Landslide Prediction Research Articles

Regional-scale InSAR investigation and landslide early warning thresholds in Umbria, Italy

In this work, we use conventional two-pass differential satellite interferometry for the investigation of active landslides at regional scale in the Umbria Region of Italy. First, we use InSAR for the detection of active slope movements during the study period (October 2019 to January 2021). Then, we characterize the displacement rates and their variation through time for selected active slopes, by analysing 6-days interferograms. The evolutionary trends of the phenomena are compared to the pertaining climate and soil parameters that are used by the regional landslide early warning system (LEWS) to predict the possibility of landslide occurrence over the territory. We aim to explore the potential of differential interferometry to detect and monitor active landslides and, through that, to validate a geographical LEWS:Through the analysis of stacked interferograms, we identified 256 InSAR deformation signals (IDS) corresponding to active slope movements. Their distribution is compared to the geological map and the landslide inventory, illustrating how active landslides are favoured by weak lithologies and pre-existing slope instability. Also, the relative orientation of the satellite's line of sight with respect to the slope influences the results indicating a bias in the completeness of remotely sensed data. However, in the context of land management and civil protection, the results constitute a valuable information dataset for plans managed by local authorities.The analysis, at the interferogram scale (6-days), of 13 clearly recognizable IDSs illustrates the dynamics of the slopes through the variation of the displacement rates. Due to decorrelation, only about half of the interferograms can be used for this purpose, and interpretation is required. The comparison to the alert thresholds of the Umbria region shows the remarkable relationship between InSAR-derived landslide activity and the parameters used for landslide prediction (i.e., 48 h rainfall and soil moisture conditions). The maximum 48 h cumulative rainfall and the soil saturation index prove effective to predict landslide reactivations and accelerations. We provide uncertainty measures of the landslide prediction through binary classification metrics. Such results are only preliminary, given the limited number of landslides and timespan of our dataset. However, it represents one of the few attempts to validate the forecast of a regional LEWS quantitatively.

The use of global versus region-specific data for the prediction of co-seismic landslides

This study compares the impact of global and region-specific variables on the prediction of co-seismic landslides, investigating whether the incorporation of higher-resolution data into a global framework can improve the prediction outcome for regional landslide assessments. Based on a statistical landslide model, which was developed through logistic regression involving various global landslide inventories, the likelihood of a landslide occurring during an earthquake is estimated for a regional event. Using the recently updated landslide inventory of the 2016 MW7.8 Kaikōura earthquake (centroid of landslide source area), the prediction performance of the model is evaluated by individually replacing the global variables for slope, compound topographic index, lithology and land cover with New Zealand specific datasets that offer higher resolution and/or more accurate information. Statistical metrics are used to compare the changes in the prediction performance of each modification, while probability maps and binary prediction maps are evaluated to identify spatial differences. The results suggest that calculating the slope variable from a higher-resolution DEM (e.g., 25 m) improves the model's prediction performance without changing the model equation or replacing the other global variables. Other region-specific variables such as land cover show stronger correlation but require an adjustment of the model coefficients (e.g., regression analysis). As the region-specific datasets increase the landslide estimates, a sensible interpretation of the model results is important, including the identification of a probability threshold which allows for a binary prediction (landslide occurs versus landslide does not occur). Future research should investigate the role of global and region-specific datasets for other earthquake events and consider incorporating other variables (e.g., local slope relief). The findings demonstrate the limitations of global variables for regional landslide predictions and illustrate the impact as well as implications of region-specific datasets on the model outcome, providing a better understanding on how incorporating region-specific datasets into a global approach can improve the landslide prediction in areas where regional models are not available or where inventory data is not sufficient to develop a robust model.

