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Selecting appropriate mapping units is vital for landslide susceptibility mapping as well as pattern investigation, given that various units-based analyses extensively control the prediction performances. This paper investigated the landslide development through the interpretation and field surveys along the Yunling–Yanjing segment of the Lancang River in southeastern Tibet, and fulfil LSM with the consideration of 15 conditioning factors. Two grid unit methods (single-point and multi-point patterns) and two slope unit methods were comparatively analyzed for model training and mapping of landslide susceptibility via machine learning algorithms. The results suggest that the landslides are preferentially distributed in an elevation range of 2000–4000 m, in a slope range of 20–40°, a local relief range of 1000–2500 m, and southwest-oriented slopes. The data extracted by the multi-point method denotes a higher representation of landslide development features. All models possessed positive prediction ability for landslide susceptibility, and the multi-point method based on grid unit performed the best with the AUC exceeding 0.9. The best-performing models indicated that zones of high and very high susceptibility mainly distributed adjacent to the mainstem and some tributaries of the Lancang River. Furthermore, the distribution of "safety islands" (the slopes less prone to landslides) along National Highway G214 was reasonably illustrated as well, which provides a hazard predictability for such an important transportation corridor along the deeply-incised valley in the Lancang River. This study demonstrates a theoretical basis for the regional disaster prevention and mitigation for human activity, and provides methodological references for landslide susceptibility evaluation in similar mountainous areas over the world. [ABSTRACT FROM AUTHOR]

Long-runout landslides can be extremely hazardous and unpredictable. The prediction of long-runout landslides is challenging because it is unclear what factors control their mobility, and it is unclear how to best measure their runout given that they traverse changing topography. In this paper, we document the development and statistical evaluation of a Landslide Runout Score (LRS) system to predict short, medium, and long runout using a new mobility measure, the unitless Runout Number L/A1/2 (where L is landslide length and A is landslide area). The Runout Number has previously been correlated to three geomorphological factors (planimetric curvature, sand content, and upslope contributing area normalized to landslide area), so the LRS uses these factors as inputs. These factors are readily calculated using geographic information systems (GIS) and publicly available data sources. The LRS system predicts runout categories (short, medium, and long) with one-vs.-all accuracies (i.e., percentage of correct predictions within a given class) of 75, 58, and 72%, respectively, for a total weighted accuracy of approximately 65%. The results of this work are summarized in a worksheet that can be used by geologists and engineers to develop preliminary predictions of landslide runout behavior, which can be incorporated into or used alongside regional-scale landslide hazard assessments. [ABSTRACT FROM AUTHOR]

This paper presents a geomechanical model test for analyzing rainfall-induced slope instability. The proposed method integrates HCA726S tilt sensors, micro soil pressure sensors, pore water pressure sensors, three-dimensional laser scanners, and other instruments to monitor tilt angle, stress, vertical displacement, and morphology of the slope. The tilting deformation and stress response were analyzed, and the landslide evolution process is summarized. Based on the tilt deformation curves obtained from the tilt sensors, it can be concluded that shallow landslide exhibits creep characteristics, and the creep behavior can be divided into three stages: primary, secondary, and tertiary creep. A linear relationship between the reciprocal tilting rate and time was observed during the primary and tertiary creep stages, and a linear equation was established. A reciprocal tilting rate of 0 min/° was then input into the linear equation to obtain the time of initial and complete slope failure. The predicted landslide failure times were consistent with the actual recorded times obtained from the model tests. Thus, tilt deformation is a significant early warning indicator or slope instability and could be used to predict landslides for disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The monitoring data of landslide deformation are characterized by non-smooth, nonlinear and random changes, and the cumulative changes of the monitored objects have both monotonous growth trends and short-term fluctuations. The GM(1,1) model can get better results only when the data series are monotonous. Due to the limitations of the model, the prediction accuracy of the GM(1,1) model is limited to a certain extent. An improved algorithm based on the GM(1,1) model and the empirical mode decomposition (EMD-GM(1,1) model) for deformation prediction was presented to improve the forecast accuracy in this paper. Firstly, EMD was used to effectively separate the nonlinear high-frequency and low-frequency components hidden in the deformation sequence; then the moving average method was applied to build a prediction model for high-frequency component, and the GM(1,1) was applied to build the prediction model for low-frequency one according to the characteristics of each component; finally, the predicted value of each component was superimposed. The experimental results indicate that the optimized EMD-GM(1,1) model combines the advantages of the two models to separate effectively the different frequency components of the deformation sequence, which has higher prediction accuracy. Compared with the conventional GM(1,1) model, DGM(2,1) model and the Linear fitting model, the proposed model could satisfactorily describe the landslide deformation prediction practically. [ABSTRACT FROM AUTHOR]

An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis (SSA) and stack long short-term memory (SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine (SVM) and auto regressive integrated moving average (ARIMA). The mean relative error (MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error (RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides. [ABSTRACT FROM AUTHOR]

Early Warning Systems (EWS) are efficient tools for preventing and mitigating the risks associated to landslides occurrence. In this paper, an integrated methodology for landslides' analysis is presented and described. Such methodology is aimed at the creation of early warning systems and is based on the integration between a modern monitoring technique, such as the Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR), along with advanced numerical modelling. The paper also shows the application of the proposed methodology to the case study of a rockslide in central Italy. The integration between monitoring data, thanks to a GBInSAR survey and advanced numerical simulations with the combined Finite-Discrete Elements Method (FDEM), allowed for the definition of a set of surface velocity thresholds to be adopted for the long-term monitoring of the landslide and for the creation of an effective EWS. [ABSTRACT FROM AUTHOR]

Landslide failure time prediction is considered a challenging issue in landslide research. A significant goal of landslide research is to provide scientific and accurate prediction methods. In this paper, the short-term forecasting of landslides (STFL) model is proposed by analyzing landslide deformation characteristics in a known evolution process. The landslides on the southern slope of the West open pit mine (SSWOPM) in Liaoning province, northeast China, and Huangci in Gansu province, northwest China, were selected as the study cases. After pre-processing, which include eliminating abnormal data (t test), adding missing data (cubic spline interpolation), and smoothing noisy data (Savitzky–Golay filters), the displacement data are assembled to serve as the model's input parameters. The parameters of STFL can be obtained through the Levenberg–Marquardt (LM) algorithm. Using the forecasting criterion, the time of failure of landslides can be predicted and determined. Forecasting results provide evidence that the STFL model can achieve an accurate prediction of landslide displacement (correlation coefficient R2 > 0.99). The forecasting results of the SSWOPM and Huangci landslides indicate that the forecasting failure time of the STFL model is March 10, 2014 and January 31, 1995, respectively, which are near the actual failure time compared with those obtained using the Verhulst model. This finding indicates that the new method has excellent reliability and accuracy in landslide prediction and a robust description of the landslide movement. [ABSTRACT FROM AUTHOR]

