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Empirical thresholds indicating the meteorological conditions leading to shallow landslide triggering are one of the most important components of landslide early warning systems (LEWS). Thresholds have been determined for many parts of the globe and present significant margins of improvement, especially for the high number of false alarms they produce. The use of soil moisture information to define hydro-meteorological thresholds is a potential way of improvement. Such information is becoming increasingly available from remote sensing and sensor networks, but to date, there is a lack of studies that quantify the possible improvement of the performance of LEWS. In this study, we investigate this issue by modelling the response of slopes to precipitations, introducing also the possible influence of uncertainty in soil moisture provided by either field sensors or remote sensing, and investigating various soil depths at which the information may be available. Results show that soil moisture information introduced within hydro-meteorological thresholds can significantly reduce the false alarm ratio of LEWS, while keeping at least unvaried the number of missed alarms. The degree of improvement is particularly significant in the case of soils with small water storage capacity. [ABSTRACT FROM AUTHOR]

Shallow landslides have posed significant threats to humans around the world. In order to reduce landslide disaster risk, the effectiveness of early warning systems and hazard zonation work needs to be improved. This research attempted to couple a landslide simulation model (LS-RAPID model) and a hydrological model (Rainfall-Runoff-Inundation (RRI) model) to exploit the advantages of each model for simulating and predicting landslide hazard (location and timing). The pilot area is a small catchment where a shallow landslide happened in July 2015 after 2 days of heavy rain. The landslide buried 3 houses and killed 8 people in Cao Thang Ward, Halong City, Vietnam. A soil sample was collected from the sliding surface and tested using an undrained ring-shear apparatus ICL-2 in the undrained condition. A thickness map of the potential sliding material was interpolated from the relationship between the depth of sandy soil layer and slope (based on 12 soil drill locations) and updated through field surveys in the study area. Different pore water pressure ratio scenarios were applied in the LS-RAPID model to simulate in 3D the initiation and motion of the rapid shallow landslide to create different hazard maps. The subsurface water level was monitored at two locations on the top of the shallow landslide. Based on the observed subsurface water and rainfall data, the RRI model was calibrated and then integrated with the LS-RAPID scenarios to generate Risk Index maps. The simulation results from the newly proposed coupled hydrological-geotechnical framework were compared with those from the observed landslide hazard and showed the reliability to predict the spatial and temporal occurrence of landslide hazard. This could be very useful for supporting decision-makers in rainfall-induced landslide hazard early warning and land use planning. [ABSTRACT FROM AUTHOR]

On 2 September 2018, an intense rainstorm swept Mengdong Town in Yunnan Province of China, inducing a serious landslide and debris flow disaster with 10 deaths and 11 missing. Image interpretation, field survey, and slope stability analysis were used to examine the characteristics and initiation mechanism of this hazard. A total of 1774 landslide scars were identified, the area of which occupied 8.26% of the study region. Due to the spatial inhomogeneity of precipitation, these scars mainly concentrated along valleys in the lower part of the study area. Besides, well-forested hillslopes were more prone to landslides, indicating the limited role of trees in stabilizing slopes in extreme rainfall events. The initiation of landslides is mainly attributed to the week cohesion of the saturated clayey sand beneath the root zone, where the tensile resistance of roots was absent, and the increase of positive pore water pressure. Additionally, 288 landslide scars were situated adjacent to roads, demonstrating that the road construction activity had intensified the landslide disaster. Owing to the relatively low mobility of landslides, a considerable portion of landslide debris deposited on the valley floor in smaller watersheds (< 6.03 hm2), while the remaining portion entered high-order channels. In these channels, where stream power was relatively large, the woody debris and soil carried by landslides were entrained by streamflow, and debris flows were formed. Moreover, the magnitude of debris flow was amplified by the vertical and lateral erosion of the stream channel. [ABSTRACT FROM AUTHOR]

