The study area is Wudu County in Gansu Province, an area which exhibits frequent landslide occurrences. The results demonstrate a significant influence of high intensity rainfall events on landslide triggering. Still, antecedent rainfall conditions are very important and once a threshold of approximately 20 mm is exceeded, landslides and debris flows can occur even without additional rainfall. The study presented could also facilitate the implementation of a regional forecasting scheme once additional validation has been carried out. 相似文献