Modelos de autorregresión espacial para la evaluación de la susceptibilidad por movimientos en masa

As a critical predisaster warning tool, landslide susceptibility assessment is crucial in disaster prevention and mitigation efforts. However, earlier methods for assessing landslide susceptibility have often ignored the impact of similarities in geographical attributes, restricting their feasibility in regions with diverse characteristics. The geographical-optimal-similarity (GOS) model effectively captures similarity relations within geospatial data and can isolate region-specific landslide features, thus overcoming this challenge. Consequently, a landslide susceptibility assessment method was developed by integrating the information value (IV) model with the GOS model. Huangshan City in Anhui Province, China, was selected as the study region. This research used 11 remote sensing feature factors and 657 historical landslide points, combined with the IV model, to construct a dataset for landslide prediction and susceptibility assessment using the GOS model. The findings indicate that, compared to conventional methods such as random forest, logistic regression, and radial basis function classifier, the GOS model enhances the area under the curve (AUC) value by 2.81% to 8.92%, reaching 0.846. This demonstrates superior performance and confirms the effectiveness and accuracy of the method in landslide susceptibility assessment. Furthermore, compared to the basic-configuration-similarity (BCS) model, the GOS model increases the AUC value by 9.64%, achieving 0.846. This approach substantially diminishes the effects of historical data accuracy, revealing upgraded applicability in landslide susceptibility evaluations. Landslides in Huangshan City are primarily influenced by rainfall and vegetation cover. High-susceptibility zones are predominantly located in areas with high precipitation and low vegetation cover. In contrast, low-susceptible and non-susceptible zones are primarily found in flat areas with high vegetation cover and farther from fault lines. The majority of the study region lies within landslide-prone zones, with non-susceptible areas comprising only 12.43% of the total area. Historical landslides are largely concentrated in moderate- to high-susceptibility zones, accounting for 92.24% of all landslide occurrences. Landslide density increases with the susceptibility level, with a density of 0.15 landslides per square kilometre in high-susceptibility zones. This study brings forward a reliable strategy for establishing the spatial relationship between geographical attribute similarity and landslide susceptibility, bolstering the method’s adaptability across various regions.

The recent census in India revealed that India is now housing 17% of the world’s population, and India is on the way to become the most populated country. Landslides are an increasing concern in India due to the rapid population expansion in hilly and mountainous terrain. Landslides affect vast areas within India, in particular in the Himalayan chain in the North and Eastern part of the country and the Western Ghats in the Southwest. The Geological Survey of India (GSI) has been designated as the nodal agency for landslides by the Indian government, and they are responsible for landslide inventory, susceptibility and hazard assessment. Until recently their landslide susceptibility assessment was based on a heuristic approach using fixed weights or ranking of geofactors, based on guidelines of the Bureau of Indian Standards (BIS). However, this method is disputed as it doesn’t provide accurate results. This paper gives an overview of recent research on how the existing methods for landslide inventory, susceptibility and hazard assessment in India could be improved, and how these could be used in (semi)quantitative risk assessment. Due to the unavailability of airphotos in large parts of India, satellite remote sensing data has become the standard data input for landslide inventory mapping. The National Remote Sensing Center (NRSC) has developed an approach using semi-automatic image analysis algorithms that combine spectral, shape, texture, morphometric and contextual information derived from high resolution satellite data and DTMs for the preparation of new as well as historical landslide inventories. Also the use of existing information in the form of maintenance records, and other information to generate event-based landslide inventories is presented. Event-based landslide inventories are used to estimate the relation between temporal probability, landslide density and landslide size distribution. Landslide susceptibility methods can be subdivided in heuristic, statistical and deterministic methods. Examples are given on the use of these methods for different scales of analysis. For medium scales a method is presented to analyze the spatial association between landslides and causal factors, including those related to structural geology, to select the most appropriate spatial factors for different landslide types, and combine them using the multivariate methods. For transportation corridors a method is presented for quantitative hazard and risk assessment based on a landslide database. Deterministic methods using several dynamic slope-hydrology and slope stability models have been applied to evaluate the relation between land use changes and slope stability in a steep watershed. The paper ends with an overview how the susceptibility maps can be combined with the landslide databases to convert them into hazard maps which are subsequently used in (semi) quantitative risk assessment at different scales of analysis, and how the results can be used in risk reduction planning.

Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions

A multi-annual landslide inventory for the assessment of shallow landslide susceptibility – Two test cases in Vorarlberg, Austria

Nepal is facing the threat of landslides each year causing huge loss of lives and properties. Landslide prediction and susceptibility assessments help in identifying the potential zones of landslide occurrences and provide opportunities to treat them prior to their occurrence. Among different methods of landslide susceptibility mapping, the InfoVal method is one of the simple and useful methods. In this study, this method is used to study the landslide susceptibility in the Marin Khola Watershed within the Siwaliks of central Nepal as this area comprises of the weak geological formations that contribute to high potentialities of landslides, yet there are no studies for predicting landslides. A total of 217 landslides were taken for the study and they were divided into two groups: working landslides and validating landslides. 75% of these total landslides were selected as working landslides and the remaining 25% were selected for validating landslides. Spatial relationships of the landslide distribution with different causative factors including topographic factors, hydrologic factors, geological factors and landuse factors were employed and analyzed. The results depict that very high, high, moderate, low and very low susceptibility classes cover 1.15%, 49.93%, 30.17%, 11.48%, and 11.28% area, respectively. The Middle Siwaliks are most susceptible to landslides compared to the Upper Siwaliks and Lower Siwaliks. The accuracy values are found to be affected by the difference in the landslide characteristics and types occurring in the study area. The model accuracy remains at 66% and predictive accuracy at 75%.

Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal landslide inventory that was derived from a 30-year time series of satellite remote sensing data using an automated identification approach. To evaluate the effect of the resulting inventory on the landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical inventory that was derived by an expert through combining visual interpretation of remote sensing data with already existing knowledge on landslide activity in this region. For both susceptibility models, the same predisposing factors were used: geology, stream power index, absolute height, aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide inventory covering the 30-year period results in model coefficients and susceptibility values that more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility maps present the highest susceptibility values for similar regions and are characterized by acceptable to high predictive performances. We conclude that the results of the automated landslide detection provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment. We also used the temporal information of the automatically detected multi-temporal landslide inventory to assess the temporal component of landslide hazard in the form of exceedance probability. The results show the great potential of satellite remote sensing for deriving detailed and systematic spatio-temporal information on landslide occurrences, which can significantly improve landslide susceptibility and hazard assessment at a regional scale, particularly in data-scarce regions such as Kyrgyzstan.

The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, the authors formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model.

Establishing proper neighbor relations between a set of spatial units under analysis is essential when carrying out a spatial or spatio-temporal analysis. However, it is usual that researchers choose some of the most typical (and simple) neighborhood structures, such as the first-order contiguity matrix, without exploring other options. In this paper, we compare the performance of different neighborhood matrices in the context of modeling the weekly relative risk of COVID-19 over small areas located in or near Valencia, Spain. Specifically, we construct contiguity-based, distance-based, covariate-based (considering mobility flows and sociodemographic characteristics), and hybrid neighborhood matrices. We evaluate the goodness of fit, the overall predictive quality, the ability to detect high-risk spatio-temporal units, the capability to capture the spatio-temporal autocorrelation in the data, and the goodness of smoothing for a set of spatio-temporal models based on each of the neighborhood matrices. The results show that contiguity-based matrices, some of the distance-based matrices, and those based on sociodemographic characteristics perform better than the matrices based on k-nearest neighbors and those involving mobility flows. In addition, we test the linear combination of some of the constructed neighborhood matrices and the reweighting of these matrices after eliminating weak neighbor relations, without any model improvement.

Landslides pose a significant threat to the safety of reservoirs, particularly those situated in canyon terrains. This study aims to enhance the safety and security of reservoir areas by proposing an integrated method for the automatic identification and assessment of landslides. By combining the SBAS-InSAR technique with spatial clustering analysis, we successfully delineated landslide areas and developed a new landslide susceptibility assessment model. This model operates independently of historical landslide inventory data. Based on the delineated landslide areas, we enhanced the information value model using the inverse tangent function, which was then integrated with Random Forest and Extreme Gradient Boosting methods for landslide susceptibility assessment. The identified landslides were validated through field tests, demonstrating a high degree of consistency with actual conditions. The results indicated that, in canyon-type reservoirs, aspect was a critical factor influencing landslide occurrence, with susceptibility being greater near water bodies. In model comparisons, the RF-NIV model outperformed, providing a more realistic representation of landslide susceptibility distribution. These findings offer valuable insights for landslide safety management in canyon-type reservoirs, such as those in Hekou Village and Baihetan.