In this work, we describe soil moisture profiles related to typical colluvial slopes that were involved in rainfall-induced shallow failures occurring in alpine and pre-alpine areas of the Friuli Venezia Giulia Region (NE Italy). The trend of the volumetric water content (θw) showed a general increase from the ground surface to the bottom soil layer, with two or three marked moisture peaks. The saturation degree (S) varied from 65–70% (topsoil horizon) to nearly saturated basal colluvium (S = 95–100%). Soil moisture data demonstrates that, for a very humid climate, colluvial covers are often close to the saturation condition for most of the year. The calculated suction profiles indicated that maximum values ranging from 40 to 55 kPa often occur in the slope surficial soil (depth < 0.2–0.5 m). This negative pore-water pressure greatly decreases after a heavy rainfall event because of the infiltration process. Complete saturation of colluvial cover in the alpine and pre-alpine regions generally requires rainfall exceeding 150–200 mm for a 24-h storm duration. This results in a recurrence time of Tr ≅ 5–10 years for critical rainfall episodes involving colluvial slopes in the Friuli Venezia Giulia Region. The case histories analyzed demonstrate the importance of performing a detailed lithostratigraphic analysis of the colluvial deposit in order to properly define the suction measurement points, which there should be more of than the three-point determinations usually reported in the literature (for example, z = 0.5, 1.0 and 1.5 m).

The main purpose of this study was to establish a data-driven approach and assess its potential for shallow landslide susceptibility mapping of the Shennongjia Forestry District, China. For the data processing, Fisher segmenting was used for the classification of topographical factors (gradient, relief amplitude, plan curvature, and normalized difference vegetation index) and the improved Otsu method was used to determine the buffer length thresholds of the locational factors (proximity to roads, rivers and faults). The computations show that these two objective methods help to avoid the uncertainty caused by the subjective judgments and the obtained thresholds are more consistent with actual conditions. Considering the different data types, the locational and topographical factors were evaluated by fuzzy comprehensive evaluation while the geological factors were evaluated by frequency index, and then the three separate evaluations were combined using radar chart analysis. The validation result shows that the susceptibility map based on a data-driven approach has a high accuracy of prediction and this approach will be salutary for risk mitigation and land-use management in mountainous regions.

Shallow landslides are destructive hazards and play an important role in landscape processes. The purpose of this paper is to evaluate the shallow landslide susceptibility and to investigate which predisposing factors control the spatial distribution of the collected instability phenomena. The GIS-based logistic regression model and jackknife test were respectively employed to achieve the scopes. The studied area falls in the Mesima basin, located in the southern Calabria (Italy). The research was based mainly on geomorphological study using both interpretation of Google Earth images and field surveys. Thus, 1511 shallow landslides were mapped and 18 predisposing factors (lithology, distance to faults, fault density, land use, soil texture, soil bulk density, soil erodibility, distance to streams, drainage density, elevation, slope gradient, slope aspect, local relief, plan curvature, profile curvature, TPI, TWI, and SPI) were recognized as influencing the shallow landslide susceptibility. The 70% of the collected shallow landslides were randomly divided into a training data set to build susceptibility model and the remaining 30% were used to validate the newly built model. The logistic regression model calculated the landslide probability of each pixel in the study area and produced the susceptibility map. Four classification methods were tested and compared between them, so the most reliable classification system was employed to the shallow landslide susceptibility map construction. In the susceptibility map, five classes were recognized as following: very low, low, moderate, high, and very high susceptibility. About 26.1% of the study area falls in high and very high susceptible classes and most of the landslides mapped (82.4%) occur in these classes. The accuracy of the predictive model was evaluated by using the ROC (receiver operating characteristics) curve approach, which showed an area under the curve (AUC) of 0.93, proving the excellent forecasting ability of the susceptibility model. The predisposing factors importance evaluation, using the jackknife test, revealed that slope gradient, TWI, soil texture and lithology were the most important factors; whereas, SPI, fault density and profile curvature have a least importance. According to these results, we conclude that the shallow landslide susceptibility map can be use as valuable tool both for land-use planning and for management and mitigation of the shallow landslide risk in the study area.