Landslide Prediction in Mountainous Terrain Using Remote Sensing and GIS: A Case Study of Al-Hada Road, Makkah Province, Saudi Arabia

In Saudi Arabia’s mountainous regions, debris flow is a natural hazard that poses a threat to infrastructure and human lives. To assess the potential zones of landslide in the Al-Hada Road area, a study was conducted using Geographic Information System (GIS) analysis and remote sensing (RS) data. The study took into account various factors that could affect landslides, such as drainage density, elevation, slope, precipitation, land use, geology, soil, and aspect. The study also included a field trip to identify 11 previous landslide events that were influenced by high-density drainage and slope. The study utilized weighted overlay analysis in a GIS environment to create a map indicating the potential landslide zones in the area. According to the analysis, low-risk areas cover 35,354,062.5 square meters, medium-risk areas cover 60,646,250 square meters, and high-risk zones cover an area of 8,633,281 square meters. This result was verified using the locations of previous landslide events. The study’s findings can help planners and decision-makers identify and prioritize areas for mitigation and prevention measures. Additionally, the study contributes to understanding landslide hazards in arid and semi-arid regions.

Deformation Monitoring and Dynamic Analysis of Long-Runout Bedding Landslide Based on InSAR and Particle Flow Code

Long-runout landslides occur frequently in the sandstone and mudstone mountainous areas in southwestern China under heavy rainfall conditions. This has been a key issue in the field of disaster prevention and reduction. Considering the Niuerwan landslide in Wulong, Chongqing, on 13 July 2020, as an example, we employed technical methodologies, including unmanned aerial vehicle (UAV) images, field investigation, geological condition analysis (including geomorphology and topography, stratigraphic structure and formation lithology, etc.), interferometric synthetic aperture radar (InSAR) monitoring and Particle Flow Code 3D (PFC3D) simulations to study failure mechanism and a long-runout motion model of flow-like landslides induced by the heavy rainfall. The results showed that (1) the large differences between the upper and lower strata are the root cause of the instability and long-runout fluidization movement; (2) heavy rainfall is the key driving factor of slope instability and deep-seated landslides, leading to long-distance movement of the upper saturated residual soil; (3) the long-runout fluidization model of bedding landslides is mainly divided into the overall sliding in the lower layer, the mixing of coarse and fine particles in the middle layer, and saturation fluidization in the upper layer; and (4) the long-runout fluidization process of bedding landslides is composed of three stages: overall instability, mixed acceleration, and fluidization accumulation. In view of these findings, in the risk evaluation and prediction of long-runout fluidization landslides in sandstone and mudstone mountainous areas, this particular disaster model can be used to provide quantitative references for disaster prevention and mitigation.

Landslide Susceptibility Mapping and Interpretation in the Upper Minjiang River Basin

To enable the accurate assessment of landslide susceptibility in the upper reaches of the Minjiang River Basin, this research intends to spatially compare landslide susceptibility maps obtained from unclassified landslides directly and the spatial superposition of different types of landslide susceptibility map, and explore interpretability using cartographic principles of the two methods of map-making. This research using the catalogs of rainfall and seismic landslides selected nine background factors those affect the occurrence of landslides through correlation analysis finally, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, and distance to faults, to assess rainfall and seismic landslide susceptibility, respectively, by using a WOE-RF coupling model. Then, an evaluation of landslide susceptibility was conducted by merging rainfall and seismic landslides into a dataset that does not distinguish types of landslides; a comparison was also made between the landslide susceptibility maps obtained through the superposition of rainfall and seismic landslide susceptibility maps and unclassified landslides. Finally, confusion matrix and ROC curve were used to verify the accuracy of the model. It was found that the accuracy of the training set, testing set, and the entire data set based on the WOE-RF model for predicting rainfall landslides were 0.9248, 0.8317, and 0.9347, and the AUC area were 1, 0.949, and 0.955; the accuracy of the training set, testing set, and the entire data set for seismic landslides prediction were 0.9498, 0.9067, and 0.8329, and the AUC area were 1, 0.981, and 0.921; the accuracy of the training set, testing set, and the entire data set for unclassified landslides prediction were 0.9446, 0.9080, and 0.8352, and the AUC area were 0.9997, 0.9822, and 0.9207. Both of the confusion matrix and the ROC curve indicated that the accuracy of the coupling model is high. The southeast of the line from Mount Xuebaoding to Lixian County is a high landslide prone area, and through the maps, it was found that the extremely high susceptibility area of seismic landslides is located at a higher elevation than rainfall landslides by extracting the extremely high susceptibility zones of both. It was also found that the results of the two methods of evaluating landslide susceptibility were significantly different. As for a same background factor, the distribution of the areas occupied by the same landslide occurrence class was not the same according to the two methods, which indicates the necessity of conducting relevant research on distinguishing landslide types.

