Original Landslide Research Articles

Uncertainties in landslide susceptibility prediction: Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision of the landslide spatial position. However, the spatial position error of landslide survey is inevitable, resulting in considerable uncertainties in LSP modeling. To overcome this drawback, this study explores the influence of positional errors of landslide spatial position on LSP uncertainties, and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error. This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example. The 30–110 m error-based multilayer perceptron (MLP) and random forest (RF) models for LSP are established by randomly offsetting the original landslide by 30, 50, 70, 90 and 110 m. The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics. Finally, a semi-supervised model is proposed to relieve the LSP uncertainties. Results show that: (1) The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors, and are lower than those of original data-based models; (2) 70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices, thus original landslides with certain position errors are acceptable for LSP; (3) Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

How does landslide debris grain size control sediment transport and dynamics?

The grain size of sediment delivered to a river by hillslope processes is an important factor which could impact fluvial erosion, sediment transport, and associated geomorphic changes. Grain size distribution (GSD) is increasingly recognised as an important factor for the impact of landslides on sediment pulses and long-term erosion rates. Therefore a better comprehension of grain size control on landslide-generated sediment transport and dynamics will help with understanding post-seismic fluvial processes and landscape evolution. In this study, we modelled the recovery of the Hongxi River basin affected by the Wenchuan earthquake under different landslide GSD scenarios. The CAESAR-Lisflood (CL) model was first modified to enable the spatial input of sediment GSD, which allows the model to distinguish the grain size composition of landslide sediment and the background sediment sourced from other areas. Using the modified CL model, we simulated three different landslide GSD scenarios (Original, Coarser, Finer) by altering the original landslide sediment GSD observed from a post-earthquake basin. In particular, we analysed the fate of landslide-generated sediment using a new sediment tracing function embedded in CL. This enabled us to evaluate the role of landslide GSD variation on the spatio-temporal heterogeneity of sediment transport. Our results show that the GSD of landslide material exerts a clear control on both sediment yield and channel deposition, the sediment transport of landslides disconnected to the fluvial system is more sensitive to grain size variation, while the GSD of landslides connected to the fluvial system has less impact on sediment dynamics. The duration of landslide disturbances on basin sediment dynamics can largely be dependent on the period of time (e.g., 20 years in this study basin) taken for grain coarsening (the grain size is increasing upward), which is dominated by the evacuation of fine sediments. The modelling results and findings highlight the importance of grain size distribution of landslide material and provide information to support basin sediment management and environmental risk mitigation.

A Combination of Deep Autoencoder and Multi-Scale Residual Network for Landslide Susceptibility Evaluation

Landslide susceptibility evaluation can accurately predict the spatial distribution of potential landslides, which offers great usefulness for disaster prevention, disaster reduction, and land resource management. Aiming at the problems of insufficient samples for landslide compilation, difficulty in expanding landslide samples, and insufficient expression of nonlinear relationships among evaluation factors, this paper proposes a new evaluation method of landslide susceptibility combining deep autoencoder and multi-scale residual network (DAE-MRCNN). In the first step, a deep autoencoder network was used to learn the feature expression of the original landslide data in order to acquire effective features in the data. Next, a multi-scale residual network was constructed; specifically, the model was improved into a deep residual network model by adding skip connections in the convolutional layer. In addition, the multi-scale idea was utilized to fully extract the scale characteristics of the evaluation factors. Finally, the model was used for feature training, and the results were input into the Softmax classifier to complete the prediction of landslide susceptibility. For this purpose, a machine learning method and two state-of-the-art deep learning methods, namely SVM, CPCNN-ML, and 2D-CNN, were utilized to model landslide susceptibility in Hanzhong City, Shaanxi Province. The proposed method produced the highest model performance of 0.891, followed by 0.842, 0.869, and 0.873. The experimental results show that the DAE-MRCNN method can fully express the complex nonlinear relationships among the evaluation factors, alleviate the problem of insufficient samples in convolutional neural networks (CNN) training, and significantly improve the accuracy of susceptibility prediction.

