Prediction Of Shallow Landslides Research Articles

Rainfall threshold for prediction of shallow landslides in the Garhwal Himalaya, India

Rainfall is a significant triggering factor for landslides, after tectonics and structurally vulnerable lithology— particularly in Himalayan Range. Globally, extensive efforts have been undertaken to determine the specific rainfall threshold conditions that lead to the initiation of a slide on a regional scale. Rainfall-induced landslides disrupt life and cause extensive damage to properties in the Himalayan region. This study has a two-fold objective; to determine the relationship between the occurrence of landslides and a significant triggering factor, namely rainfall, based on intensity-duration (I-D) and antecedent rainfall methods, and also to determine the best fit distribution for rainfall data based on goodness of fit tests within the four western-most districts of the Garhwal Himalaya, in Dehradun, Rudraprayag, Tehri Garhwal, and Uttarkashi. The rainfall patterns of these four districts conform to the log-logistic (3P) distribution, and the rainfall threshold has been fitted over a power-law equation with the lower boundary demarcated by a quantile regression that is presented as a threshold with an established relationship of y(I)=1.38D−0.126 (I=rainfall intensity, D=duration). The results suggest that a rainfall intensity of 0.45-0.50 mm per hour over short durations (48 h) has the potential to trigger landslides in this region. Antecedent rainfall of around 80 mm in the 15 days prior to a landslide event significantly raises the landslide risk. Further, lithologies like mud and sandstones are highly susceptible to landslides and can be triggered by rainfall of 10–20 mm occurring, consecutively, over a 5-day period.

Establishing a shallow-landslide prediction method by using machine-learning techniques based on the physics-based calculation of soil slope stability

Establishing a shallow-landslide prediction method by using machine-learning techniques based on the physics-based calculation of soil slope stability

Stability Prediction of Rainfall-Induced Shallow Landslides: A Case Study of Mountainous Area in China

Heavy rainfall induces shallow landslides in the mountainous areas of China. There is a need for regional slope stability prediction to reduce the damage to infrastructure, residents, and the economy. This study attempts to demarcate areas prone to rainfall-induced shallow landslides using the transient rainfall infiltration and grid-based slope stability (TRIGRS) model under different rainfall conditions. After inputting the engineering geological and geotechnical characteristic data of the area in China, the slope stability was simulated and verified by a deformation monitoring landslide. The slope stability gradually declined under the influence of precipitation from 5–8 July 2021. Slope stability gradually decreased under the predicted rainfall intensity of 60 mm/d for 6 days. The percentage of the slope area with a factor of safety (FS) less than 1.0 increased from 0.00% (1 d) to 3.18% (6 d). The study results could be used for hazards mitigation in this region.

Temporal prediction of shallow landslides exploiting soil saturation degree derived by ERA5-Land products

ERA5-Land service has been released recently as an integral and operational component of Copernicus Climate Change Service. Within its set of climatological and atmospheric parameters, it provides soil moisture estimates at different soil depths, represeting an important tool for retrieving saturation degree for predicting natural hazards as shallow landslides. This paper represents an innovative attempt aiming to exploit the use of saturation degree derived from ERA5-Land soil moisture products in a data-driven model to predict the daily probability of occurence of shallow landslides. The study was carried out by investigating a multi-temporal inventory of shallow landslides occurred in Oltrepò Pavese (northern Italy). The achieved results follow: (i) ERA5-Land-derived saturation degree reconstructs well field trends measured in the study area until 1 m from ground; (ii) in agreement with the typical sliding surfaces depth, saturation degree values obtained since ERA5-Land 28–100 cm layer represent a significant predictor for the estimation of temporal probability of occurrence of shallow landslides, able especially to reduce overestimation of triggering events; (iii) saturation degree estimated by ERA5-Land 28–100 cm layer allows to detect soil hydrological conditions leading to triggering in the study area, represented by saturation degree in this layer close to complete saturation. Even if other works of research are required in different geological and geomorphological settings, this study demonstrates that ERA5-Land-derived saturation degree could be implemented to identify triggering conditions and to develop prediction methods of shallow landslides, thanks also to its free availability and constantly updating with a delay of 5 days.

Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models

Abstract. The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at a regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term “triggering” precipitation, medium-term “preparatory” precipitation, seasonal effects, and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false-alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design, and model transparency are crucial for landslide prediction using advanced data-driven techniques.

An open-source and QGIS-integrated physically based model for Spatial Prediction of Rainfall-Induced Shallow Landslides (SPRIn-SL)

This paper presents a new open-source and physically based model for Spatial Prediction of Rainfall-Induced Shallow Landslides (SPRIn-SL) through the Quantum GIS (QGIS) software. SPRIn-SL consists of a set of shell scripts developed using the Python language that can be directly run from the QGIS processing toolbox through a user-friendly graphical interface. The tool implements the infinite slope method by incorporating the TOPOG and the Green-Ampt models to consider groundwater flow and transient rainfall infiltration, respectively. Furthermore, DEM pre-processing procedures to extract reliable terrain morphometric features, a new statistical method for modelling soil depth and a procedure for predictive accuracy evaluation, were implemented. By using a 1-m resolution DEM, the developed model was tested in a small coastal catchment of Cinque Terre (Liguria, Italy), providing accurate outcomes, and proving to be an easy-to-use tool for landslide susceptibility zoning which can have useful implications on the risk reduction.

Simple Method for Shallow Landslide Prediction Based on Wide-Area Terrain Analysis Incorporated with Surface and Subsurface Flows

Simple Method for Shallow Landslide Prediction Based on Wide-Area Terrain Analysis Incorporated with Surface and Subsurface Flows

A Simplified Analytical Method to Predict Shallow Landslides Induced by Rainfall in Unsaturated Soils

In order to assess slope stability owing to rainfall, the availability of an effective and simple-to-use methodology, relating directly rain to eventual landslide triggering, is undoubtedly useful. To this purpose, a simplified method aimed to the prediction of rainfall-induced shallow landslides in unsaturated soils is proposed in the present study. This method takes advantage of some closed-form solutions to evaluate the change in pore pressure due to infiltration of a rainfall characterized by a given intensity and duration, and the simple scheme of infinite slope to calculate a threshold for the change in pore pressure when the slope is under limit conditions. Particularly, using the present approach, a critical curve can be defined to establish the rainfall events that can trigger a failure process at a given depth, where suction before rainfall is known. The proposed method appears promising from an engineering viewpoint, since it is simple to use and requires few parameters as input data. In addition, these parameters can be determined from conventional geotechnical tests. The validity of the proposed approach is corroborated by some comparisons with the results of well-documented case studies.

A new approach based on integration of random subspace and C4.5 decision tree learning method for spatial prediction of shallow landslides

The research approaches a new machine learning ensemble which is a hybridization of Random subspace (RS) and C4.5, named RandSub-DT, for improving the performance of the landslide susceptibility model. This is based on the GIS database, including 170 landslide polygons and ten predisposing landslide factors, i.e., slope, aspect, curvature, TWI, land use, distance to road, distance to the river, soil type, distance to fault, and lithology. We carried out this study in the Halong and Cam Pha City areas which are important economic centers in the Quang Ninh province, Vietnam, where landslides seriously influence the daily life of the citizen causing economic damage. We then used a GIS database to construct and validate the proposed RandSub-DT model. The model performance was assessed using a confusion matrix and a set of statistical measures. The result showed that the RandSub-DT model with the classification accuracy of 90.34% in the training dataset and the prediction capability of 77.48% had a high performance for landslide prediction. This research proved that an ensemble of the C4.5 and RS provided a highly accurate estimate of landslide susceptibility in the research area.