Landslide Susceptibility Assessment of a Railway Based on GIS Application

This study uses GIS-based maps to identify the potential for landslide movement on the railroad section between Prupuk Station and Purwokerto Station. The preparation was conducted by overlaying various maps obtained by online resource data, including rainfall, slope, geology, soil type, and land use, by using the DVMBG 2004 landslide prediction model. The distribution of landslide susceptibility levels shows that of the overall railway lines, 1.10% was very low, 17.29% was low, 63.16% was medium, 18.25% was high, and 0.21% was very high. The results of this study can be used as a reference for operators to provide information systems, landslide monitoring, and mitigation systems, especially for railroads in areas with high and very high vulnerability for landslides. Detailed conditions of landslide parameters and assessment of landslide occurrence on the location are needed to improve the accuracy of the models.

Rapid Emergency Response Assessment of Earthquake-Induced Landslides Driven by Fusion of InSAR Deformation Data and Newmark Physical Models

Strong earthquakes induce a large number of secondary disasters, such as landslides, which bring serious challenges to post-disaster emergency rescue, and the rapid and accurate assessment of earthquake-induced landslide disasters is crucial for post-earthquake emergency rescue. This research aims to propose an emergency assessment model that is suitable for post-earthquake landslides, specifically targeting the first 72 h after an earthquake for emergency rescue guidance. The model combines remote sensing technology and the Newmark physical mechanics assessment model to form the InSAR Data–Newmark Physical Fusion Driver Model (IDNPM), which comprehensively considers the dynamic deformation of the ground surface and geological features. To validate the predictive performance of the IDNPM, the model is applied to the 5 September 2022 Luding earthquake event and the 8 August 2017 Jiuzhaigou earthquake event. The landslide qualitative evaluation, confusion matrix and Receiver Operating Characteristic (ROC) curve are utilized for quantitative assessment. The results show that the IDNPM can effectively reduce the false negative and false positive errors in landslide prediction by utilizing the SAR deformation information, and to a certain extent, it accounts for the dependence of the Newmark model on the accuracy of empirical formulas and geotechnical parameters. For the Luding earthquake event, the IDNPM shows an accuracy improvement of 10.296% compared to the traditional Newmark model. For the Jiuzhaigou earthquake event, there is also an improvement of 3.152%, with a promising generalization performance. The simplicity and ease of operation in constructing the model are accompanied by high reliability and accuracy. The research findings provide essential references for the development of post-earthquake landslide emergency prediction models and offer robust data support for emergency rescue and recovery efforts in earthquake-stricken areas in the future.

On the estimation of landslide intensity, hazard and density via data-driven models

Maps that attempt to predict landslide occurrences have essentially stayed the same since 1972. In fact, most of the geo-scientific efforts have been dedicated to improve the landslide prediction ability with models that have largely increased their complexity but still have addressed the same binary classification task. In other words, even though the tools have certainly changed and improved in 50 years, the geomorphological community addressed and still mostly addresses landslide prediction via data-driven solutions by estimating whether a given slope is potentially stable or unstable. This concept corresponds to the landslide susceptibility, a paradigm that neglects how many landslides may trigger within a given slope, how large these landslides may be and what proportion of the given slope they may disrupt. The landslide intensity concept summarized how threatening a landslide or a population of landslide in a study area may be. Recently, landslide intensity has been spatially modeled as a function of how many landslides may occur per mapping unit, something, which has later been shown to closely correlate to the planimetric extent of landslides per mapping unit. In this work, we take this observation a step further, as we use the relation between landslide count and planimetric extent to generate maps that predict the aggregated size of landslides per slope, and the proportion of the slope they may affect. Our findings suggest that it may be time for the geoscientific community as a whole, to expand the research efforts beyond the use of susceptibility assessment, in favor of more informative analytical schemes. In fact, our results show that landslide susceptibility can be also reliably estimated (AUC of 0.92 and 0.91 for the goodness-of-fit and prediction skill, respectively) as part of a Log-Gaussian Cox Process model, from which the intensity expressed as count per unit (Pearson correlation coefficient of 0.91 and 0.90 for the goodness-of-fit and prediction skill, respectively) can also be derived and then converted into how large a landslide or several coalescing ones may become, once they trigger and propagate downhill. This chain of landslide intensity, hazard and density may lead to substantially improve decision-making processes related to landslide risk.

Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions

Climate change can exacerbate the occurrence of extreme precipitation events, thereby affecting both the frequency and intensity of rainfall-induced landslides. It is important to study the threat of rainfall-induced landslides under future climate conditions for the formulation of disaster prevention and mitigation policies. Due to the complexity of the climate system, there is great uncertainty in the climate variables simulated by a global climate model (GCM), which will be further propagated in landslide prediction. In this study, we investigate the spatial and temporal trends of future landslide hazards in China under climate change, using data from a multi-model ensemble of GCMs based on two scenarios, RCP4.5 and RCP8.5. The uncertainty characteristics are then estimated based on signal-to-noise ratios (SNRs) and the ratio of agreement in sign (RAS). The results show that the uncertainty of landslide prediction is mainly dominated by the GCM ensemble and the RCP scenario settings. Spatially, the uncertainty of landslide prediction is high in the western areas of China and low in the eastern areas of China. Temporally, the uncertainty of landslide prediction is evolving, with characteristics of high uncertainty in the near future and characteristics of low uncertainty in the distant future. The annual average SNRs in the 21st century are 0.44 and 0.50 in RCP4.5 and RCP8.5, respectively, and the RAS of landslide prediction in Southeastern China is only 50–60%. This indicates that more than half of the patterns show trends that are opposite to those of the ensemble, suggesting that their landslide change trends are not universally recognized in the pattern ensemble. Considering the uncertainty of climate change in landslide prediction can enable studies to provide a more comprehensive picture of the possible range of future landslide changes, effectively improving the reliability of landslide hazard prediction and disaster prevention.

The influence of seismic displacement models on spatial prediction of regional earthquake-induced landslides

Regional landslide prediction is a critical step towards achieving community resilience against landslides during earthquakes. We present a predictive methodology for regional earthquake-induced landslides that uses a pseudo-three-dimensional (pseudo-3D) procedure implemented on a digital elevation model (DEM). The methodology calculates a seismic displacement for every grid cell in the DEM as the basis for predicting the location and the size of landslide that is determined using a three-dimensional (3D) geometric projection based on topography. This “pseudo-3D” methodology for predicting landslides outperforms cell-based approaches. It has the advantage of deriving landslide number and size characteristics of individual landslides. We quantify the influence of seven commonly used seismic displacement models: four rigid, two decoupled flexible, and one fully coupled flexible. Seismic displacement model performance is evaluated by implementing a prediction for the 2015 Mw6.5 Lefkada earthquake event in Greece for which precise location and high-resolution landslide volume data are available for model validation and ground motion intensity measures are well constrained by nearby ground motion recordings. The 3D landslide inventory was created by topographically differencing pre-existing DEMs from post-event digital surface models derived from satellite and unmanned aerial vehicle (UAV) imagery. The inventory contains over 700 landslides, primarily shallow disrupted slides and rock slides, along with occasional occurrences of deep-seated, rotational failures. Qualitative comparisons between predicted and mapped landslides show that all seismic displacement models predict landslides that broadly overlap geospatially with most mapped landslides and the regions of high mapped landslide densities are predicted. A variety of metrics are used to quantify the prediction results: these include metrics such as landslide number and area density, as well as correctly predicted ratio, overlapped ratio, centroid distance, ground failure capture, and efficiency. The results show that the Rathje and Antonakos (kmax, k-velmax) model and the Saygili and Rathje (PGA, PGV) model correctly predict the most mapped landslides and obtain the highest prediction efficiency, but at the cost of the highest overprediction. The Bray and Macedo model achieves the best performance in predicting landslide number, area density, and location accuracy, but the correct prediction of the mapped landslides and the prediction efficiency are worse than other seismic displacement models. The results highlight the role of seismic displacement models in the prediction of regional earthquake-induced landslides and guide selection of the seismic displacement model depending on the objective of the prediction. Strength parameters also affect landslide prediction, and we demonstrate that all evaluation metrics vary widely due to variation of cohesion and friction angle, except for the correctly predicted ratio. More importantly, variation in prediction results due to the selection of seismic displacement models is found to be comparable to variation in results associated with strength parameter uncertainty. A critical slope parameter is proposed and defined as the flattest slope at which landslide triggering may occur for a given predictive model. As such, seismic displacement models that have lower critical slopes predict more landslides and differences in critical slopes for each seismic displacement model provide a parsimonious explanation for the different performances of the seismic displacement models in the regional earthquake-induced landslide prediction.