As increased pore pressure due to rainwater infiltration is the primary triggering factor for landslides, rainfall is normally established as the first condition that allows definition of the predictability of their occurrence over the short term for a determined location. It is therefore common to propose landslide occurrence predictions based on awareness of accumulated background rainfall from historic data analysis. This paper proposes that landslide occurrence be predicted by quantitatively estimating their probabilities based on accumulated rainfall during a pair of time frames (e.g., 24 and 72 h) prior to the event, with selection determined by statistical dependence readings. This method was applied to the Quitandinha river basin region in the municipality of Petrópolis, Brazil, using background data from 2003 to 2009. It was observed that landslide occurrence presented the highest relevance level with the two accumulated rainfall periods of 24 and 96 h, and it was possible to estimate the probability of occurrence of at least one, three, or five landslides depending on the accumulated rainfall rates during these time frames. [ABSTRACT FROM AUTHOR]

In a strong earthquake, not only a large number of coseismic landslides are triggered, but relaxation and cracks in the rocks and soils are induced, which make these rocks and soils vulnerable to instability during subsequent rainfall; thus, strong earthquakes always have long-term effects on landslides. The geo-hazards along the Zhaotong-Qiaojia road in the 2014 Ludian earthquake (Ms 6.5) of Yunnan Province, China, were investigated, and the Ganjiazhai giant landslide was chosen as a case study. First, using the limit equilibrium analysis and Newmark method, the critical seismic intensity of the landslide before the earthquake was evaluated. Secondly, the dynamic failure process of the landslide under the measured ground motion was simulated with the discontinuous deformation analysis method. Lastly, based on stress–seepage coupling analysis and precipitation data from Ludian meteorological station, the stability of the landslide during subsequent rainfall after the earthquake was predicted. The results show that the critical seismic intensity was within degrees VIII–IX, which is consistent with the results of the earthquake damage investigation. The dynamic failure process can be divided into four stages, and four scarps formed; the potential sliding zones during the subsequent rainfall were at the first scarp and the fourth scarp, and their critical rainfall amounts were 35–40 mm and 55–60 mm, respectively. In this paper, failure process simulation and stability prediction of the landslide before, during, and after the strong earthquake are presented, which provide analysis methods for the dynamic stability of landslides in the meizoseismal area. [ABSTRACT FROM AUTHOR]

Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorological-geotechnical early warning system GRAPES-LFM (GRAPES: Global and Regional Assimilation and PrEdiction System; LFM: Landslide Forecast Model), basing on the GRAPES model and the landslide predicting model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model) for predicting rainfall-triggered landslides. This integrated system is evaluated in Dehua County, Fujian Province, where typhoon Bilis triggered widespread landslides in July 2006. The GRAPES model runs in 5 km×5 km horizontal resolution, and the initial fields and lateral boundaries are provided by NCEP (National Centers for Environmental Prediction) FNL (Final) Operational Global Analysis data. Quantitative precipitation forecasting products of the GRAPES model are downscaled to 25 m×25 m horizontal resolution by bilinear interpolation to drive the TRIGRS model. Results show that the observed areas locate in the high risk areas, and the GRAPES-LFM model could capture about 74% of the historical landslides with the rainfall intense 30mm/h. Meanwhile, this paper illustrates the relationship between the factor of safety (FS) and different rainfall patterns. GRAPES-LFM model enables us to further develop a regional, early warning dynamic prediction tool of rainfall-induced landslides. [ABSTRACT FROM AUTHOR]

One of the important issues in identifying susceptible landslide locations is to select effective factors in the landslide of studied areas. The complexity of this issue is due to the fact that each of the factors is a part of the desired criteria, and the selection among them is a multi-criteria decision-making problem, requiring a structured and systematic approach. In the study area, the presence of large outcrops, as well as relatively thick soils formed on the formations, has caused a significant level of the province to potentially have the conditions for the movement of the tides. In addition, the situation is exacerbated by inappropriate use of natural resources, and construction of numerous rural and forest roads. In this paper, multi-criteria decision-making (MCDM) such as the fuzzy analytical hierarchy process (FAHP) and Dempster–Shafer (DeS) method was used to detect susceptible landslide locations, and output maps were verified applying the prediction–area (P–A) method for landslide purposes. The study has utilized P–A plot to evaluate and compare the obtained results of FAHP and DeS methods. The results indicated that the normalized density value achieved from P–A plots was significantly higher for the FAHP than the corresponding value for the DeS. In other words, FAHP, compared to DeS, resulted in the successful detection of landslide occurrences and more accurate classification of the study area. [ABSTRACT FROM AUTHOR]

The analysis of landslides using monitoring data is a commonly used method for landslide prediction and early warning. However, the loss of data due to breakdown of the monitoring equipment or interference of external factors is unavoidable in the process of monitoring landslide data. An interpolation algorithm can supplement and correct the data to solve the problem of data loss. This multi-position and long-term monitoring data is non-linear, multidimensional and time-varying, which makes it difficult for the commonly used spatiotemporal kriging interpolation methods to construct an appropriate model straightaway. This paper presents a non-uniform spatiotemporal kriging interpolation method. It breaks through the restriction of Euclidean distance in the spatial dimension while breaking away from linear relationship in the temporal dimension. The spatiotemporal deformation field model is constructed using spatiotemporal optimal weights combination and subsequently optimized by particle swarm optimization algorithm. The ordinary kriging interpolation is extended to the non-uniform spatiotemporal kriging interpolation under the spatiotemporal constraints condition. This method is successfully applied to the interpolation of the monitoring data of landslide displacement. It provides better data for studies of landslide disasters and is of great practical significance for prevention and prediction of landslide disasters. [ABSTRACT FROM AUTHOR]

Perceptions of risk are a key issue when seeking to develop systems, practices and policies to protect local populations. This is particularly evident when risk mitigation strategies involve non-structural measures such as relocation and warning systems which presuppose the active involvement of the communities in question. This study adopts an interdisciplinary approach to studying the perceptions, knowledge and opinions on landslide risk amongst residents in Sarno, a small town in Southern Italy which was hit by disastrous landslides on 5-6 May 1998. The paper presents the results of a survey conducted in the months of March, April and May 2013 using a purpose-designed questionnaire. The survey was conducted using face-to-face interviews with 100 residents, 60 of whom live inside the so-called 'red zone', a territory declared at high residual risk soon after the events of 1998. The questionnaire included questions relating to perceived risk exposure, trust in institutions responsible for risk management, evaluations of risk mitigation measures and the early-warning strategy. The results of the study clearly emerges, amongst other issues, that the organisms which are responsible for risk management in Sarno need to develop more effective communication strategies in order to transmit knowledge about the actions implemented to reduce landslide risk in the area. [ABSTRACT FROM AUTHOR]