A comparison study on the quantitative statistical methods for spatial prediction of shallow landslides (case study: Yozidar-Degaga Route in Kurdistan Province, Iran)

A comparison study on the quantitative statistical methods for spatial prediction of shallow landslides (case study: Yozidar-Degaga Route in Kurdistan Province, Iran)

Performance and topographic preferences of dynamic and steady models for shallow landslide prediction in a small catchment

Performance and topographic preferences of dynamic and steady models for shallow landslide prediction in a small catchment

Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow

Slopes covered with unsaturated shallow pyroclastic deposits, lying upon fractured limestone bedrock, are widespread in the mountains around Naples (southern Italy). Rainfall infiltration, reducing soil suction, eventually triggers shallow landslides. While drastic reduction of suction is unanimously recognized as the triggering mechanism, there is still debate about the hydrological processes controlling slope drainage and causing the establishment of landslide predisposing conditions. Field observations at the slope of Cervinara suggested that temporary storage of water in a perched aquifer, in the upper part of the fractured bedrock, may affect the leakage through the soil-bedrock interface. Hence, a physically based model, coupling flows in the unsaturated soil cover and in the perched aquifer, has been applied to three large rainfall events which occurred in December 1999 (when a landslide was triggered), January 2009, and November 2012. The results highlight that the different responses of soil and aquifer to precipitations, related not only to rainfall event characteristics (i.e., duration and mean intensity) but also to the initial conditions of the slope, determined by antecedent precipitations, can play a prominent role in the triggering of landslides. In fact, further simulations with synthetic rainfall events and different initial conditions provide a possible interpretation of the triggering of the landslide of December 1999, indicating that a soil profile with dry conditions at the base and a low level in the perched aquifer, typical of late autumn, can impede the drainage of infiltrating water through the soil-bedrock interface, thus favoring the build-up of pore pressure within the soil cover.

Determination of Empirical Rainfall Thresholds for Shallow Landslides in Slovenia Using an Automatic Tool

Rainfall-triggered shallow landslides represent a major threat to people and infrastructure worldwide. Predicting the possibility of a landslide occurrence accurately means understanding the trigger mechanisms adequately. Rainfall is the main cause of slope failures in Slovenia, and rainfall thresholds are among the most-used tools to predict the possible occurrence of rainfall-triggered landslides. The recent validation of the prototype landslide early system in Slovenia highlighted the need to define new reliable rainfall thresholds. In this study, several empirical thresholds are determined using an automatic tool. The thresholds are represented by a power law curve that links the cumulated event rainfall (E, in mm) with the duration of the rainfall event (D, in h). By eliminating all subjective criteria thanks to the automated calculation, thresholds at diverse non-exceedance probabilities are defined and validated, and the uncertainties associated with their parameters are estimated. Additional thresholds are also calculated for two different environmental classifications. The first classification is based on mean annual rainfall (MAR) with the national territory divided into three classes. The area with the highest MAR has the highest thresholds, which indicates a likely adaptation of the landscape to higher amounts of rainfall. The second classification is based on four lithological units. Two-thirds of the considered landslides occur in the unit of any type of clastic sedimentary rocks, which proves an influence of the lithology on the occurrence of shallow landslides. Sedimentary rocks that are prone to weathering have the lowest thresholds, while magmatic and metamorphic rocks have the highest thresholds. Thresholds obtained for both classifications are far less reliable due to the low number of empirical points and can only be used as indicators of rainfall conditions for each of the classes. Finally, the new national thresholds for Slovenia are also compared with other regional, national, and global thresholds. The thresholds can be used to define probabilistic schemes aiming at the operative prediction of rainfall-induced shallow landslides in Slovenia, in the framework of the Slovenian prototype early warning system.

Modelling of shallow landslides with machine learning algorithms

This paper introduces three machine learning (ML) algorithms, the ‘ensemble’ Random Forest (RF), the ‘ensemble’ Gradient Boosted Regression Tree (GBRT) and the MultiLayer Perceptron neural network (MLP) and applies them to the spatial modelling of shallow landslides near Kvam in Norway. In the development of the ML models, a total of 11 significant landslide controlling factors were selected. The controlling factors relate to the geomorphology, geology, geo-environment and anthropogenic effects: slope angle, aspect, plan curvature, profile curvature, flow accumulation, flow direction, distance to rivers, water content, saturation, rainfall and distance to roads. It is observed that slope angle was the most significant controlling factor in the ML analyses. The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic (ROC) analysis. The results show that the ‘ensemble’ GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides, with a 95% probability of landslide detection and 87% prediction efficiency.