Acquisition of high-resolution topographic information in forest environments using integrated UAV-LiDAR system: System development and field demonstration

High-resolution topographic information of landslide-prone areas plays an important role in accurate prediction and characterization of potential landslides and mitigation of landslides-associated hazards. This study presents an advanced geomorphological surveying system that integrates the light detection and ranging (LiDAR) with an unmanned aerial vehicle (UAV), a multi-rotor aerial vehicle in specific, for prediction, monitoring and forensic analysis of landslides, and for maintenance of debris-flow barriers. The test-flight over a vegetated area demonstrates that the integrated UAV-LiDAR system can provide high-resolution, three-dimensional (3D) LiDAR point clouds below canopy and vegetation in forest environments, overcoming the limitation of aerial photogrammetry and terrestrial LiDAR platforms. An algorithm is suggested to delineate the topographic information from the acquired 3D LiDAR point clouds, and the accuracy and performance of the developed UAV-LiDAR system are examined through field demonstration. Finally, two field demonstrations are presented: the forensic analysis of the recent Gokseong landslide event, and the sediment deposition monitoring for debris-flow barrier maintenance in South Korea. The developed surveying system is expected to contribute to geomorphological field surveys in vegetated, forest environments, particularly in a site that is not easily accessible.

Reliability assessment of open-source multiscale landslide susceptibility maps and effects of their fusion

ABSTRACT Several landslide susceptibility (LS) maps at various scales of analysis have been performed with specific zoning purposes and techniques. Supervised machine learning algorithms (ML) have become one of the most diffused techniques for landslide prediction, whose reliability is firmly based on the quality of input data. Site-specific landslide inventories are often more accurate and complete than national or worldwide databases. For these reasons, detailed landslide inventory and predisposing variables must be collected to derive reliable LS products. However, high-quality data are often rare, and risk managers must consider lower-resolution available products with no more than informative purposes. In this work, we compared different ML models to select the most accurate for large-scale LS assessment within the Municipality of Rome. The ExtraTreesClassifier outperformed the others reaching an average F1-score of 0.896. Thereafter, we addressed the reliability of open-source LS maps at different scales of analysis (global to regional) by means of statistical and spatial analysis. The obtained results shed light on the difference in hazard zoning depending on the scale and mapping unit. An approach for low-resolution LS data fusion was attempted, assessing the importance of the adopted criteria, which increased the ability to detect occurred landslides while maintaining precision.

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.

Multi-Scale Response Analysis and Displacement Prediction of Landslides Using Deep Learning with JTFA: A Case Study in the Three Gorges Reservoir, China

Reservoir water and rainfall, leading to fluctuations groundwater levels, are the main triggering factors that induce landslides in the Three Gorges Reservoir area. This study investigates the response mechanism of landslide deformation under reservoir water and rainfall variations through long-time on-site observations. To address the non-stationary characteristics of the time-series records, joint time-frequency analysis (JTFA) is first introduced into our landslide prediction model. This model employs optimal variational mode decomposition (VMD) to obtain specific signal components with clear physical meaning, such as trend component and periodic components. Then, multi-scale response analysis between the displacement and external factors three wavelet methods was conducted. The analysis results show a 1 year primary cycle of the time series associated with the landslide evolution. The reservoir water level and rainfall show anti-phase fluctuations. The periodic displacement correlates significantly with rainfall, lagging by about two months. The reservoir water is anti-phase with the landslide displacement, preceding it by approximately three months (−51 ± 8° phase difference). For landslide displacement prediction, the gated recurrent units (GRU) neural network model is integrated into the deep learning forecasting architecture. The model takes into account the correlation and hysteresis effect of input variables. Through six experiments, we investigate the effect of data volume on model predictions to determine the optimal model. The results demonstrate that our proposed model ensures high performance in landslide prediction. Moreover, a comparison with six other intelligent algorithms shows the advantages of our model in terms of time-effectiveness and long-sequence forecasting.