Preservation and transportation of large landslide deposits under decadal and millennial timescales in the Taiwan orogenic belt

Landsliding is one of the primary surface processes shaping the landscapes of active mountain belts. The mobilization (or stabilization) of landslide deposits plays a crucial role in fluvial dynamics, carbon output, and topographic development of active orogens. Despite numerous previous efforts, the controls on erosion or storage of large landslide deposits remain yet to fully understand. Toward this end, we investigated eight large landslides at three locations in Taiwan. Our observations of the Jiufengershan and Tsaoling landslides, the two largest co-seismic landslides of the 1999 Chi-Chi earthquake, indicate that as high as 90 % of the original landslide volume is still preserved in Jiufengershan after two decades, whereas only less than half remains in Tsaoling. The striking difference illustrates the importance of fluvial connectivity in the preservation or removal of landslide deposits. In the longer timescale, our topographic reconstructions of six fan terrace systems along the Laonung River revealed that, even with direct connectivity with one of the largest rivers in Taiwan, approximately 10 % to 30 % of the landslide deposits are still preserved in-situ after millennia of erosion. Their preservation ratio is mainly controlled by their age, their original size, and the river/hillslope processes nearby. Wider riverbeds around these terraces imply the occurrence of large landslides and subsequent deposition on riverbeds as an important mechanism of river lateral erosion. Our results suggest that a substantial portion of large landslide deposits may persist in the hinterland over thousands of years, and such long-term preservation of landslide deposits would be important in the landscape evolution and the mass balance of active orogenic belts.

Modelling and predicting landslide displacements and uncertainties by multiple machine-learning algorithms: application to Baishuihe landslide in Three Gorges Reservoir, China

Time series landslide displacement is the most critical data set to understand landslide characteristics and infer its future development. To predict landslide displacements and their quantitative uncertainties, a mathematical description of the landslide evolution should be established. This paper proposes a novel hybrid machine-learning model to predict landslide displacements and quantify their uncertainties using prediction intervals (PIs). First, wavelet de-noising and Hodrick-Prescott (HP) filters are applied to decompose the original landslide displacement into periodic, trend, and noise components. Second, a module built on the framework of bootstrap and extreme learning machine (ELM) with a hybrid grey wolf optimizer (HGWO) is used to derive a formula for modelling the periodic component of the landslide motion. Another formula for predicting the trend component of the displacement is derived by double exponential smoothing (DES). Grey relational analysis and kernel principal component analysis (KPCA) are used to select the main factors controlling the landslide motions. Finally, the two constructed formulas are used to generate the predictions of landslide displacements and the PIs. Validation and comparison experiments have been carried out on the Baishuihe landslide in the Three Gorge Reservoir of China. Results demonstrate the proposed method can achieve better performance with higher-quality PIs than other state-of-the-art methods.

Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China

This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment. At first, the landslide inventory for the study area was prepared from the high-resolution QuickBird images, and China–Brazil Earth Resources Satellite images, and field survey. Other necessary data for landslide susceptibility analysis such as the amount of rainfall, geology, and topography were also collected from the respective agencies. Twelve predisposing factors were then prepared using this available dataset. To reduce the subjectivity of models and caution in the selection of predisposing factors, and to avoid the spatial autocorrelation redundancy, certainty factor approach was attempted to optimize these twelve set of parameters. For validating the accuracy of the model, the original landslide data were randomly divided into two parts: 70% (1545 landslides) for training the model and the remaining 30% (662 landslides) for validation. The verified results showed that using the optimized predisposing factors has a higher performance than using all the original twelve factors. The results of ensemble models also showed that LSM maps prepared using binary logistic regression (accuracy is 0.848) model are more accurate than those prepared using bivariate statistical analysis (accuracy is 0.837) model. Additionally, our analysis concludes that the short duration and high-intensity rainfall, drainage density, lithology, and curvature are the major influencing factors for landslide occurrences in this case study area. This research provides an improved understanding of the mechanism of landslides caused by the typhoons for the adjoining watersheds nearby the reservoir. The preliminary understandings and approach could also be applied in similar geological and rainfall-triggered case study sites in the other parts of the world for risk mitigation.

Landslide shape, ellipticity and length‐to‐width ratios

AbstractThis paper presents a new methodology to systematically quantify the shape of landslides by their ellipticity (eL) and length‐to‐width ratio (ΛL), along with variability in these measures over different geomorphic settings. Two large substantially complete triggered‐event landslide inventories (source area and runout) are used: (i) 11 111 earthquake‐triggered landslides (1994 Northridge, USA); and (ii) 9594 rainfall‐triggered landslides (1998, Guatemala). Three methods are trialled to abstract landslide shapes to ellipses. The best method fits a convex hull to each landslide shape, approximates an ellipse with the equivalent convex hull area and perimeter, and scales this ellipse to match the original landslide area. An ellipticity index (eL) is used based on the intersection of the original landslide shape and the elliptical approximation. We consider an ellipse a reasonable approximation of landslide shape if eL ≥ 0.5 (>80% of landslides in each of the two landslide inventories). Landslides with eL < 0.5 reflect processes such as coalescence. We calculate for each landslide an ellipse length‐to‐width ratio (ΛE), finding 1.2 ≤ ΛE ≤ 15.1. The statistical distributions of ΛE are examined for ten categories of landslide area (AL). An inverse‐Gamma probability density function is found to be a good statistical model for landslide ΛE, with model parameters dependent on landslide area category. As landslide area AL increases, ΛE tends to decrease for the Northridge (earthquake‐triggered) inventory and increase for Guatemala (rainfall‐triggered). In three additional (rainfall‐triggered) landslide inventories, ΛE trends are similar to Guatemala. Our findings show that (i) an ellipse is a reasonable model for >80% of landslide shapes across different geomorphic settings, (ii) those landslides significantly deviating from an ellipse can be related to landscape processes, (iii) the length‐to‐width ratios of ellipses are non‐normally distributed, with implications for modelling landslide hazard and risk. Supporting information includes code so that the new methodology may be applied more widely. © 2018 John Wiley & Sons, Ltd.