The recognition of landslides and making their inventory map are considered to be urgent tasks not only for damage estimation but also for planning rescue and restoration activities. Owing to the capability of synthetic aperture radar (SAR) for day-and-night and all-weather imaging, various studies utilizing SAR data for landslide detection have been reported to date. Among the detection methods utilizing SAR data, those based on height differences accompanying landslides are attractive and should be further improved, since they can directly contribute to damage estimation through a volumetric estimation of landslides. In this context, we propose in this paper a landslide detection method utilizing height differences derived from pre- and post-event SAR digital elevation models (DEMs) combined with amplitude differences. The proposed method was applied to the landslides triggered by the 2016 Kumamoto earthquake. The application results demonstrate that SAR DEMs with a high altitudinal resolution can improve the detection ability and that the incorporation of the amplitude differences is effective for decreasing the number of false detections. Although the reliability of the proposed method is deemed moderate when evaluated on the basis of the kappa coefficients derived through an accuracy assessment, most of the outliers are correctly filtered out and large- and medium-scale landslides are detected. Therefore, the inventory maps derived from the proposed method are thought to be effective at the initial stage of planning rescue and restoration activities. [ABSTRACT FROM AUTHOR]

Landslide deformation prediction has significant practical value that can provide guidance for preventing the disaster and guarantee the safety of people's life and property. In this paper, a method based on recurrent neural network (RNN) for landslide prediction is presented. Genetic algorithm is used to optimize the initial weights and biases of the network. The results show that the prediction accuracy of RNN model is much higher than the feedforward neural network model for Baishuihehe landslide. Therefore, the RNN model is an effective and feasible method to further improve accuracy for landslide displacement prediction. [ABSTRACT FROM AUTHOR]

An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. CHAID as a multivariate method has an automatic classification capacity to analyze large numbers of landslide conditioning factors. Moreover, it results two or more nodes for each independent variable, where every node contains numbers of presence or absence of landslides (dependent variable). Other DT methods such as Quick, Unbiased, Efficient Statistic Tree (QUEST) and Classification and Regression Trees (CRT) are not able to produce multi branches based tree. Thus, the main objective of this paper is to use CHAID method to perform the best classification fit for each conditioning factors, then, combined it with logistic regression (LR) to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. In the first step, a landslide inventory map with 296 landslide locations were extracted from various sources over the Pohang-Kyeong Joo catchment (South Korea). Then, the inventory was randomly split into two datasets, 70 % was used for training the models, and the remaining 30 % was used for validation purpose. Thirteen landslide conditioning factors were used for the susceptibility modeling. Then, CHAID was applied and revealed that some conditioning factors such as altitude, soil drain, soil texture and TWI, as terminal nodes and reflected the best classification fit. Then, a proposed ensemble technique was applied and the interpretations of the coefficients showed that the relationship between the decision tree branch nodes distance from drain, soil drain, and TWI, respectively, leads to better consequences assessment of landslides in the current study area. The validation results showed that both success and prediction rates, 75 and 79 %, respectively. This study proved the efficiency and reliability of ensemble DT and LR model in landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Sliding mass of landslides highly endangered the area along travel path, especially landslides with long travel distance. It is necessary to develop an effective prediction model for preliminarily evaluating landslide travel distance so as to improve disaster prevention and relocation. This paper collected 54 landslides with 347-4,170 m travel distance triggered by the 2008 Wenchuan earthquake to discuss the effectiveness of various influential factors on landslide travel distance and obtained an empirical model for its prediction. The results revealed that rock type, sliding source volume, and slope transition angle were the predominant factors on landslide travel distance. The validity of proposed model was verified by the satisfactory agreement between observations and predictions. Therefore, this model might be practically applicable in Wenchuan earthquake area and other similar geomorphological and geological regions. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to exploit information obtained from satellite SAR data to detect and monitor instability phenomena affecting hilly and scarcely urbanized areas, overtaking some of the restrictions due to the presence of thick vegetation. To this end, phase and amplitude analyses of COSMO-SkyMed SAR data were carried out on two landslides located in the North-Eastern Italian pre-Alps: Cischele roto-translational slide and Val Maso rotational slide-earth flow. In the first case, the careful choice of processing parameters allowed to evaluate landslide displacement fields considering the phase difference between SAR acquisitions. In the second case, the speed of movement and the deep changes in morphology and vegetation induced by the landslide did not allow to apply DInSAR techniques; in this case the variation in the amplitude between SAR acquisitions allowed to detect the area affected by the instability. Obtained results show that methods and techniques to analyse satellite SAR data could be further refined in order to provide useful tools for landslide mapping and monitoring. [ABSTRACT FROM AUTHOR]

The denudation of young reliefs prone to landslides can have severe consequences for society and the environment. However, landslide databases and the additional information (landslide type, date and triggering factors) necessary to deal with landslide hazard assessment and the development of effective and reliable landslide warning systems are usually scarce or non-existent. In this way, by taking into account the date of landslide events and by expanding the analysis of cumulative rainfall from these dates to a broader time period that includes the days or months leading up to a landslide, the corresponding triggering rainfall threshold can be assessed more accurately. In this paper, a methodology based on a partial duration series analysis applied to rainfall variables allows the possibility to better understand precipitation patterns. Another advantage of analysing precipitation variables within a broader time period is the ability to identify greater accuracy rainfall anomalies such as extreme rainfalls with their return period related to a low number of dated landslide events (in this case, 20 landslide events). The landslide spatial distribution within a regional area requires the processing and analysis of data from multiple long-term historical daily rainfall records from different rainfall gauges, which notably increase the number of calculations to be dealt with. To overcome this inconvenience, these processes were streamlined by using macro-automation. Additionally, different rainfall durations can be interactively identified from graphical outputs that show anomalies on more than one rainfall variable after applying this methodology. Among these rainfall variables, the antecedent accumulated rainfall (A1) was found to be the most suitable to apply the occurrence probability analysis. When compared to other variables, the return period values of A1 were determined to be conservative, neither too high nor too low. Using this approach, the return period curve was shown to be an important graphic object in detecting uncommon rainfalls that are contemporaneous or previous to landslides. The relevant findings of this research show a power-law trend with α = 88.005 and β = 0.69 in the correlation of intensity and duration associated with antecedent cumulative rainfall (A1) anomalies. The mean return period for these anomalies resulted in 12.4 years, while for 50 % of the landslides, the recurrence interval was estimated in less than or equal to 3.6 years. In addition, significant differences were found between catalogued slope-cut failures and natural landslides. Moreover, differences were also found between simplified types of natural landslides. [ABSTRACT FROM AUTHOR]