On 25 October 2011, the eastern Liguria (Vara Valley and Cinque Terre area) and northwestern Tuscany (Magra Valley) were affected by an extreme rainstorm (almost 600 mm/24 h) that caused floods, thousands of shallow landslides, 13 casualties and damage to villages and infrastructure. This study aims at analysing the main features of the 25 October 2011 shallow landslides occurred in the Pogliaschina Torrent basin (25 km2 wide, Vara Valley), in order to investigate the influence of specific predisposing factors (land use, geological and structural setting, plan and profile curvature, slope angle and aspect) on landslide occurrence. For this purpose, both a landslide inventory map and a geology map (scale 1:10,000) were prepared. In addition, a database including the main geological, geomorphological, structural and land use features of the landslide source areas was implemented. The relationship between landslide source areas and the main predisposing factors was evaluated through spatial and statistical analysis.

The accuracy and reliance increase and consequently reduction of uncertainty of spatial prediction maps of environmental hazards including landslides is one of the challenges facing with in such studies Therefore, the objective of this research is to introduce a hybrid model of data mining algorithm named Random Forest (RF)-Random Subspace (RF-RS) in order to enhance the accuracy of spatial prediction map of landslide-prone areas around the city of Bijar, Kurdistan province, Iran. Firstly, 19 affecting factors on shallow landslides in the study area including slope degree, slope aspect, elevation, curvature, profile curvature, plan curvature, solar radiation, stream power index (SPI), topographic wetness index (TWI), length-angle of slope (LS), land use, normalized difference vegetation index (NDVI), litho logy, distance to fault, fault density, rainfall, distance to stream, stream density and distance to road were identified. Then based on Information Gain Ratio (IGR), twelve factors among them were selected to be used in modeling. The elative importance of each factor was assessed by Random Forest (RF) model as well as Random Forest-Random Subspace (RF-RS) hybrid model. Kappa, Precision, Recall, and AUROC indices were used to evaluate the models not only for training dataset but also for testing dataset. Shallow landslide susceptibility maps of the study area were prepared using both models. The results showed that slope aspect in the RF model and slope degree in the RF-RS hybrid model is the most important affecting factor on landslide occurrence in the area. The model evaluation results indicated that both models are reasonable in application for shallow landslide susceptibility mapping. The findings also indicated that the percentage of area under the curve of ROC (AUROC) was 0.729 and 0.784 for training dataset by RF and RF-RS hybrid model, respectively, while these values were 0.717 and 0.771 for testing dataset. In conclusion, it can be claimed that the new technique (RF-RS hybrid model) is able to increase the accuracy of spatial prediction map of shallow landslides in the study area. This accurate map will help decision-makers to protect infrastructures of an area, to develop better land-use planning programs and to more effectively control sediments.

We analyzed characteristics manifested by shallow landslides using SHALSTAB and Li- DAR 1-m DEM for the intensive shallow landslide event that occurred in the Tsurugi-kawa drainage basin, Hofu City, Yamaguchi Prefecture in 2009. Based on the results, we created a risk assessment map to evaluate surface landslides in this basin. Consequently, we could quantitatively extract and visualize shallow landslide risk areas that had a tendency to have a highrisk probability based on their qualitative geomorphological characteristics. Using SHALSTAB and LiDAR 1-m DEM, we found that it was possible to evaluate relative risk by least assumptions (parameters) without conventional data that were typically utilized for studying shallow landslide processes such as the spatial distribution of soil depth and hydraulic conductivity. However, basic topographic parameters such as slope and basin area and hydraulic conductivity values that were measured in the laboratory considerably affect risk evaluation, but the process of obtaining these values at each shallow landslide area was not established. For example, hydraulic conductivity values measured in the laboratory proved unsuitable to be used in a drainage scale numerical model such as SHALSTAB. This result clearly indicates that spatially expanding the measurement results of various physical property tests in the laboratory is difficult. Conversely, this indicates the importance of research that is focused on using numerical models to estimate values such as watershed characteristics that cannot be actually measured. However, estimating soil depth and hydraulic conductivity, whose spatial distributions are difficult to grasp, may be possible by a numerical model such as SHALSTAB. [ABSTRACT FROM AUTHOR]