Landslide displacement prediction technique using improved neuro-fuzzy system

Landslides are one of the most destructive forms of natural hazards, which cause serious threat to life and properties. Landslide monitoring and perdition of future landslide behavior is an important aspect of disaster mitigation, as it helps to issue early warnings and adopt suitable control measures in time. This paper proposes a technique, landslide displacement prediction using recently proposed extreme learning adaptive neuro-fuzzy inference system (ELANFIS) with empirical mode decomposition (EMD) technique. ELANFIS reduces the computational complexity of conventional ANFIS by incorporating the theoretical idea of extreme learning machines (ELM). The nonlinear original landslide displacement series first converted into a limited number of intrinsic mode functions (IMFs) and one residue. Then, the decomposed data are predicted using ELANFIS algorithm. Final prediction is obtained by summation of outputs of all ELANFIS submodels. The performances of the proposed technique are tested in Baishuihe and Liangshuijing landslides. The results show that ELANFIS with EMD model outperforms state of art methods in terms of prediction accuracy and generalization performance.

Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS)

Landslide is a major geo-environmental hazard which imparts serious threat to lives and properties. The slope failures are due to adverse inherent geological conditions triggered by an external factor. This paper proposes a new method for the prediction of displacement of step-like landslides, by accounting the controlling factors, using recently proposed extreme learning adaptive neuro-fuzzy inference system (ELANFIS) with empirical mode decomposition (EMD) technique. ELANFIS reduces the computational complexity of conventional ANFIS by incorporating the theoretical idea of extreme learning machines (ELM). The rainfall data and reservoir level elevation data are also integrated into the study. The nonlinear original landslide displacement series, rainfall data, and reservoir level elevation data are first converted into a limited number of intrinsic mode functions (IMF) and one residue. Then decomposed displacement data are predicted by using appropriate ELANFIS model. Final prediction is obtained by the summation of outputs of all ELANFIS sub models. The performance of proposed the technique is tested for the prediction Baishuihe and Shiliushubao landslides. The results show that ELANFIS with EMD model outperforms other methods in terms of generalization performance.

Volume determination of the Selo landslide complex (SW Slovenia): integrating field mapping, ground penetrating radar and GIS approaches

The Selo landslide complex in the Vipava Valley (SW Slovenia) is a large fan-shaped sedimentary body that differs significantly from other slope deposits in the area in its exceptional size and considerable runout length. The landslide is predominantly composed of carbonate gravel deposited on a flysch paleo-relief. To determine the volume and geometry of the landslide and its potential source area, we integrated geological mapping, ground penetrating radar (GPR) and GIS techniques. The landslide deposits cover an area of about 10 km2 with an average thickness of 10 m (maximum thickness reaching 56 m) and a maximum length of 5500 m. The volume of carbonate gravel was estimated from geological cross sections and GPR profiles. The base and top surfaces were interpolated by inverse distance and kriging methods, for which the Cut/Fill method was used in ESRI ArcGIS to determine the original landslide volume before the erosion. The estimated original volume is 190 × 106 m3. The recent volume after the erosion is 96 × 106 m3. The calculated volume of the Selo landslide, angle of reach θ = 10° and H/L ratio of 0.18 are in accordance with data for landslides of a comparable size. The most reasonable explanation for the development of the Selo landslide complex is a slope collapse involving the breakdown of the rock mass and the development of a high-mobility rock avalanche.

