Slope Units Research Articles

Landslide Hazard Assessment in the Northern Mountainous Areas of Tianshan Mountains Based on GIS

China is a country prone to geological disasters, especially in the northern mountainous areas of the Tianshan Mountains in Xinjiang, where the surface vegetation is sparse and the rainfall is concentrated, which is prone to landslides and brings a lot of losses to the local people. Based on the field investigation, this paper evaluates the landslide susceptibility in the northern mountainous area of Tianshan Mountains. The frequency ratio method is used to calculate the landslide probability, and the landslide index (LSI) is formed to represent the landslide susceptibility. The slope unit method is used to determine the landslide units, which values were calculated by the average of the landslide index. According to the calculated LSI range of 4.53 - 20.60. It is divided into 4 grades, LSI = 4.53 - 9, which is an area that is not prone to landslides, with an area of 891.69 km2. LSI = 9 - 11 indicates an area where landslides are more likely to occur, with an area of 1252.31 km2. LSI = 11 - 13 indicates the area is more prone to landslides, with an area of 714.86 km2. LSI > 13 indicates the most prone area for landslides, with an area of 924.60 km2.

Generation of Homogeneous Slope Units Using a Novel Object-Oriented Multi-Resolution Segmentation Method

The generation of map units is a fundamental step for an appropriate assessment of landslide susceptibility. Recent studies have indicated that the terrain relief-based slope units perform better in homogeneity compared with the grid units. However, it is difficult at present to generate high-precision and high-matching slope units by traditional methods. The problem commonly concentrates in the plain areas without obvious terrain reliefs and the junction of sudden changes in terrain. In this paper, we propose a novel object-oriented segmentation method for generating homogeneous slope units. Herein, the multi-resolution segmentation algorithm in the image processing field is introduced, enabling the integration of terrain boundary conditions and image segmentation conditions in slope units. In order to illustrate the performances of the proposed method, Kitakyushu region in Japan is selected as a case study. The results show that the proposed method generates satisfactory slope units that satisfactorily reproduce the actual terrain relief, with the best within-unit and between-unit homogeneities compared with the previous methods, in particular at the plain areas. We also verify the effectiveness of the presented method through the sensitivity analysis using different resolutions of digital elevation models (DEMs) data of the region. It is reported that the presented approach is notably advanced in the requirements of the quality of DEM data, as the presented approach is less sensitive to DEM spatial resolution compared with other available methods.

Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results

Landslide susceptibility is one of the main topics of geomorphological risk studies. Unfortunately, many of these studies applied an exclusively statistical approach with little coherence with the geomorphodynamic models, resulting in susceptibility maps that are difficult to read. Even if many different models have been developed, those based on statistical techniques applied to slope units (SUs) are among the most promising. SU segmentation divides terrain into homogenous domains and approximates the morphodynamic response of the slope to landslides. This paper presents a landslide susceptibility (LS) analysis at the catchment scale for a key area based on the comparison of two GIS-based bivariate statistical methods using the landslide index (LI) approach. A new simple and reproducible method for delineating SUs is defined with an original GIS-based terrain segmentation based on hydrography. For the first time, the morphometric slope index (MSI) was tested as a predisposing factor for landslides. Beyond the purely statistic values, the susceptibility maps obtained have strong geomorphological significance and highlight the areas with the greatest propensity to landslides. We demonstrate the efficiency of the SU segmentation method and the potential of the proposed statistical methods to perform landslide susceptibility mapping (LSM).

Late Palaeozoic-Neogene sedimentary and tectonic development of the Tauride continent and adjacent Tethyan ocean basins in eastern Turkey: New data and integrated interpretation