The paper shows the back-analysis results of a shallow landslide occurred on 10 November 2014. A very intense rainfall (that occurred after a prolonged rainy period) caused a number of landslides in the area of Leivi (Liguria, Northern Italy). The considered slope consists of shallow debris overlying the bedrock (Argille a Palombini). The analyses were carried out using the limit equilibrium method. Debris characterization was carried out by means of direct shear tests. For such a purpose, direct shear tests on large specimens were carried out using a large shear box (300 × 300 × 100 mm). The specimens were reconstituted in the laboratory using the soil collected just above the sliding surface. The direct shear tests were performed to determine the effective strength parameters according to different density and water content. Direct shear test results showed that the friction angle values decrease and the cohesion increases, as water content increases. Furthermore, previous geological and geomorphological studies of the area affected by landslides were used to define the study model. [ABSTRACT FROM AUTHOR]

Rainfall-induced diffuse shallow landslides are one of the most critical natural hazards as they often evolve into highly destructive flow slides and debris flows. Vegetation is recognised to play a key role in landslide occurrence and is frequently invoked as a potential remedial measure for slope stabilisation at the catchment scale. The beneficial action of vegetation is generally associated with mechanical (root anchoring) and hydrological (suction generated by root water uptake) effects. There is indeed a third effect that has thus far been little explored. The rhizosphere, the portion of soil directly affected by plant roots, is characterised by hydraulic conductivity higher than the underlying soil horizons. This significantly affects hillslope hydrology by promoting lateral diversion of rainwater. This paper presents a case study in Scotland where the rhizosphere is demonstrated to play a major role in controlling shallow landslides. Field investigation and laboratory testing were carried out to characterise the hydraulic conductivity of the rhizosphere and deeper horizons. In turn, this formed the basis for the development of a physically based model for the slope. The model was first validated against its capability to simulate failure of two historical landslides and then exploited to demonstrate the beneficial effect of the rhizosphere. The lesson learned from this study is that shallow landslide hazard can be mitigated by enhancing the capacity of the rhizosphere to act as a natural lateral drainage. This implies that plants with root-system architecture that enhances lateral subsurface flow should be privileged when designing vegetation-based remedial measures. [ABSTRACT FROM AUTHOR]

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The 2018 Hokkaido Eastern Iburi Earthquake struck the eastern Iburi region (epicenter: 42.691°N, 142.007°E, depth: 37.0 km) of Hokkaido, Japan, at 3:07.59 JST, September 6, 2018 (18:07.59, September 5, 2018 UTC). Many shallow landslides were triggered by this Mw 6.6 (Mj 6.7) earthquake. The basement complex in the affected area (sedimentary rocks) is covered with thick pyroclastic fall deposits derived from the Tarumae Volcano, etc., and the strong seismic shocks triggered shallow landsliding of them. Shallow landslides moving along valley type topography traveled greater distances than those moving along planar slope topography. Some shallow landslides occurred on relatively gentle slopes (< 30°). The earthquake also induced several large-scale deep-seated landslides, including one that has formed a landslide dam in the Hidaka-horonai River. Landslides were densely distributed over hilly regions (elevation: 200–400 m) within an area of approximately 400 km2 in Atsuma (landslides caused 36 deaths), Abira, and Mukawa, and the number of landslides and the total area of the landslides were the largest in Japan ever since the Meiji Era (1868–1912). The catchments where shallow landslides were concentrated were severely devastated. [ABSTRACT FROM AUTHOR]