The propagation of inventory-based positional errors into statistical landslide susceptibility models

Abstract. There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based errors propagate into subsequent statistical landslide susceptibility models are scarce. The main objective of this research was to systematically examine whether and how inventory-based positional inaccuracies of different magnitudes influence modelled relationships, validation results, variable importance and the visual appearance of landslide susceptibility maps. The study was conducted for a landslide-prone site located in the districts of Amstetten and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory was available. The methodological approach comprised an artificial introduction of inventory-based positional errors into the present landslide data set and an in-depth evaluation of subsequent modelling results. Positional errors were introduced by artificially changing the original landslide position by a mean distance of 5, 10, 20, 50 and 120 m. The resulting differently precise response variables were separately used to train logistic regression models. Odds ratios of predictor variables provided insights into modelled relationships. Cross-validation and spatial cross-validation enabled an assessment of predictive performances and permutation-based variable importance. All analyses were additionally carried out with synthetically generated data sets to further verify the findings under rather controlled conditions. The results revealed that an increasing positional inventory-based error was generally related to increasing distortions of modelling and validation results. However, the findings also highlighted that interdependencies between inventory-based spatial inaccuracies and statistical landslide susceptibility models are complex. The systematic comparisons of 12 models provided valuable evidence that the respective error-propagation was not only determined by the degree of positional inaccuracy inherent in the landslide data, but also by the spatial representation of landslides and the environment, landslide magnitude, the characteristics of the study area, the selected classification method and an interplay of predictors within multiple variable models. Based on the results, we deduced that a direct propagation of minor to moderate inventory-based positional errors into modelling results can be partly counteracted by adapting the modelling design (e.g. generalization of input data, opting for strongly generalizing classifiers). Since positional errors within landslide inventories are common and subsequent modelling and validation results are likely to be distorted, the potential existence of inventory-based positional inaccuracies should always be considered when assessing landslide susceptibility by means of empirical models.

INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE

Abstract. This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.

INTEGRATING GEO-SPATIAL DATA FOR REGIONAL LANDSLIDE SUSCEPTIBILITY MODELING IN CONSIDERATION OF RUN-OUT SIGNATURE

This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.

Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine

In this paper, an M–EEMD–ELM model (modified ensemble empirical mode decomposition (EEMD)-based extreme learning machine (ELM) ensemble learning paradigm) is proposed for landslide displacement prediction. The nonlinear original surface displacement deformation monitoring time series of landslide is first decomposed into a limited number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data structure. Then, these sub-series except the high frequency are forecasted, respectively, by establishing appropriate ELM models. At last, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original landslide displacement series. A case study of Baishuihe landslide in the Three Gorges reservoir area of China is presented to illustrate the capability and merit of our model. Empirical results reveal that the prediction using M–EEMD–ELM model is consistently better than basic artificial neural networks (ANNs) and unmodified EEMD–ELM in terms of the same measurements.

Landslide Susceptibility Mapping in Yalova, Turkey, by Remote Sensing and GIS

Remote sensing and GIS (geographic information systems) techniques were used to map the landslide susceptibility in Yalova Province and to assess the landslide risk in its counties. The set of criteria investigated using GIS were determined as a result of a landslide literature survey. These criteria are geology, hydrology, terrain slope, vegetation cover, soil thickness, proximity to roads, soil moisture, and terrain aspect. Active/inactive landslide occurrences data were extracted from the 1:500,000 scale landslide maps prepared by the General Directorate of Mineral Research and Exploration of Turkey (MTA). Considering both the landslide occurrence statistics in the study area and the ranks given to the landslide-controlling criteria in previous landslide studies available in the literature, these criteria were evaluated in GIS, resulting in classification of a province into none, low, moderate, and high landslide susceptibility areas. A comparison of the resulting map with the original landslide map prepared by the MTA revealed that 91.1 percent of the locations corresponding to active creeps, slides, and flows 5 m deep. The resulting map showed new landslide risk areas as well, which were not on the original map of the MTA. These may be worthwhile to investigate while updating the original map for new potential landslide area research.

A PROJECTION APPROACH TO THE NUMERICAL ANALYSIS OF LIMIT LOAD PROBLEMS

This paper proposes a numerical scheme to approximate the solution of (vectorial) limit load problems. The method makes use of a strictly convex perturbation of the problem, which corresponds to a projection of the deformation field under bounded deformation and incompressibility constraints. The discretized formulation of this perturbation converges to the solution of the original landslide problem when the amplitude of the perturbation tends to zero. The projection is computed numerically with a multi-step gradient descent on the dual formulation of the problem.

The ruined town of Campomaggiore Vecchio, Basilicata, Italy

Abstract This paper describes the geological and geomorphological setting of the ruined town of Campomaggiore Vecchio, abandoned by its inhabitants following a large displacement landslide a little over a hundred years ago. The reasons for the original landslide, and future prospects for the historical ruins, are discussed in the following text, and the photographs show clearly the effects both of the original slide and the depredations of over a century of abandonment.