The eastern Taurus exemplifies continental rifting, passive margin development, Late Cretaceous melange genesis and ophiolite emplacement. Following Triassic rifting, a carbonate platform developed near sea level in the south (Munzur unit), whereas its northern extension (Neritic-pelagic unit) subsided into deep water during Late Jurassic-Late Cretaceous. Triassic-Cretaceous deep-water sediments and volcanics restore as distal deep-water slope/base of slope units. Jurassic-Cretaceous basic volcanics, interbedded with pelagic sediments, represent emplaced oceanic seamounts. Supra-subduction zone ophiolites formed to the north (c. 93 Ma), probably within an Inner Tauride ocean, and were emplaced southwards by trench-margin collision during latest Cretaceous (c. 75–66 Ma). The margin underwent flexural uplift/erosion and then subsidence/foredeep-infill. Part of the Tauride continent in the south (Malatya Metamorphics) deeply underthrust/subducted northwards, then exhumed rapidly by the late Maastrichtian (c. 65 Ma). To the south, oceanic lithosphere (e.g. Göksun ophiolite) was thrust northward beneath Tauride (Malatya) crust from a more southerly oceanic basin (Berit ocean), and intruded by Late Cretaceous subduction-related granitic rocks (88–82 Ma). Allochthonous units were assembled during the latest Cretaceous, followed by thick-skinned folding/thrusting, generally southwards, related to regional collision tectonics during Mid-Late Eocene. Part of the unmetamorphosed Tauride platform and its over-riding Late Cretaceous allochthon were apparently displaced >60 km northeastwards. Mid-Late Miocene regional collision drove variable folding and re-thrusting, in places northwards. Regional comparisons suggest that the Tauride carbonate platform (Geyik Dağ) narrowed eastwards, such that the palaeogeography of the E Taurides differed from farther west, influencing the late Mesozoic-Cenozoic structural development.

Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments

Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments

Prediction of total landslide volume in watershed scale under rainfall events using a probability model

AbstractThis study established a probability model based on the landslide spatial and size probabilities to predict the possible volume and locations of landslides in watershed scale under rainfall events. First, we assessed the landslide spatial probability using a random forest landslide susceptibility model including intrinsic causative factors and extrinsic rainfall factors. Second, we calculated the landslide volume probability using the Pearson type V distribution. Lastly, these probabilities were joined to predict possible landslide volume and locations in the study area, the Taipei Water Source Domain, under rainfall events. The possible total landslide volume in the watershed changed from 1.7 million cubic meter under the event with 2-year recurrence interval to 18.2 million cubic meter under the event with 20-year recurrence interval. Approximately 62% of the total landslide volume triggered by the rainfall events was concentrated in 20% of the slope units. As the recurrence interval of the events increased, the slope units with large landslide volume tended to concentrate in the midstream of Nanshi River subwatershed. The results indicated the probability model posited can be used not only to predict total landslide volume in watershed scale, but also to determine the possible locations of the slope units with large landslide volume.

Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria

The reliability of input data to be used within statistically based landslide susceptibility models usually determines the quality of the resulting maps. For very large territories, landslide susceptibility assessments are commonly built upon spatially incomplete and positionally inaccurate landslide information. The unavailability of flawless input data is contrasted by the need to identify landslide-prone terrain at such spatial scales. Instead of simply ignoring errors in the landslide data, we argue that modellers have to explicitly adopt their modelling design to avoid misleading results. This study examined different modelling strategies to reduce undesirable effects of error-prone landslide inventory data, namely systematic spatial incompleteness and positional inaccuracies. For this purpose, the Austrian territory with its abundant but heterogeneous landslide data was selected as a study site. Conventional modelling practices were compared with alternative modelling designs to elucidate whether an active counterbalancing of flawed landslide information can improve the modelling results. In this context, we compared widely applied logistic regression with an approach that allows minimizing the effects of heterogeneously complete landslide information (i.e. mixed-effects logistic regression). The challenge of positionally inaccurate landslide samples was tackled by elaborating and comparing the models for different terrain representations, namely grid cells, and slope units. The results showed that conventional logistic regression tended to reproduce incompleteness inherent in landslide training data in case the underlying model relied on explanatory variables directly related to the data bias. The adoption of a mixed-effects modelling approach appeared to reduce these undesired effects and led to geomorphologically more coherent spatial predictions. As a consequence of their larger spatial extent, the slope unit–based models were able to better cope with positional inaccuracies of the landslide data compared to their grid-based equals. The presented research demonstrates that in the context of very large area susceptibility modelling (i) ignoring flaws in available landslide data can lead to geomorphically incoherent results despite an apparent high statistical performance and that (ii) landslide data imperfections can actively be diminished by adjusting the research design according to the respective input data imperfections.