The Ripley Landslide is a soil slide moving on a fully developed, sub-horizontal, shear surface. The landslide represents a hazard for two important railway lines across its toe. The landslide is being monitored by an array of displacement measurement systems including GPS units, a ShapeAccelArray (SAA), satellite InSAR, and crack extension metres, as well as an array of piezometers targeting pore water pressures in the vicinity of the shear surface. The displacement monitoring system shows an annual cycle of slope deformations most active between September and May. Annual horizontal displacements range between 60 and 100 mm. Vertical displacements range between 20 and 80 mm of settlement. The average horizontal velocities during the active displacement period are between 0.2 and 0.35 mm/day, with maximum velocities of up to 0.6 mm/day. This paper describes the development of an early warning system based on landslide displacement measurements. The system is based on GPS and SAA measurements, which provide near real-time displacement data. The early warning system focuses on detecting changes in landslide annual displacement cycles and potential accelerations, as well as the effects of slope deformation on the railway alignment. As such, the system monitors both the integrity and performance of the slope. [ABSTRACT FROM AUTHOR]

This paper presented the reactivated mechanism of Badu landslide according to site investigations, monitoring, finite element and limit equilibrium analyses. Finite element analysis incorporating a stability analysis with the finite element stress-based method revealed a large railway cut triggered a localized slide in the upper part of Badu landslide rather than a global failure, and piles in three rows were effective in stabilizing the reactivated zone. Monitoring results and the limit equilibrium analysis showed that the landslide underwent multi-instabilities rather than a global failure due to a rise in groundwater table in rainy season. The mechanism of reactivated Badu landslide was finally proposed, i.e., 'grading creeping-sliding' mechanism, which helped designers to propose the most suitable mitigation measures to stabilize the landslide. [ABSTRACT FROM AUTHOR]

Mountain areas in the southern western corner of the Kingdom of Saudi Arabia frequently suffer from various types of landslides due to rain storms and anthropogenic activities. To resolve the problem related to landslides, landslide susceptibility map is important as a quick and safe mitigation measure and to help making strategic planning by identifying the most vulnerable areas. This paper summarizes findings of landslide susceptibility analysis at Al-Hasher area, Jizan, KSA, using two statistical models: frequency ratio and index of entropy models with the aid of GIS tools and remote sensing data. The landslide locations (inventory map) were identified in the study area using historical records, interpretation of high-resolution satellite images that include Geo-Eye in 2.5 m and Quickbird in 0.6m resolution, topographic maps of 1:10,000 scale, and multiple field investigations. A total of 207 landslides (80% out of 257 detected landslides) were randomly selected for model training, and the remaining 50 landslides (19%) were used for the model validation. Ten landslide conditioning factors including slope angle, slope-aspect, altitude, curvature, lithology, distance to lineaments, normalized difference vegetation index (NDVI), distance to roads, precipitation, and distance to streams, were extracted from spatial database. Using these conditioning factors and landslide locations, landslide susceptibility and weights of each factor were analyzed by using frequency ratio and index of entropy models. Our findings showed that the existing landslides of high and very high susceptibility classes cover nearly 80.4% and 79.1% of the susceptibility maps produced by frequency ratio and index of entropy models respectively. For verification, receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) were calculated for success and prediction rates. For success rate the results revealed that for the index of entropy model (AUC = 77.9%) is slightly lower than frequency ratio model (AUC = 78.8%). For the prediction rate, it was found that the index of entropy model (AUC = 74.9%) is slightly lower than the frequency ratio model (AUC = 76.7%). The landslide susceptibility maps produced from this study could help decision makers, planners, engineers, and urban areas developers to make suitable decisions. [ABSTRACT FROM AUTHOR]

Landslide susceptibility (LS) assessment by indirect approaches presents some limitations due to (1) the tendency to simplify the environmental factors (i.e., variables) and (2) the assumptions that landslides occur under the same combination of variables for a study site. Recently, some authors have discussed the interest to introduce expert knowledge in the indirect approaches in order to improve the quality of indirect LS maps. However, if the results are reliable, the procedures used seem fastidious and a very good knowledge of the study site is essential. The objectives of this paper are to discuss a methodology to introduce the expert knowledge in the indirect mapping process. After the definition of the expert rules associated to three landslide types, several indirect LS maps are produced by two indirect exploratory approaches, based on fuzzy set theory and on a modification of a bivariate method called expert weight of evidence. Then, the indirect LS maps are confronted to a landslide inventory and a LS map produced by a direct approach. The analyses indicate that the methodology used to introduce the expert rules in the mapping process increases the predictive power of indirect LS map. Finally, some indications about advantages and drawbacks of each approach are given to help the geoscientist to introduce his expert knowledge in the landslide susceptibility mapping process. [ABSTRACT FROM AUTHOR]

In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility mapping on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations. [ABSTRACT FROM AUTHOR]

The primary objective of landslide susceptibility mapping is the prediction of potential landslides in landslide-prone areas. The predictive power of a landslide susceptibility mapping model could be tested in an adjacent area of similar geoenvironmental conditions to find out the reliability. Both the 2008 Wenchuan Earthquake and the 2013 Lushan Earthquake occurred in the Longmen Mountain seismic zone, with similar topographical and geological conditions. The two earthquakes are both featured by thrust fault and similar seismic mechanism. This paper adopted the susceptibility mapping model of co-seismic landslides triggered by Wenchuan earthquake to predict the spatial distribution of landslides induced by Lushan earthquake. Six influencing parameters were taken into consideration: distance from the seismic fault, slope gradient, lithology, distance from drainage, elevation and Peak Ground Acceleration (PGA). The preliminary results suggested that the zones with high susceptibility of coseismic landslides were mainly distributed in the mountainous areas of Lushan, Baoxing and Tianquan counties. The co-seismic landslide susceptibility map was completed in two days after the quake and sent to the field investigators to provide guidance for rescue and relief work. The predictive power of the susceptibility map was validated by ROC curve analysis method using 2037 co-seismic landslides in the epicenter area. The AUC value of 0.710 indicated that the susceptibility model derived from Wenchuan Earthquake landslides showed good accuracy in predicting the landslides triggered by Lushan earthquake. [ABSTRACT FROM AUTHOR]