Onset of submarine debris flow deposition far from original giant landslide

Submarine landslides can generate sediment-laden flows whose scale is impressive. Individual flow deposits have been mapped that extend for 1,500 km offshore from northwest Africa. These are the longest run-out sediment density flow deposits yet documented on Earth. This contribution analyses one of these deposits, which contains ten times the mass of sediment transported annually by all of the world's rivers. Understanding how this type of submarine flow evolves is a significant problem, because they are extremely difficult to monitor directly. Previous work has shown how progressive disintegration of landslide blocks can generate debris flow, the deposit of which extends downslope from the original landslide. We provide evidence that submarine flows can produce giant debris flow deposits that start several hundred kilometres from the original landslide, encased within deposits of a more dilute flow type called turbidity current. Very little sediment was deposited across the intervening large expanse of sea floor, where the flow was locally very erosive. Sediment deposition was finally triggered by a remarkably small but abrupt decrease in sea-floor gradient from 0.05 degrees to 0.01 degrees. This debris flow was probably generated by flow transformation from the decelerating turbidity current. The alternative is that non-channelized debris flow left almost no trace of its passage across one hundred kilometres of flat (0.2 degrees to 0.05 degrees) sea floor. Our work shows that initially well-mixed and highly erosive submarine flows can produce extensive debris flow deposits beyond subtle slope breaks located far out in the deep ocean.

The Santiago Landslide and Associated Ridge-top Graben (Sackungen): Implications for Paleoseismic Landslide Studies

Some recent paleoseismic studies have focused on dating ridge-top graben deposits to evaluate the timing of paleoseismic events. By contrast, our study of the Santiago landslide demonstrates that ridge-top grabens also can be associated with aseismic, deep-seated landsliding. The Santiago landslide in Anaheim Hills, California, failed during the winter of 1992–1993 in response to elevated groundwater conditions associated with intense rainfall. The head of the active landslide included a zone of extensional deformation along the bounding ridgeline. Interpretation of historical, aerial photographs indicates that the active landslide is a re-activated, ancient, deep-seated, translational landslide and an associated ridge-top graben. Large-diameter borings within the ridge-top graben encountered thick colluvium, steeply dipping colluvium-filled fractures, and shears with normal offsets. In contrast to the rupture surface within the central part of the landslide, the basal rupture surfaces in the graben area had significantly less gouge. We interpret this contrast in gouge development as an indication that the ridge-top graben developed later than the original landslide by upslope progression of the deformation. Our limit-equilibrium, slope-stability analyses indicate that either high groundwater or seismic ground motion could have previously activated the ancient landslide and ridge-top graben. Because colluvial deposits preserved within the ridge-top graben and produced by these two different types of triggering events could be misinterpreted as representing the late Quaternary paleoseismic record, these features are not useful for paleoseismic studies unless aseismic activation can be clearly precluded.

Downslope volume enlargement of a debris slide–debris flow in the 1999 Hiroshima, Japan, rainstorm

Following a heavy rainstorm on 29 June 1999, hundreds of slope failures occurred in granitic mountains in Hiroshima Prefecture, Japan. Among these events, a highly mobile landslide (termed the Kameyama landslide in this paper), which occurred in Kameyama area of Hiroshima city, was the most catastrophic, and was investigated in detail. The displaced soil mass from the source area of this landslide traveled about 300 m and deposited a volume more than 10 times as great as that in the source area. The landslide originated in and traversed a valley-shaped concave slope covered by pre-existing colluvial debris deposits. In addition, a spring was visible in the source area and very shallow ground water was observed in an observation pit dug in the source area. Thus, it is inferred that the ground-water table rose quickly during the rainfall, and that this rise triggered the slope failure in the source area. Based on a field survey along the landslide cross section, a possible explanation for the mechanism of the landslide was obtained: the displaced soil mass from the source area impacted the debris deposit in the path of the landslide, thus triggering liquefaction failure of the saturated part of debris. The original landslide and the liquefied debris then moved downslope as a single mass. To examine this assumption, ring-shear tests were performed on samples taken from the source area. Undrained ring-shear tests on the saturated samples showed that the sample is highly liquefiable, and liquefaction failure could have been triggered in the debris deposits by a very small impact from the displaced soil mass of the initial failure. In addition, laboratory tests simulating the impacts on the debris deposits at natural water content, i.e., unsaturated (at the survey time, 2 days after the failure) showed that although shear failure could be caused by the assumed impact force, the displaced soils stopped after a few centimeters displacement, indicating that existence of a saturated zone in debris deposits is prerequisite for this kind of failure.