A grid-based physical model to analyze the stability of slope unit

Grid cells and slope units are commonly used in regional landslide analysis models. In practice, both show significant advantages and disadvantages. Due to the simplicity of using regular grid cells, grid cells are favored in landslide analysis models but cannot reflect the boundary of natural slopes and landforms. Slope unit can reflect characteristics of a natural slope. The algorithm of slope stability analysis needs to iteratively calculate the safety factor of each dangerous slip surface, which is more complicated than the stability analysis method of grid cells. To this end, the failure area ratio (rA) of the slope unit, which represents the size of the failure area inside the slope unit under the rainfall infiltration, is employed to establish our landslide analysis model. In this model, larger rA can indicate a greater unstable probability of the slope unit, which is used to identify whether a slope unit is stable or not. Taking the rainfall data provided by Chongqing Meteorological Bureau as inputs, the landslide analysis model was tested in Fengjie County, Chongqing. Results show that the spatial distribution of the predicted landslides has a good correspondence with the observed landslides. The rate of false negative and false positive of the proposed model is 12.3% and 24.3%, and the accuracy of the model is 78.3%.

Engineering geological mapping procedures in data-scarce hillsides for shallow landslide assessments: Applications in southeastern Brazil

This paper shows the results obtained from a group of simple and basic procedures used to assess slope instability in terms of shallow landslides on data-scarce hillsides at the slope unit scale. The procedures are based on engineering geological mapping to obtain geological and geotechnical data and on an adapted Hovland 3D safety factor (SF3D) associated with a van Genuchten infiltration model to simulate the rainfall percolation. The procedures were applied on a data-scarce hillside in the Campos do Jordão municipality located in the Mantiqueira Mountains, São Paulo state, Brazil. This area was chosen because it is affected by shallow landslides due to a loss of shear strength when suction decreases or dissipates due to rainfall infiltration. The combined procedures permitted the total SF3Dto be obtained from the partial SF of each grid cell (considering 4 different rainfall events and quasi-saturation conditions) and the failure probability (SF3D < 1) and marginal probability (1 <SF3D < 1.5) to be calculated for 2 potential rupture surfaces (PRS1 and PRS2) on each slope. The total SF3Dresults under quasi-saturation conditions, which are generated primarily by rainfall events of long duration, are less than 1 for part of the slopes but less than 1.5 for all the slopes, which reflects the favorable conditions for shallow landslides to occur. The failure probability values (SF < 1) for all slopes vary from 0.01 to 0.05 for PRS1 and from 0.02 to 0.12 for PRS2, and the reliability index values range from 1.64 to 2.35 and from 1.22 to 2.08 for PRS1 and PRS2, respectively.All of these results verify that all the slopes and selected potential rupture surfaces are compatible with the occurrence of shallow landslides on December 31st, 1999/January 1st, 2000.Large-scale engineering geological mapping proved to be fundamental because it provided detailed mapping of the spatial arrangement and characterizations of the geological material, thereby permitting adequate definitions of the potential rupture surfaces and the adoption of geotechnical and hydraulic parameters to calculate the SF3D and the failure probability (SF < 1).