This paper proposes a WD-GA-LSSVM model for predicting the displacement of a deepseated landslide triggered by seasonal rainfall, in which wavelet denoising (WD) is used in displacement time series of landslide to eliminate the GPS observation noise in the original data, and genetic algorithm (GA) is applied to obtain optimal parameters of least squares support vector machines (LSSVM) model. The model is first trained and then evaluated by using data from a gentle dipping (~2°-5°) landslide triggered by seasonal rainfall in the southwest of China. Performance comparisons of WD-GA-LSSVM model with Back Propagation Neural Network (BPNN) model and LSSVM are presented, individually. The results indicate that the adoption of WD-GA-LSSVM model significantly improves the robustness and accuracy of the displacement prediction and it provides a powerful technique for predicting the displacement of a rainfall-triggered landslide. [ABSTRACT FROM AUTHOR]

Landslides have claimed many lives and caused significant economic losses in recent years. Therefore, assessing the potential risk for landslide hazard prevention and mitigation is vital and the landslide travel distance is a key aspect in this process. In this study, the group method of data handling (GMDH) was used to predict the landslide travel distance. A total of 111 landslide datasets collected from the literature were used to construct and validate the proposed GMDH model. Five parameters were selected as the input parameters, including initial slope angle, body height, average body thickness, the logarithm of source volume, and body aspect ratio. The GMDH model was compared with the gene expression programming (GEP) model as well as four empirical models proposed by other researchers. The comparison results demonstrated that the predicted results of the GMDH model agreed well with the measured values, with a correlation coefficient of 0.9123, which was higher than the GEP model and the four empirical models. It was concluded that the proposed GMDH model had great potential in estimating the landslide travel distance. [ABSTRACT FROM AUTHOR]

Landslide risk analysis is a common geotechnical evaluation and it aims to protect life and infrastructure. In the case of sensitive clay zones, landslides can affect large areas and are difficult to predict. Here we propose a methodology to determine the landslide hazard across a large territory, and we apply our approach to the Saint-Jean-Vianney area, Quebec, Canada. The initial step consists of creating a 3D model of the surficial deposits of the target area. After creating a chart of the material electrical resistivity adapted for eastern Canada, we applied electric induction to interpret the regional soil. We transposed parameter values obtained from the laboratory to a larger scale, that is to a regional slope using the results of a back analysis undertaken earlier, on a smaller slide within the same area. The regional 3D model of deposits is then used to develop a zonation map of slopes that are at risk and their respective constraint areas with the study region. This approach allowed us to target specific areas where a more precise stability analysis would be required. Our methodology offers an effective tool for stability analysis in territories characterized by the presence of sensitive clays. [ABSTRACT FROM AUTHOR]

Landslide vulnerability prediction maps are among the most important tools for managing natural hazards associated with slope stability in river basins that affect ecosystems, properties, infrastructure and society. Landslide events are among the most hazardous patterns of slope instability in the coastal mountains of Syria. Thus, the main goals of this research are to evaluate the performance of three different statistical outputs: Frequency Ratio (FR), Statistical Index (SI) and Index of Entropy (IoE) and therefore map landslide susceptibility in the coastal region of Syria. To this end, we identified a total of 446 locations of landslide events, based on the preliminary inventory map derived from fieldwork and high-resolution imagery surveys. In this regard, 13 geo-environmental factors that have a high influence on landslides were selected for landslide susceptibility mapping. The results indicated that the FR method outperformed the SI and IoE models with a high AUC of 0.824 and better adaptability, followed by the SI with 0.791. According to the SCAI values, although the FR model achieved the best reliability, the other two models also showed good capability in determining landslide susceptibility. The result of FR-based modelling showed that 18.51 and 19.98% of the study area fall under the high and very high landslide susceptible categories, respectively. In the map generated by the SI method, about 36% of the study area is classified as having high or very high landslide sensitivity. In the IoE method, whereas 14.18 and 25.62% of the study area were classified as "very high susceptible" and "high susceptible," respectively. The relative importance analysis demonstrated that the slope aspects, lithology and proximity to roads effectively motivated the acceleration of slope material instability and were the most influential in both the FR and SI models. On the other hand, the IoE model indicated that the proximity to faults and roads, along with the lithology factor, were important influences in the formation of landslide events. As a result, the statistical bivariate models-based landslide mapping provided a reliable and systematic approach to guide the long-term strategic planning procedures in the study area. [ABSTRACT FROM AUTHOR]

Seismic slope stability analysis at regional scale (e.g., landslide hazard mapping, infrastructure slope management) is essential in areas that are susceptible to earthquake. Analytical infinite slope model is well suited and widely used for regional analyses which involve hundreds and thousands of slopes. Use of infinite slope model implicitly assumes shallow translational failure mechanism. However, earthquake can also cause deep rotational failure, which is more destructive and should also be considered. This study aims to develop closed-form solutions that can be efficiently used for seismic slope stability analysis at regional scale considering deep rotational failure mechanism. The existing research is critically reviewed first, and their shortcomings are identified. In contrast to the conventional "two-step" approach, a more efficient and versatile "one-step" approach is proposed in this study. The "one-step" approach can be used to calculate factor of safety for pseudo-static analysis and yield coefficient for displacement-based seismic analysis. To consider the effects of uncertainty from soil properties and seismic loading, the "one-step" approach is further extended for probabilistic analysis and application is demonstrated through a case study. The efficiency and versatility of the proposed "one-step" approach make it well suited for regional seismic slope stability analysis. [ABSTRACT FROM AUTHOR]

Landslides can cause extensive damage, particularly those triggered by earthquakes. The current study used back propagation of an artificial neural network (ANN) to conduct risk studies on landslides in the area affected by the Manjil-Rudbar earthquake in Iran in 1990 (M = 7.7). Newmark displacement analysis was used to develop an earthquake-induced landslide hazard map for the blocks representing Chahar-Mahal and Chalkasar near the earthquake epicenter, an area of 309 km2. The input data included soil cohesion, soil friction angle, unit weight of soil, unit weight of water, distance from hypocenter, slope, and earthquake magnitude as effective parameters for landslide occurrence. The hazard map was compared with an inventory map and other research findings. The results indicated that the landslides predicted by ANN covered 50% of the inventory map of the study area (2088 of 4097 slide cells). The results of the current study suggest that the ANN method is relatively efficient for accurate prediction of landslides. [ABSTRACT FROM AUTHOR]