In mechanism studies on sudden sediment source areas (SSAs), such as those of shallow landslides, area-specific soil strength information at the soil layer between saturated and unsaturated soil is an important factor. Although various soil-testing methods have been developed, it is difficult to measure soil strength directly due to local variation. Therefore, the purpose of this study is to estimate SSAs of shallow landslides by considering the soil strength attributes reflected in a simple subsurface hydrologic concept (SHC). The selected study area is a granitic region in Jinbu, eastern South Korea, where shallow landslides occurred at more than 1200 locations in 2006. To estimate SSAs, soil strength was generated using several methods (i.e., direct shear tests [DSTs], Monte Carlo-simulated direct shear tests [MSDTs], and triaxial compression test [TCTs]). In addition, to investigate the effects of soil attributes in saturated/unsaturated soil on the SHC, soil depth was surveyed at a small hillslope site within the large study area. A topographically driven physical shallow landslide stability model (SHALSTAB) was utilized to investigate the performance based on the various soil strength attributes, and the results were analyzed using receiver operating characteristic (ROC) analyses. In the evaluation using the three soil strength measures (Case I), the accuracy assessment of the estimated SSAs was relatively low (range of 0.45–0.71). Using the soil strength attribute reflecting SHC (Case II), the results showed a reasonable range of accuracy (0.84–0.85). In addition, using the effects of soil strength reflected by SHC and average soil depth (Case III) also showed good predictive accuracy (0.81–0.82), but it was lower than that of Case II. This indicates that combining the hydraulic properties related to soil strength facilitates more accurate identification of SSAs causing geomorphic surface changes. [ABSTRACT FROM AUTHOR]

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

Abstract Dendrogeomorphic methods are frequently used for the analysis of past landslide behaviour and have become the standard approach used to date landslide activity. Unfortunately, many questions related to the application of tree-rings to landslide analysis remain unsolved. This study points to the significance of dividing a large complex slope deformation area into homogenous zones to obtain as much relevant chronological data as possible and to help with a more precise landslide hazard assessment. The multidisciplinary approach included geophysical measurement and geomorphic mapping. The clay mineral content in weathered slope sediments was analysed to verify the presence of expanding minerals contributing to slope instability. Furthermore, 713 samples from 271 trees and 18 tree roots were analysed to create event chronologies for each zone. The results provided evidence about the different behaviours of each zone. The intensity of tree responses to slope movement significantly changed even within one isolated zone. Chronological data were used to identify landslide triggers using rainfall characteristics and indices for climate variability and extremes. The results suggested a significant contribution of spring rainfall to landslide activity. In addition, shallow landslides movement was dependent on above-average rainfall characteristics compared to the movement of deep-seated blocks. Tree-ring-based chronologies of individual zones were used to characterise the general concept of complex slope deformation development. Graphical abstract Unlabelled Image Highlights • Multidisciplinary approach for complex slope deformation study • Dendrogeomorphic analysis reveals connected and disconnected slope. • Exposed root analyses confirm and refine the site chronology. • Various patterns of precipitation triggers were detected. • New insight to slope deformation behaviour using multidisciplinary approach [ABSTRACT FROM AUTHOR]

Degradation of slopes due to shallow landslide and the subsequent erosional processes are a big challenge on the application of soil bioengineering techniques; that is the use of plants as main structural components of a slope protection and conservation system. An optimal application of soil bioengineering techniques should include not only the technical factor of plants as structural components but also the ecology of species and the plant adaptations to disturbances, which is crucial if a longterm successful slope restoration system is intended. Ferns are a dominant understory vegetation species in the forest of Japan, but its characteristics and influences on the recovery of shallow landslide scars have not been fully studied yet. This study aims to find out the ecological characteristics of fern species through the calculation of ecological indicators and the quantification of the morphological features of specimens growing on disturbed and non-disturbed forest slopes in Japan. Gleichenia japonica was found as the vegetation species with biggest ecological indicators on both slopes. The analysis of morphological characteristics of the specimens growing on both sites showed that the development of the specimens is focused in below-ground characteristics. The pull-out force of Gleichenia japonica root system as an indicator of ecological adaptation to a constraint environment and morphological characteristics quality is influenced by height and root length according to the principal component analysis. The eco-morphological characteristics of species can be used as an indicator of an optimal element in soil bioengineering establishment for slope conservation proposes. The long and fibrous root system could be placed on forest roads, steep or small slopes where space limitation is an issue for the establishment of bigger species and if the slope conditions allow it, it can control soil losses due to rainfall and provide stability. [ABSTRACT FROM AUTHOR]