An event-duration based rainfall threshold model for landslide prediction in Uttarkashi region, North-West Himalayas, India

An event-duration based rainfall threshold model for landslide prediction in Uttarkashi region, North-West Himalayas, India

Spatio-temporal prediction and mapping of landslides using MTInSAR-learning logistic regression and weight of evidence modeling in urban environments: a case study for the Büyükçekmece–Küçükçekmece region, İstanbul, Türkiye

Spatio-temporal prediction and mapping of landslides using MTInSAR-learning logistic regression and weight of evidence modeling in urban environments: a case study for the Büyükçekmece–Küçükçekmece region, İstanbul, Türkiye

Improving Landslides Prediction: Meteorological Data Preprocessing Based on Supervised and Unsupervised Learning

The hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant factors in landslides is intensive rainfall. The present work proposes the use of data generated by weather stations to predict landslides. We give special treatment to precipitation information as the most influential factor and whose data are accumulated in time windows (3, 5, 7, 10, 15, 20, and 30 days) looking for the persistence of meteorological conditions. To optimize the dataset composed of geological, geomorphological, and climatological data, a feature selection process is applied to the meteorological variables. We use filter-based feature ranking and Self-Organizing Map (SOM) with Clustering as supervised and unsupervised machine learning techniques, respectively. This contribution was successfully verified by experimenting with different classification models, improving the test accuracy of the prediction, and obtaining 99.29% for Multilayer Perceptron, 96.80% for Random Forest, and 88.79% for Support Vector Machine. To validate the proposal, a geographical area sensitive to this phenomenon was selected, which is monitored by several meteorological stations. Practical use is a valuable tool for risk management decision making, can help save lives and reduce economic losses.

An Analytical Study on Soil Water Index (SWI), Landslide Prediction and Other Related Factors Using XRAIN Data during the July 2018 Heavy Rain Disasters in Hiroshima, Japan

The rainfall-induced landslide disasters in July 2018 in Southwestern Japan yet again exemplified the severity of slope failure-related damage and the need for improvement of early warning systems. The Japanese Meteorological Agency (JMA) uses a method based on a threshold value of soil water index (SWI), a conceptual measurement that represents saturation of slope soil. The current SWI early warning system uses 60-min rainfall data on a 5-km2 mesh and does not take into consideration other landslide conditioning factors such as slope angle and geology. This study calculates SWI values during the July 2018 disasters in Kure City (Hiroshima Prefecture) using 1-min XRAIN rainfall data in a 250-m mesh to investigate the relationship between SWI and landslide occurrence. It was found that the SWI threshold of 124 mm used in the JMA early warning system for the area was surpassed in all cells. A new SWI threshold calculation method taking slope angle and geology into consideration and produced with machine learning is proposed, comprising power lines for different geological units at a two-dimensional graph where points located above the threshold line represent landslide risk. It is judged that this method would provide a more accurate early warning system for landslide disasters.

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.

Influence of Rainfall Pattern on Wetness Index for Infinite Slope Stability Analysis

Landslides are one of the riskiest disasters combining excessive rainfall and unstable slope that a wetness index can quantify. The wetness index generated by water infiltration considering the rainfall pattern such as cumulated rainfall, rainfall duration and rainfall intensity should be estimated for the slope stability analysis. Even though the infiltration capacity of soils has been largely focused to evaluate the slope stability, the temporal patterns of rainfall have commonly been ignored or assumed as a steady state for the prediction of the slope failure in the previous studies. Thus, this study focuses more on evaluating the influence of various rainfall patterns on the slope stability, and compares it with an actual landslide incident that occurred in 2011, in Korea. The factor of safety (FS) considering the time-dependent wetness index variation is used to determine the slope stability. For the various rainfall designs, the uniform rainfall distribution, Yen and Chow, Mononobe, alternating block and second quartile Huff models are adopted. Thereafter, the FS variations from five models are compared with an actual landslide incident in Seoul, Korea. Among the rainfall designs, the models that consider the abrupt rainfall intensity capture the landslide time with an FS < 1. Therefore, the appropriate adoption of a rainfall distribution model should be highlighted for landslide prediction.