Uncertainties in parameters of landslide susceptibility models often hinder them from providing accurate spatial and temporal predictions of landslide occurrences. Substantial contribution to the uncertainties in landslide assessment originates from spatially variable geotechnical and hydrological parameters. These input parameters may often vary significantly through space, even within the same geological deposit, and there is a need to quantify the effects of the uncertainties in these parameters. This study addresses this issue with a new three-dimensional probabilistic landslide susceptibility model. The spatial variability of the model parameters is modeled with the random field approach and coupled with the Monte Carlo method to propagate uncertainties from the model parameters to landslide predictions (i.e., factor of safety). The resulting uncertainties in landslide predictions allow the effects of spatial variability in the input parameters to be quantified. The performance of the proposed model in capturing the effect of spatial variability and predicting landslide occurrence has been compared with a conventional physical-based landslide susceptibility model that does not account for three-dimensional effects on slope stability. The results indicate that the proposed model has better performance in landslide prediction with higher accuracy and precision than the conventional model. The novelty of this study is illustrating the effects of the soil heterogeneity on the susceptibility of shallow landslides, which was made possible by the development of a three-dimensional slope stability model that was coupled with random field model and the Monte Carlo method. [ABSTRACT FROM AUTHOR]

On 22 March 2014, a massive, catastrophic landslide occurred near Oso, Washington, USA, sweeping more than 1 km across the adjacent valley flats and killing 43 people. For the following 5 weeks, hundreds of workers engaged in an exhaustive search, rescue, and recovery effort directly in the landslide runout path. These workers could not avoid the risks posed by additional large-scale slope collapses. In an effort to ensure worker safety, multiple agencies cooperated to swiftly deploy a monitoring and alerting system consisting of sensors, automated data processing and web-based display, along with defined communication protocols and clear calls to action for emergency management and search personnel. Guided by the principle that an accelerating landslide poses a greater threat than a steadily moving or stationary mass, the system was designed to detect ground motion and vibration using complementary monitoring techniques. Near real-time information was provided by continuous GPS, seismometers/geophones, and extensometers. This information was augmented by repeat-assessment techniques such as terrestrial and aerial laser scanning and time-lapse photography. Fortunately, no major additional landsliding occurred. However, we did detect small headscarp failures as well as slow movement of the remaining landslide mass with the monitoring system. This was an exceptional response situation and the lessons learned are applicable to other landslide disaster crises. They underscore the need for cogent landslide expertise and ready-to-deploy monitoring equipment, the value of using redundant monitoring techniques with distinct goals, the benefit of clearly defined communication protocols, and the importance of continued research into forecasting landslide behavior to allow timely warning. [ABSTRACT FROM AUTHOR]

Loesses are widely distributed all over the world, especially in China. Meanwhile, long-term agricultural irrigation and extreme climate changes have led to frequent geological disasters in the Heifangtai area of Lanzhou, Gansu, China. Of these, landslides are one of the most frequent types of disasters that are harmful to humans and the environment. A landslide is closely related to the lithology, structure, and groundwater of the site and consists of a combination of structure units divided by discontinuities, such as faults, joints, and fissures. Therefore, geological surveys, engineering drilling, and trenching are typically used to identify the stratum lithology, structure, and groundwater in order to visually display underground information within a limited depth range. However, these methods have disadvantages. In particular, geological surveys can only describe geological phenomena on the surface, while the cost of drilling and trenching may be high, along with the inefficiency of exploration. In this research, the integrated geophysical exploration method was used to analyze the hidden joints, cracks, and other discontinuities and geophysical features of the discontinuities. The results were verified by trench exploration, and large amounts of underground information were obtained, especially the spread and distribution of discontinuities in landslide areas. Such information can be effectively used for carrying out and providing meaningful experiences and lessons in future comprehensive geophysical processing and interpretations related to the prediction and evaluation of landslides. [ABSTRACT FROM AUTHOR]

Regional empirical-statistical thresholds indicating the precipitation conditions initiating landslides are of crucial importance for landslide early warning system development. The objectives of this research were to use landslide and precipitation data in an empirical-statistical approach to (1) identify precipitation-related variables with the highest explanatory power for landslide occurrence and (2) define both trigger and trigger-cause based thresholds for landslides in Rwanda, Central-East Africa. Receiver operating characteristics (ROC) and area under the curve (AUC) metrics were used to test the suitability of a suite of precipitation-related explanatory variables. A Bayesian probabilistic approach, maximum true skill statistics and the minimum radial distance were used to determine the most informative threshold levels above which landslide are high likely to occur. The results indicated that the event precipitation volumes E, cumulative 1-day rainfall (RD1) that coincide with the day of landslide occurrence and 10-day antecedent precipitation are variables with the highest discriminatory power to distinguish landslide from no landslide conditions. The highest landslide prediction capability in terms of true positive alarms was obtained from single rainfall variables based on trigger-based thresholds. However, that predictive capability was constrained by the high rate of false positive alarms and thus the elevated probability to neglect the contribution of additional causal factors that lead to the occurrence of landslides and which can partly be accounted for by the antecedent precipitation indices. Further combination of different variables into trigger-cause pairs and the use of suitable thresholds in bilinear format improved the prediction capacity of the real trigger-based thresholds. [ABSTRACT FROM AUTHOR]

Landslides are dangerous and destructive geomorphic processes that cause annual damage to human infrastructure or even loss of life. As recovery is very costly, knowledge of past landslide activities, a detailed analysis of triggers and prediction of future landslide development are important. Dendrogeomorphic (tree-ring-based) dating is the best solution of chronological data obtaining in forested areas, where trees annually produce increment rings. A moving landslide mass affects trees that grow on its surface. Trees respond to this influence in different ways that are recordable and subsequently visible in tree ring series. Thus, tree rings represent an ideal natural archive of past landslide behaviour. Depending on the tree species, the length of a landslide chronology can be several centuries with sub-annual resolution. Although dendrogeomorphic approaches have some limitations, provided data are unique because they represent insight into the past without the need for long-term monitoring. Nevertheless, trees as landslide archives are suitable for medium-magnitude events because excessively small movements can be disregarded and catastrophic movements destroy trees. This review introduces details regarding tree-landslide interactions, provides a historical overview of applied methods, presents and assesses methodical approaches and summarises basic advantages and contributions to the knowledge of landslide chronology, spatial behaviour and triggers. Finally, limitations, the potential for subsequent research directions and calls for future fundamental studies in new world regions are presented. [ABSTRACT FROM AUTHOR]