Landslide size matters: A new data-driven, spatial prototype

The standard definition of landslide hazard requires the estimation of where, when (or how frequently) and how large a given landslide event may be. The geoscientific community involved in statistical models has addressed the component pertaining to how large a landslide event may be by introducing the concept of landslide-event magnitude scale. This scale, which depends on the planimetric area of the given population of landslides, in analogy to the earthquake magnitude, has been expressed with a single value per landslide event. As a result, the geographic or spatially-distributed estimation of how large a population of landslide may be when considered at the slope scale, has been disregarded in statistically-based landslide hazard studies. Conversely, the estimation of the landslide extent has been commonly part of physically-based applications, though their implementation is often limited to very small regions.In this work, we initially present a review of methods developed for landslide hazard assessment since its first conception decades ago. Subsequently, we introduce for the first time a statistically-based model able to estimate the planimetric area of landslides aggregated per slope units. More specifically, we implemented a Bayesian version of a Generalized Additive Model where the maximum landslide size per slope unit and the sum of all landslide sizes per slope unit are predicted via a Log-Gaussian model. These “max” and “sum” models capture the spatial distribution of (aggregated) landslide sizes. We tested these models on a global dataset expressing the distribution of co-seismic landslides due to 24 earthquakes across the globe. The two models we present are both evaluated on a suite of performance diagnostics that suggest our models suitably predict the aggregated landslide extent per slope unit. In addition to a complex procedure involving variable selection and a spatial uncertainty estimation, we built our model over slopes where landslides triggered in response to seismic shaking, and simulated the expected failing surface over slopes where the landslides did not occur in the past.What we achieved is the first statistically-based model in the literature able to provide information about the extent of the failed surface across a given landscape. This information is vital in landslide hazard studies and should be combined with the estimation of landslide occurrence locations. This could ensure that governmental and territorial agencies have a complete probabilistic overview of how a population of landslides could behave in response to a specific trigger. The predictive models we present are currently valid only for the 25 cases we tested. Statistically estimating landslide extents is still at its infancy stage. Many more applications should be successfully validated before considering such models in an operational way. For instance, the validity of our models should still be verified at the regional or catchment scale, as much as it needs to be tested for different landslide types and triggers. However, we envision that this new spatial predictive paradigm could be a breakthrough in the literature and, in time, could even become part of official landslide risk assessment protocols.

Regional-scale simulation of flowslide triggering in stratified deposits

Capturing the failure mechanisms responsible for static liquefaction is a challenging task for the zoning of landslide susceptibility. While geomechanical models accounting for solid-fluid coupling can identify locations prone to flowslides, their performance in regional settings is impacted by the lack of procedures to replicate accurately stratigraphic heterogeneities. To mitigate this inconvenience, this paper discusses the performance of a spatially-distributed model aimed at capturing distinct landslide triggering mechanisms in layered deposits. The model relies on the simultaneous computation of multiple factors of safety associated with the triggering of frictional slips and liquefaction-induced flowslides. In addition, the spatiotemporal variation of such indices is retrieved from a vectorized finite element platform solving pore pressure transients in stratified unsaturated soils. Such features enable the model to take into account the vertical heterogeneity of individual slope units, while dramatically reducing the computational cost of regional-scale analyses. For illustrative purposes, the model is used to analyze a series of documented shallow landslides that occurred in Campania (Italy) following heavy rainstorms. A comparison of model predictions with and without stratigraphic heterogeneity is provided, showing that the incorporation of site heterogeneity leads to a substantial improvement of the model's spatiotemporal performance, as well as of its ability to identify different modes of failure. Furthermore, the model is applied to four municipalities across the region of interest, each characterized by different representative stratigraphic settings. Despite some mismatches, often linked to the scarcity of site-specific information, the numerical results obtained on the basis of the available dataset shows that the proposed methodology is a robust and effective tool for landslide susceptibility mapping in stratified deposits.

The Evolution of Diagenetic Fluids and Accumulation Characteristics of Tight Sandstone Reservoir in Upper Paleozoic, Southwestern Ordos Basin