Abstract A new landslide cannot form until the soil has recovered to the critical depth for the recurrence of a landslide through the weathering of bedrock and soil transportation from adjacent areas. In volcanic areas with tephra deposits, landslides expose tephra and not bedrock. Therefore, the immunity of landslides in volcanic areas may be different from that of landslides in non-volcanic areas. Herein, we developed landslide inventory maps (LIMs) for 6 periods during 1955–2016 using aerial photographs and digital elevation models in northern Aso Volcano. In this area, landslides were found to continuously occur due to rainfall events and a large earthquake. Using the 6 LIMs, we examined the terrain attributes (i.e., slope angle, slope aspect, and normalized distance to ridge) and the overlap of landslides. Among the terrain attributes, slope angle was a dominant factor affecting the occurrence of landslides caused by both rainfall events and an earthquake. The total landslide areal density in 2016 was 50% for a slope angle of 35%–45%. 2 atypical events (a rainfall in July 2012 and an earthquake in April 2016) caused landslides to occur on slopes that were relatively resistant to landslides by typical amounts of rainfall, resulting in high landslide density in 2016. The intensity of rainfall for an event in July 2012 was considerably higher than that for other rainfall events. The type of landslides caused by an earthquake in April 2016 was different from that of landslides caused by rainfall. The depths of some landslides caused by this earthquake were deeper than those of landslides caused by the rainfall in July 2012. The overlap ratio was <2.3% for all combinations of the 6 LIMs. The small overlap ratio in the study area suggests that the immunity of landslides continued during the 60-year period we examined. Further research clarifying the process leading to subsequent landslides would be useful to better understand the immunity of landslide and the associated landslide susceptibility in volcanic areas. Highlights • Six landslide inventory maps during 1955–2016 were developed in a volcanic area. • Slope angle strongly affected the occurrence of landslides. • An extreme rainfall and a major earthquake caused a large landslide density. • The overlap ratio was <2.3% for all combinations of the six maps. • The immunity of landslides could be higher in volcanic areas than in other areas. [ABSTRACT FROM AUTHOR]

Shallow landslides are an increasing concern in Italy and worldwide because of the frequent association with vegetation management. As vegetation cover plays a fundamental role in slope stability, we developed a GIS-based model to evaluate the influence of plant roots on slope safety, and also included a landslide susceptibility map. The GIS-based model, 4SLIDE, is a physically based predictor for shallow landslides that combines geological, topographical, and hydrogeological data. The 4SLIDE combines the infinite slope model, TOPMODEL (for the estimation of the saturated water level), and a vegetation root strength model, which facilitates prediction of locations that are more susceptible for shallow landslides as a function of forest cover. The aim is to define the spatial distribution of Factor of Safety (FS) in steep-forested areas. The GIS-based model 4SLIDE was tested in a forest mountain watershed located in the Sila Greca (Cosenza, Calabria, South Italy) where almost 93% of the area is covered by forest. The sensitive ROC analysis (Receiver Operating Characteristic) indicates that the model has good predictive capability in identifying the areas sensitive to shallow landslides. The localization of areas at risk of landslides plays an important role in land management activities because landslides are among the most costly and dangerous hazards.

Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for preventing and mitigating landslide hazards as it provides information regarding locations where landslides are most likely to occur. The main purpose of this study is to produce a landslide susceptibility map of Mt. Umyeon in Korea using an artificial neural network (ANN) involving the factor selection method and various non-linear activation functions. A total of 151 historical landslide events and 20 predisposing factors consisting of Geographic Information System (GIS)-based morphological, hydrological, geological, and land cover datasets were constructed with a resolution of 5 x 5 m. The collected datasets were applied to information gain ratio analysis to confirm the predictive power and multicollinearity diagnosis to ensure the correlation of independence among the landslide predisposing factors. The best 11 predisposing factors that were selected in this study were randomly divided into a 70:30 ratio for training and validation datasets, which were used to produce ANN-based landslide susceptibility models. The ANN model used in this study had a multi-layer perceptron (MLP) structure consisting of an input layer, one hidden layer, and an output layer. In the output layer, the logistic sigmoid function was used to represent the result value within the range of 0 to 1, and six non-linear activation functions were used for the hidden layer. The performance of the landslide susceptibility models was evaluated using the receiver operating characteristic curve, Kappa index, and five statistical indices (sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) with the training dataset. In addition, the landslide susceptibility models were validated using the aforementioned measures with the validation dataset and were compared using the Friedman test to check the significant differences among the six developed models. The optimal number of neurons was determined based on the aforementioned performance evaluation and validation results. Overall, the model with the best performance was the MLP model with the logistic sigmoid activation function in the output layer and the hyperbolic tangent sigmoid activation function with five neurons in the hidden layer. The validation results of the best model showed a sensitivity of 82.61%, specificity of 78.26%, accuracy of 80.43%, PPV of 79.17%, NPV of 81.82%, a Kappa index of 0.609, and AUC of 0.879. The results of this study highlight the effectiveness of selecting an optimal MLP model structure for shallow landslide susceptibility mapping using an appropriate predisposing factor section method.

Heavy rainfall is a key cause of shallow landslides in red-bed terrains with steep topography and residual soils of degradable strength. In this study, laboratory model tests were carried out to examine the characteristics of rainfall infiltration, deformation, and failures of slopes in the red-bed area of Sichuan. The hydrological response and deformation of the slope soil during rainfall are addressed. Based on a modified Green-Ampt infiltration model, ponding along the bedrock surface is incorporated. A physically based model for shallow landslides caused by rainfall is developed. The theoretical analysis and the model test results indicate that the slope failures are related to erosion in the shallow soil layer and rainwater infiltration, particularly along preferential seepage channels. The process of rainfall-induced shallow landslides can be separated into three stages: erosion at the slope toe, tension crack formation at the slope crest, and shallow sliding. When initial underground water level is located at the bedrock surface or the preferential seepage flow quickly reaches the bedrock surface, it is easier for the soil slopes to slide along the bedrock surface than along the wetting front. [ABSTRACT FROM AUTHOR]

Rainfall-induced shallow landslide is a common geological hazard around the world that can pose serious threat to both lives and property. Previous studies suggested that rainfall induced shallow landslides always occurred either above the bottom of a downward wetting front in infiltration, or below a rising water table. This paper proposed a different mechanism of shallow landslides in a modelling approach. The comprehensive physics-based Integrated Hydrology Model (InHM) and the infinite slope stability model were employed on a virtual slope to simulate the hydrologic response and estimate the slope stability. Different failure mechanisms with various characteristics were investigated under diverse rainfall scenarios (i.e., various combinations of rainfall depths, durations and temporal patterns). The results showed a novel mechanism of shallow landslides: a significant vertical change in saturated hydrologic conductivity causes the accumulation of infiltrated water, and subsequently leading to an increase pore pressures and landslides in unsaturated soil layer. More importantly, this kind of landslide would bring larger landslide volume even with smaller total rainfall depths. This study expanded our understanding about landslides and had important application in alleviating the loss of lives and property. [ABSTRACT FROM AUTHOR]