Early warning systems have often been considered an effective risk mitigation tools for landslides. In 1977, the Geotechnical Engineering Office (GEO) of Hong Kong government established the world's first territorial-wide early warning system for landslide disaster. The Landslip Warning System (LWS) has then been continuously enhanced and upgraded in response to the enrichment of rainfall and landslide database, advancement in instrumentation techniques and change in public perception of landslide risk over the last 40 years. This article consolidates the extensive experience of Hong Kong in using the landslip early warning system (LEWS) as a landslide risk management tool. A comprehensive review on the development process of the rainfall-landslide prediction models is presented. The landslide prediction model evolved from a rainfall duration-intensity model (late 1970) to a simple rainfall threshold model (middle 1980 to late 1990), then to a rainfall-landslide density model (early 2000) and rainfall-landslide frequency model (middle 2000s onward). Through regular review and update of the prediction model taking into account the availability of more recent data and activities that alter landslide risks, the performance of prediction model could be enhanced. The number of rain gauges expanded from 20 to 92 to support the operation of different generations of LWS. The GEO is currently adopting internet of thing (IOT) and cloud computing technology to enhance the resilience of the LWS, especially during extreme weather condition. [ABSTRACT FROM AUTHOR]

Landslides are the most destructive geological hazard in the hilly regions. For systematic landslide mitigation and management, landslide evaluation and hazard zonation is required. Over the past few decades several techniques have been developed that can be used for landslide evaluation and zonation. These techniques can broadly be classified into qualitative and quantitative approaches. Qualitative approaches include geomorphological analysis and heuristic techniques whereas quantitative approaches include statistical, artificial intelligence and deterministic techniques. In quantitative techniques prediction for landslide susceptibility is based on the actual realistic data and interpretations. Further, the quantitative techniques also overcome the subjectivity of qualitative approaches. Each of these techniques may consider different causative factors and utilizes various means for factor evaluation and analysis. When compared, each of these techniques has its own advantage and disadvantage over other techniques. The selection of appropriate technique for landslide hazard evaluation and zonation is very crucial. The factors that need to be considered to adopt an appropriate approach are; investigation purpose, the extent of the area to be covered, the type of mapping units, the scale of map to be produced, type of data to be used, type of landslides, availability of resources, capability and skill set of an evaluator and the accessibility to the study area. The main aim of this article is to present a comprehensive review on various techniques and approaches available for landslide susceptibility and hazard zonation mapping. Further, attempt is also made to assess the effectiveness of these techniques in landslide hazard zonation studies. [ABSTRACT FROM AUTHOR]

Currently, mathematical models such as the regression model, grey prediction model, and neural networks are commonly used to predict landslide displacement. In this study, we develop multi-data-driven models for prediction of landslide displacement. Multi-kernel learning and weighted learning methods are integrated with the grey prediction theory and three models are constructed to predict landslide displacement: the kernel-based grey model, multi-kernel grey model, and weighted multi-kernel grey model. All three models are simple to calculate and easy to program. The models were applied to a case study of the Baishuihe landslide in the Three Gorges reservoir, China. The influence of rainfall, reservoir water level, landslide rate, and historical displacement on the current displacement of the landslide were investigated, and multi-data-driven prediction models with input parameters (rainfall, reservoir water level, landslide rate, and historical displacement) and an output parameter (current displacement) were developed. The results show that the weighted multi-kernel grey model has better prediction accuracy than the kernel-based grey model and the multi-kernel grey model. These findings can be widely applied in practical landslide prediction. [ABSTRACT FROM AUTHOR]

In recent years, increasing efforts have been made to predict the time, location, and magnitude of future landslides. This study explores the potential application of four state-of-the-art data mining models (logistic regression, random forest, support vector machine, and Naïve Bayes tree) for the spatially explicit prediction of landslide susceptibility across a landslide-prone landscape in the Zagros Mountains, Iran. Fifteen conditioning factors and 272 historical landslide events were used to develop a geospatial database for the study area. A two-step factor analysis procedure based on the multicollinearity analysis and the Gain Ratio technique was performed to measure the predictive utility of the factors and to quantify their contribution to landslide occurrences across the study region. Once the models were successfully trained and validated using several performance metrics (i.e., ROC-AUC, sensitivity, specificity, accuracy, RMSE, and Kappa), they were applied to the entire study region to generate distribution maps of landslide susceptibilities. Overall, the random forest model demonstrated the highest training performance (AUC = 0.971; accuracy = 99%; RMSE = 0.120) and ability to predict future landslides (AUC = 0.942; accuracy =87%; RMSE = 0.312), followed by the support vector machine, Naïve Bayes tree, and logistic regression models. The Wilcoxon signed-rank test further proved the superiority of the random forest model for mapping landslide susceptibility in the Zagros region. The insights obtained from this research could be useful for the spatially explicit assessment of landslide-prone landscapes and obtaining a better understanding of the capability of different predictive models. [ABSTRACT FROM AUTHOR]

In order to reach the designated final water level of 175 m, there were three impoundment stages in the Three Gorges Reservoir, with water levels of 135 m, 156 m and 175 m. Baishuihe landslide in the Reservoir was chosen to analyze its displacement characteristics and displacement variability at the different stages. Based on monitoring data, the landslide displacement was mainly influenced by rainfall and drawdown of the reservoir water level. However, the magnitude of the rise and drawdown of the water level after the reservoir water level reached 175 m did not accelerate landslide displacement. The prediction of landslide displacement for active landslides is very important for landslide risk management. The time series of cumulative displacement was divided into a trend term and a periodic term using the Hodrick-Prescott (HP) filter method. The polynomial model was used to predict the trend term. The extreme learning machine (ELM) and least squares support vector machine (LS-SVM) were chosen to predict the periodic term. In the prediction model for the periodic term, input variables based on the effects of rainfall and reservoir water level in landslide displacement were selected using grey relational analysis. Based on the results, the prediction precision of ELM is better than that of LS-SVM for predicting landslide displacement. The method for predicting landslide displacement could be applied by relevant authorities in making landslide emergency plans in the future. [ABSTRACT FROM AUTHOR]

Prediction on landslide displacement plays an important role in landslide early warning. Many models have been proposed for this purpose. However, the accuracy of the prediction results by these models often varies under different conditions. Rational evaluation and comprehensive consideration of these results still remain a scientific challenge. A new comprehensive combination model is proposed to predict the landslides displacement. The elementary displacement prediction is made by the support vector machine model, the exponential smoothing model, and the gray model (GM)(1,1). The results of the models are comprehensively evaluated by combining the results and introducing the accuracy matrix. The optimal weight in the evaluation work is obtained. A rational prediction result can be attained based on the so-called combination model. The proposed method has been tested by the application of Qinglong landslides in Guizhou Province, China. The comparison between the prediction results and in situ measurement shows that the prediction precision of the proposed model is satisfactory. The root-mean-square error (RMSE) of the combination model can be reduced to 1.4316 (monitoring site JCK2), 1.2623 (monitoring site JCK4), 2.3758 (monitoring site JCK6), 2.2704 (monitoring site JCK8), 1.4247 (monitoring site JCK11), and 0.9449 (monitoring site JCK12), which is much lower than the RMSE of the individual models. [ABSTRACT FROM AUTHOR]