The Upper Paleozoic in the southwestern Ordos Basin has significant potential for natural gas exploration. This study investigated the diagenetic fluid evolution and hydrocarbon accumulation characteristics of He 8 section from Permian Lower Shihezi formation and Shan 1 section from Shanxi formation tight sandstone reservoirs by petrographic observation, scanning electron microscope imaging, fluid inclusion study, and laser Raman spectrum analysis. The results show that He 8 section and Shan 1 section reservoirs are mainly composed of quartz sandstone, subordinate arkose quartz sandstone, and lithic quartz sandstone, with minor lithic sandstone and lithic arkose sandstone. The major pores are intergranular dissolved pores. The main diagenetic minerals include quartz overgrowth, siliceous cement, carbonate cement, illite, montmorillonite, and mixed-layer clay minerals. The overall diagenetic features show strong compaction, multistage siliceous and calcareous cements, and abundant clay minerals, strong dissolution, and well-developed fractures. Two stages of fluid inclusions developed in the He 8 and Shan 1 sections recorded the migration and accumulation of the early-stage and late-stage natural gas, respectively. The reservoir in the study area experienced early and late diagenetic stages, and its formation was simultaneous with or after its densification. The diagenetic environment changed from alkaline to acidic and again into alkaline. There are two stages of fluid activities in the study area, namely, the early diagenetic stage corresponding to hydrocarbon generation and migration and the late diagenetic stage corresponding to hydrocarbon accumulation. This study suggests that Upper Paleozoic natural gas migrated into the reservoir in Weibei Uplift, Yishan Slope, and Tianhuan Depression tectonic units during 220-197 Ma, and the large-scale migration and accumulation occurred in these tectonic units at different times. No natural gas was generated in the west margin of the basin because the temperatures of the hydrocarbon source rocks in the Upper Paleozoic were below the gas window.

Combining Satellite InSAR, Slope Units and Finite Element Modeling for Stability Analysis in Mining Waste Disposal Areas

Slope failures pose a substantial threat to mining activity due to their destructive potential and high probability of occurrence on steep slopes close to limit equilibrium conditions, which are often found both in open pits and in waste and tailing disposal facilities. The development of slope monitoring and modeling programs usually entails the exploitation of in situ and remote sensing data, together with the application of numerical modeling, and it plays an important role in the definition of prevention and mitigation measures aimed at minimizing the impact of slope failures in mining areas. In this paper, a new methodology is presented; one that combines satellite radar interferometry and 2D finite element modeling for slope stability analysis at a regional scale, and applied within slope unit polygons. Although the literature includes many studies applying radar interferometry and modeling for slope stability analysis, the addition of slope units as input data for radar interferometry and modeling purposes has, to our knowledge, not previously been reported. A former mining area in southeast Spain was studied, and the method proved useful for detecting and characterizing a large number of unstable slopes. Out of the 1959 slope units used for the spatial analysis of the radar interferometry data, 43 were unstable, with varying values of safety factor and landslide size. Out of the 43 active slope units, 21 exhibited line of sight velocities greater than the maximum error obtained through validation analysis (2.5 cm/year). Finally, this work discusses the possibility of using the results of the proposed approach to devise a proxy for landslide hazard. The proposed methodology can help to provide non-expert final users with intelligible, clear, and easily comparable information to analyze slope instabilities in different settings, and not limited to mining areas.

Wave and storm signals in a lacustrine succession and their relationship to paleowind direction (Tanan Depression, Mongolia, early Cretaceous)

Existing fan-delta depositional models for lacustrine basins do not adequately address significant wave- and storm-related processes. However, in many lakes, such high-energy processes can be effectively driven by winds. As such, the prevailing wind direction determines the propagation direction of wind-induced waves. Observations from modern examples show that wave-dominated deposits commonly exist on one side of the lake, and their sedimentary processes are completely distinct from those on the opposite side. However, equivalent deposits have seldom been identified from subsurface data. We present an ancient example from the Upper Tongbomiao Formation in the Tanan Depression (Tamtsag Basin, Mongolia), which was previously interpreted as a fan-delta depositional system. Based on extensive core investigations, eighteen lithofacies, six lithofacies associations and two genetic deposition systems are identified, and a new sedimentological interpretation is proposed comprising a wave-dominated clastic littoral system in the western half-graben dip slope and a fluvial-dominated fan-delta system in the eastern half-graben. Sediments within the eastern half-graben unit include braidplain, fan-delta front and prodelta environments, while those within the western half-graben dip slope unit comprise dominantly alluvial fan, wave-reworked beach, storm-affected shoreface and offshore sediments. The strikes of the sediments in the western half-graben dip slope unit are parallel to the westward shoreline trend, while the progradation direction of sediments in the eastern half-graben unit is perpendicular to the eastern shoreline. This indicates approximately northwestward-propagating waves that were possibly driven by southeasterly winds, which has implications for understanding the prevailing wind direction in Northeast Asia during the depositional period. Other factors contributing to the preservation of these two distinct sedimentary systems are the paleotopography and subsequent climate shift towards more humid climatic conditions which intensified sediment supply and hydrodynamic conditions that were able to rework the sediments.

Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model

Considering the slope units as our reference mapping units, three statistical models [frequency ratio (FR), index of entropy (IOE), and evidential belief function (EBF)] are used in combination by two methods [normalized spatial-correlated scale index (NSCI) and weighted calibrated landslide potential model (WCLPM)]. For this aim, ten conditioning factors correlated with landslide namely, altitude, slope angle, slope aspect, relief amplitude, cutting depth, gully density, surface roughness, distance to roads, rainfall, and lithology are considered. The performance of the models is tested using the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical evaluation measures. The weighted calibrated landslide potential index (WCLPI)-based FR model has the highest predictive capability, followed by the calibrated landslide potential index (CLPI)-based FR, the WCLPI-EBF, the CLPI-EBF, the WCLPI-IOE, the CLPI-IOE, the FR, the EBF, and the IOE models, respectively. Results indicated that hybrid models have improved significantly the performance of single models. This highlights that NSCI and WCLPM hybrid techniques are promising methods for landslide susceptibility assessment.

Rainfall-induced landslides assessment in the Fengjie County, Three-Gorge reservoir area, China

It is critical to accurately assess the location and evolution of impending landslide hazards, especially when influenced by the effects of climate change. However, most existing landslide models only analyze the stability of individual grid cells without considering soil interaction between adjacent cells. This means that the stability of individual grid cells is not necessarily representative of actual slope stability at a given location, in turn affecting the accuracy of landslide stability assessments. In this paper, slope units are defined using the morphological image analysis and homogeneous slope unit method to represent the actual slope conditions and to extract the profile of each slope unit. The critical slip surface of the soil mass is investigated to account for the interaction between adjacent grid cells within the slope unit. A probabilistic analytical landslide model is established and the response mechanism of the hydrological processes is proposed. This model is applied to landslide hazard assessment in the Three-Gorges Reservoir Area, Fengjie County, China. Using historical rainfall data, the distribution of landslide hazards is analyzed for different antecedent precipitation levels and rainfall return periods. The results show that the amount of antecedent precipitation, and rainfall return period, have different effects on landslide triggering in the area. Importantly, the model reveals that extreme rainfall events induced by climate change will lead to a large number of new landslides in areas where they are not anticipated. Further investigation and monitoring (e.g., early-warning systems, long-term assessment) of rainfall-induced landslides in the Three-Gorges Reservoir Area is recommended.

Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models

Landslide susceptibility mapping is very important for landslide risk evaluation and land use planning. Toward this end, this paper presents a case study in Ningqiang County, Shanxi Province, China. Slope units were selected as the basic mapping units. A traditional statistical certainty factor model (CF), a machine learning support vector machine model (SVM) and random forest model (RF), along with a hybrid CF-SVM model and a CF-RF model were applied to analyze landslide susceptibility. Firstly, 10 landslide conditioning factors were selected, namely slope-angle, altitude, slope aspect, degree of relief, lithology, distance to rivers, distance to faults, distance to roads, average annual rainfall and normalized difference vegetation index. The 23,169 slope units were generated from a Digital Elevation Model and the corresponding 10 conditioning factor layers were produced from both geological and geographical data. Then, landslide susceptibility mapping was carried out using the five models, respectively. Next, the landslide density (LD), frequency ratio (FR), the area under the curve (AUC) and other indicators were used to validate the rationality, performance and accuracy of the models. The results showed that the susceptibility maps produced from the different models were all reasonable. In each map, the LD and FR were greatest in the zones classed as having very high landslide susceptibility, followed by the high, moderate, low and very low landslide susceptibility classes, respectively. From the comparison of the different maps and ROC curves, the RF model based on slope units was the most appropriate for landslide susceptibility mapping in the study area. It was also found that the combination of weaker learner model (CF model here) with a stronger learner model (SVM and RF model here) can impact the applicability of the stronger model.