Shallow landslide represents one of the most devastating morphodynamic processes that bring about great destructions to human life and infrastructure. Landslide spatial prediction can significantly help government agencies in land use and mitigation measure planning. Nevertheless, landslide spatial modeling remains a very challenging problem due to its inherent complexity. This study proposes an integration of geographical information system (GIS) and gene expression programming (GEP) for predicting rainfall-induced shallow landslide occurrences in Son La Province, Vietnam. A landslide inventory map has been constructed based on historical landslide locations. Furthermore, a dataset which features 12 influencing factors is collected using GIS technology. Based on the GEP algorithm and the collected dataset, an empirical model for spatial prediction of the shallow landslide has been established by means of natural selection. The predictive capability of the model has been verified by the area under the curve calculation. Experimental results point out that the newly proposed approach is a promising tool for shallow landslide prediction. [ABSTRACT FROM AUTHOR]

Shallow landslides are unforeseeable phenomena often resulting in critical conditions in terms of people’s safety and damage. The main purpose of this paper is the comparison of different statistical methods used to determine the rainfall thresholds for the shallow landslide occurrence. Rainfall data over a 46-year period were collected for one rain gauge located in a test area of northwest Italy (Riviera Spezzina; RS). In the RS, intense rainfalls often induce shallow landslides causing damage and sometimes casualties. The rainfall events occurred in the 1967-2006 period were classified as events inducing shallow landslides (SLEs1967-2006) and events that did not trigger shallow landslides (NSLEs1967-2006). Thresholds for various percentiles of SLEs1967-2006 were computed by identifying the lower limit above which shallow landslides occurred. Another set of thresholds, corresponding to different probabilities of occurrence, was determined using SLEs1967-2006 and NSLEs1967-2006. The least-squares linear fit (LSF) and the quantile regression (QR) techniques were employed in the former approach, while the logistic regression (LR) was applied in the latter. The thresholds were validated with the same data used for their definition and with the data recorded in the 2008-2014 period. Contingency tables were created and contingencies and skill scores were computed. The 10% probability threshold obtained using the LR method is characterized by the best values of at least two skill scores for both periods considered; therefore, it may be considered the “best” threshold for the RS. The results of this work can help the choice of the best statistical method to determine the shallow landslide rainfall thresholds. [ABSTRACT FROM AUTHOR]

Physically-based models are reliable techniques for landslide susceptibility assessment, therefore, several models have been proposed in the literature. In this study, two different hydrological models (FSLAM and TRIGRS) and two slope stability calculation methods (infinite slope and limit equilibrium method with spherical failure surface in SCOOPS3D) were compared. The rainfall-triggered shallow landslides that occurred in Kaptanpaşa, Rize (Turkey) in 2017 were selected as a case study. The main focus is investigating the impact of landslide morphology on the success of the slope stability models. For this purpose, landslides in the inventory were grouped according to their morphological characteristics, such as the landslide depth, length, width and their ratios, and their compatibility with model assumptions (e.g. the infinite slope assumption). Then, the model performances in different landslide types were evaluated separately. To calibrate the model parameters a new time-saving, semi-automated calibration strategy, based on a sensitivity analysis, is proposed. Concretely, stability parameters c ′ ∅ ′ Z were calibrated via a MATLAB code by considering the area under the curve (AUC) and extreme conditions: saturated and initial conditions. Overall, SCOOPS3D is deduced to have better performance than infinite slope analysis. However, the accuracy of SCOOPS3D is revealed to decrease for landslides with a high ratio of length/depth and width/depth. From the comparison of hydrological models, FSLAM yielded more plausible results than TRIGRS with the available data. It is demonstrated that the morphological features of the landslides significantly influence the performance of the physically-based stability models. Furthermore, it is highlighted that the resolution of the Digital Elevation Model in comparison to the width and length of the landslides in an inventory can be influential in the performance of the physical model. 10 m resolution of DEM is deemed to be suitable for the study area judging the predictive performance in small landslides. • Landslide morphology affects physically-based landslide susceptibility assessments. • A new calibration strategy using stability parameters c ′ ∅ ′ Z via a MATLAB code. • Validation using 1025 shallow landslides triggered by rainfall in Rize,Turkey, 2017. • TRIGRS, FSLAM and SCOOPS3D are compared for landslide susceptibility assessment. [ABSTRACT FROM AUTHOR]