On 16 April 2016, a Mw 7.0 earthquake occurred in Kumamoto city, Japan. The main shock induced two large landslides, namely the Aso Bridge landslide and the Aso Volcanological Laboratory landslide. Their topographical and geological conditions and motion features were investigated by using an unmanned aerial vehicle (UAV) and portable dynamic cone penetration tests (PPTs). The Aso Bridge landslide lies between elevations of 385 m and 725 m, with a total estimated volume of about 1,980,000 m3. The main body is composed of cohesive soil with lapilli and block. The Aso Volcanological Laboratory landslide lies on a slope between 483 m to 582 m, and the total volume is about 81,000 m3, with an average thickness of 4.5 m. The main body is composed of Kusasenrigahama volcanic pumice tephra beds. The material compositions and deposits of both landslides have low cohesion and easily induced shear failure for the two landslides. The sliding distance of the Aso Bridge landslide was long, the sliding direction almost unchanged from the scarp to the toe, and the sliding speed was rapid. The sliding distance of the Aso Volcanological Laboratory landslide, however, was short, the sliding direction changed from the N-direction at the scarp to the NW-direction at the toe, and the sliding speed was slow. [ABSTRACT FROM AUTHOR]

Some of the remarkable characteristics of natural landslides, such as surprisingly long travel distances and high velocities, have been attributed to the mechanisms of frictional heating and thermal pressurization. In this work, this mechanism is combined with a depth-averaged model to simulate the long runout of landslides in the condition of deformation. Some important factors that influence frictional heating and thermal pressurization within the shear zone are further considered, including velocity profile and pressurization coefficient. In order to solve the coupled equations, a combined computational method based on the finite volume method and quadratic upwind interpolation for convective kinematics scheme is proposed. Several numerical tests are performed to demonstrate the feasibility of the computational scheme, the influence of thermal pressurization on landslide run-out, and the potential of the model to simulate an actual landslide. [ABSTRACT FROM AUTHOR]

The main objective of this study is to produce a landslide susceptibility map for the Lao Cai area (Vietnam) using a new hybrid intelligent method based on least squares support vector machines (LSSVM) and artificial bee colony (ABC) optimization, namely LSSVM-BC. LSSVM and ABC are state-of-the-art soft computing techniques that have been rarely utilized in landslide susceptibility assessment. LSSVM is adopted to develop landslide prediction model whereas ABC was used to optimize the prediction model by identifying an appropriate set of the LSSVM hyper-parameters. To establish the hybrid intelligent method, a GIS database with ten landslide-influencing factors and 340 landslide locations that occurred mainly during the last 20-years was constructed. These historical landslide locations were collected from the existing inventories that sourced from (i) five landslide projects carried out in this study areas before and (ii) interpretations of SPOT satellite images with resolution of 2.5 m. The study area was geographically split into two different parts, with landslides located in the first part was used for building models whereas the other landslides in the second part was used for the model validation. Performance of the LSSVM-BC model was assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Result shows that the prediction power of the model is good with the area under the curve (AUC) = 0.900. Experiments have pointed out the prediction power of the LSSVM-BC is better than that obtained from the popular support vector machines. Therefore, the proposed model is a promising tool for spatial prediction of landslides at the study area. The landslide susceptibility map is useful for landuse planning for the Lao Cai area. [ABSTRACT FROM AUTHOR]

This work focuses on the development of a combined statistical-mechanical approach to predict changes in landslide displacement rates from observed changes in rainfall amounts. The forecasting tool FLAME (Forecasting Landslides Accelerations induced by Meteorological Events) associates (1) a statistical impulse response (IR) model to simulate the changes in landslide rates by computing a transfer function between the input signal (e.g. rainfall) and the output signal (e.g. displacement) and (2) a simple 1D mechanical (MA) model (e.g. viscoplastic rheology) to take into account changes in pore water pressure. The models have been applied to forecast the displacement rates at the Super-Sauze landslide (South East France). The performance of different combinations of models (IR model alone, MA model alone and a combination of the IR and MA models) is evaluated against observed changes in pore water pressures and displacement rates at the study site. Results indicate that the three models are able to reproduce the displacement pattern in the general kinematic regime (succession of acceleration and deceleration phases); conversely, extreme kinematic regimes such as fluidization of part of the landslide mass are not being reproduced. The approach constitutes however a robust tool to predict changes in displacement rates from rainfall or groundwater time series. [ABSTRACT FROM AUTHOR]

Landslides are a common type of mass movement that occur in large numbers and have significant effects on topography. In Turkey, landslides rank second after earthquakes among natural disasters that cause widespread loss of life and property. This study investigates the monitoring of the Taşkent Landslide (Konya, Turkey), which has considerably affected the local topography. The landslide in Taşkent had an adverse effect on transportation to nearby towns and villages. Initial studies were conducted using the global navigation satellite system (GNSS) measurement scale; these studies are currently on-going. Since 2011, studies using terrestrial laser scanning (TLS), which is one of the latest ground-positioning technologies, have also been launched. This article presents the results of the TLS and GNSS approaches used to monitor, characterise and model an active landslide. It includes the comparison and analysis of a point cloud, three-dimensional model, profile and change in volume mass acquired in different time periods in one landslide area. The point cloud data, obtained from two different time periods using the TLS technique, and supported by the results of GNSS analysis, were analysed via different methods. The TLS revealed that up to 4 m of material was displaced at the site of the landslide, and it revealed detailed information about how the landslide occurred. The volume of the mass that moved as a result of the landslide was determined, and movements that occurred in the geological structure were monitored. These studies showed that the TLS point cloud method can be effectively used in the monitoring of landslides that occur in areas that are large, difficult to reach, rugged or mountainous. [ABSTRACT FROM AUTHOR]

The rheology of marine sediment-laden flows in a submarine environment is examined. The viscosity and yield stress of marine sediments are key parameters controlling landslide mobility: the former is related to the peak velocity and the latter is related to the final deposition of debris flow. One of the simplest rheological models is the ideal plastic Bingham model, which is often applied to diverse materials. For marine sediments, the flow behavior can be approximated by modified Bingham models, such as the bi-viscosity and Papanastasiou models. The flow of viscoplastic materials can then be described by unyielded (local shear stress τ is lower than a yield stress τ), yield surface ( τ = τ), and post-yield ( τ > τ) characteristics. It is concluded that modified Bingham models with a proper choice of flow parameters can be used to predict the unyielded, yielding and yielded zones for marine sediments. It is also demonstrated that in some cases the yield surface can be effectively obtained using the relationship between viscosity and shear rate. [ABSTRACT FROM AUTHOR]