A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

The main purpose of this study aims to apply and compare the rationality of landslide susceptibility maps using support vector machine (SVM) and particle swarm optimization coupled with support vector machine (PSO-SVM) models in Lueyang County, China, enhance the connection with the natural terrain, and analyze the application of grid units and slope units. A total of 186 landslide locations were identified by earlier reports and field surveys. The landslide inventory was randomly divided into two parts: 70% for training dataset and 30% for validation dataset. Based on the multisource data and geological environment, 16 landslide conditioning factors were selected, including control factors and triggering factors (i.e., altitude, slope angle, slope aspect, plan curvature, profile curvature, SPI, TPI, TRI, lithology, distance to faults, TWI, distance to rivers, NDVI, distance to roads, land use, and rainfall). The susceptibility between each conditioning factor and landslide was deduced using a certainty factor model. Subsequently, combined with grid units and slope units, the landslide susceptibility models were carried out by using SVM and PSO-SVM methods. The precision capability of the landslide susceptibility mapping produced by different models and units was verified through a receiver operating characteristic (ROC) curve. The results showed that the PSO-SVM model based on slope units had the best performance in landslide susceptibility mapping, and the area under the curve (AUC) values of training and validation datasets are 0.945 and 0.9245, respectively. Hence, the machine learning algorithm coupled with slope units can be considered a reliable and effective technique in landslide susceptibility mapping.

From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

The vast majority of statistically-based landslide susceptibility studies assumes the slope instability process to be time-invariant under the definition that “the past and present are keys to the future”. This assumption may generally be valid. However, the trigger, be it a rainfall or an earthquake event, clearly varies over time. And yet, the temporal component of the trigger is rarely included in landslide susceptibility studies and only confined to hazard assessment. In this work, we investigate a population of landslides triggered in response to the 2017 Jiuzhaigou earthquake (M_w = 6.5) including the associated ground motion in the analyses, these being carried out at the Slope Unit (SU) level. We do this by implementing a Bayesian version of a Generalized Additive Model and assuming that the slope instability across the SUs in the study area behaves according to a Bernoulli probability distribution. This procedure would generally produce a susceptibility map reflecting the spatial pattern of the specific trigger and therefore of limited use for land use planning. However, we implement this first analytical step to reliably estimate the ground motion effect, and its distribution, on unstable SUs. We then assume the effect of the ground motion to be time-invariant, enabling statistical simulations for any ground motion scenario that occurred in the area from 1933 to 2017. As a result, we obtain the full spectrum of potential coseismic susceptibility patterns over the last century and compress this information into a hazard model/map representative of all the possible ground motion patterns since 1933. This backward statistical simulations can also be further exploited in the opposite direction where, by accounting for scenario-based ground motion, one can also use it in a forward direction to estimate future unstable slopes.

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model

Landslides are one of the most extensive geological disasters in the world. The objective of this study was to assess the performances of different landslide susceptibility models information content method (ICM), analytical hierarchy process (AHP), and random forest (RF) model) and mapping unit (slope unit and grid unit) for landslide susceptibility mapping in the Helong city, Jilin province, northeastern China. First, a total of 159 landslides were mapped in the study area based on a geological hazard survey (1:50,000) of Helong city. Then, the slope units of the study area were divided by using the curvature watershed method. Next, eight influencing factors, namely, lithology, slope angle, slope aspect, rainfall, land use, seismic intensity, distance to river, and distance to fault, were selected to map the landslide susceptibility based on geological data, field survey, and landslide information. Afterward, landslide susceptibility modeling of landslide inventory data is performed for extracting and learning the symmetry latent in data patterns and relationships by three landslide susceptibility models and utilizing it to predict landslide susceptibility. Finally, the receiver operating characteristic (ROC) curve was used to compare the landslide susceptibility models. In addition, results based on grid units were calculated for comparison. The AUC (the area under the curve) result for ICM, AHP, and RF model was 87.1%, 80.5%, and 94.6% for slope units, and 83.4%, 70.9%, and 91.3% for grid units, respectively. Based on the overall assessments, the SU-RF model was the most suitable model for landslide susceptibility mapping. Consequently, these methods can be very useful for landslide hazard mitigation strategies.