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

Succession of Woody Vegetation and Prediction of Shallow Landslides on Steep Slopes in an Abandoned Forest after Plantation Clear Cutting, Kagoshima Prefecture

Succession of Woody Vegetation and Prediction of Shallow Landslides on Steep Slopes in an Abandoned Forest after Plantation Clear Cutting, Kagoshima Prefecture

Rainfall thresholds for shallow landslides based on physical models: application in a sub-basin of the Valle de Aburrá (Colombia)•

An analysis of the rainfall conditions and the influence of the infiltration processes on the slope stability are important in tropical mountain terrains due to the wide occurrence of rainfall-induced landslides. Distributed physical-based models can be coupled with techniques that relate the factor of safety with rainfall thresholds associated with the occurrence of this phenomenon. This study presents a methodology that determines the thresholds of rainfall intensity and duration, for the prediction of shallow landslides, using a distributed physical model called TRIGRS. In the analysis, it is used a percentage of the critical area of failure defined for a study area. The conditions of mean intensity and duration of rain that induce this failing area to be reached are adjusted to a potential equation, within certain ranges of duration and intensity. The model is used in a sub-basin of the Valle de Aburrá (Colombia) and the results are compared with rainfall thresholds defined with another distributed physical model. The obtained results reaffirm that the conditions of rainfall intensity and duration that trigger landslides in a region exhibit scaling properties determined by relationships of potential equations, like those obtained by other researchers in different places of the world.

Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides.

Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides

In some rainfall-triggered landslides, intensity-duration thresholds can have limited prediction ability; therefore, investigation of alternative approaches that can be used for temporal prediction of rainfall-induced landslides is needed. This paper presents a methodology for predicting rainfall-induced shallow landslides based on a lumped conceptual hydrological model. The production storage level during the rainfall event and the rainfall sum during the event are used for landslide prediction. Based on these two hydro-meteorological variables a threshold is defined that could be used for rainfall-induced landslides prediction as part of an early warning system. The presented methodology is tested using the meso-scale Selška Sora River catchment case study in western Slovenia where 20 active landslides from the Slovenian National Landslide Database are used to calibrate and evaluate the methodology performance. The results are compared to three different (i.e. local, regional, and global) intensity-duration thresholds. The results of the presented approach are superior in terms of several goodness-of-fit criteria compared to tested local and global ID thresholds. Because only daily rainfall, evapotranspiration, and discharge data are needed to calibrate the selected hydrological model and only daily rainfall and evapotranspiration to run the model, the presented approach could also be useful for data-scarce areas where detailed physically based landslide prediction models that require many data cannot be constructed. Moreover, we have also derived the probabilistic version of the proposed threshold for triggering of shallow landslides using copula functions.

Volcaniclastic debris avalanche on Motomachi area of Izu-Oshima, Japan, triggered by severe storm: Phenomenon and mechanisms

Heavy rainfall and strong wind associated with Typhoon Wipha hit Izu-Oshima Island, Japan, and triggered catastrophic landslides on the western slope of Oshima Volcano on 16 October 2013. These landslides were shallow but large in scale, and the displaced landslide materials were characterized by rapid and long runout movement. A massive landslide resulted in 36 people dead and 3 missing, with 46 buildings being completely destroyed in the downstream area of Motomachi district. To understand the initiation and movement of these shallow landslides, we surveyed the landslide area and found that in addition to heavy rainfall, strong wind may also have played a key role in increasing the instability of the slopes. We took samples from the source areas and examined their static and dynamic shear behaviors under naturally drained or undrained conditions. Test results showed that high pore-water pressure can be built up and maintained within the displaced landslide material and increase landslide mobility, even though the thickness of displaced landslide materials is 0.7–1.2 m. This may then give a rational explanation on the fluidization process of the displaced landslide materials. We also examined the windthrow features of trees subjected to different wind speed through monitoring the ground motion by seismometer and portable anemometer, and then performed cyclic shear tests on the materials laying above the sliding surface. The test results suggest that the windthrow may have promoted the buildup of excess pore-water pressure within the saturated soil layer and thus favoured the occurrence of slope failure. Therefore, it is suggested that in the prediction of shallow landslides on forested slopes subjected to heavy storm, both the effects of rainfall and strong wind should be taken into account.