Car ownership is linked to higher car use, which leads to important environmental, social and health consequences. As car ownership keeps increasing in most countries, it remains relevant to examine what factors and policies can help contain this growth. This paper uses an advanced spatial econometric modeling framework to investigate spatial dependences in household car ownership rates measured at fine geographical scales using administrative data of registered vehicles and census data of household counts for the Island of Montreal, Canada. The use of a finer level of spatial resolution allows for the use of more explanatory variables than previous aggregate models of car ownership. Theoretical considerations and formal testing suggested the choice of the Spatial Durbin Error Model (SDEM) as an appropriate modeling option. The final model specification includes sociodemographic and built environment variables supported by theory and achieves a Nagelkerke pseudo-R2 of 0.93. Despite the inclusion of those variables the spatial linear models with and without lagged explanatory variables still exhibit residual spatial dependence. This indicates the presence of unobserved autocorrelated factors influencing car ownership rates. Model results indicate that sociodemographic variables explain much of the variance, but that built environment characteristics, including transit level of service and local commercial accessibility (e.g., to grocery stores) are strongly and negatively associated with neighborhood car ownership rates. Comparison of estimates between the SDEM and a non-spatial model indicates that failing to control for spatial dependence leads to an overestimation of the strength of the direct influence of built environment variables.

Landslide susceptibility assessment is a major research topic in geo-disaster management. In recent days, various landslide susceptibility and landslide hazard assessment methodologies have been introduced with diverse thoughts of assessment and validation method. Fundamentally, in landslide susceptibility zonation mapping, the susceptibility predictions are generally made in terms of likelihoods and probabilities. An overview of landslide susceptibility zoning practices in the last few years reveals that susceptibility maps have been prepared to have different accuracies and reliabilities. To address this issue, the work in this paper focuses on extreme event-based landslide susceptibility zonation mapping and its evaluation. An ideal terrain of northern Shikoku, Japan, was selected in this study for modeling and event-based landslide susceptibility mapping. Both bivariate and multivariate approaches were considered for the zonation mapping. Two event-based landslide databases were used for the susceptibility analysis, while a relatively new third event landslide database was used in validation. Different event-based susceptibility zonation maps were merged and rectified to prepare a final susceptibility zonation map, which was found to have an accuracy of more than 77 %. The multivariate approach was ascertained to yield a better prediction rate. From this study, it is understood that rectification of susceptibility zonation map is appropriate and reliable when multiple event-based landslide database is available for the same area. The analytical results lead to a significant understanding of improvement in bivariate and multivariate approaches as well as the success rate and prediction rate of the susceptibility maps.

BackgroundThousands of landslides were triggered by the Hokkaido Eastern Iburi earthquake on 6 September 2018 in Iburi regions of Hokkaido, Northern Japan. Most of the landslides (5627 points) occurred intensively between the epicenter and the station that recorded the highest peak ground acceleration. Hundreds of aftershocks followed the major shocks. Moreover, in Iburi region, there is a high possibility of earthquakes occurring in the future. Effective prediction and susceptibility assessment methods are required for sustainable management and disaster mitigation in the study area. The aim of this study is to evaluate the performance of an autoencoder framework based on deep neural network for prediction and susceptibility assessment of regional landslides triggered by earthquakes.ResultsBy applying 12 sampling sizes and 12 landslide-influencing factors, 12 landslide susceptibility maps were produced using an autoencoder framework. The results of the model were evaluated using qualitative and quantitative assessment methods. The ratios of the sampling sizes on the non-landslide points randomly generated from the combination zone including plain and mountain (PM) and a mountainous only zone (M) affected different prediction abilities of the model’s performance.ConclusionsThe 12 susceptibility maps, including the landslide susceptibility index, indicated the various spatial distributions of the landslide susceptibility values in both PM and the M. The highly accurate models explicitly distinguished the potential areas of landslide from stable areas without expanding the spatial extent of the potential landslide areas. The autoencoder is proved to be an effective and efficient method for extracting spatial patterns through unsupervised learning for the prediction and susceptibility assessment of landslide areas.

Review on landslide susceptibility mapping using support vector machines

Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30–90 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%–84%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area.

The constant threat from landslides in the northeastern part of Istria, Croatia, calls for the need to apply accurate and reliable methods in landslide hazard assessment in order to prevent landslide damage and to set an early warning system if necessary. Furthermore, landslide susceptibility and hazard assessment enable optimal area management and regional urban planning. The study area is in the northeastern and central part of the Istrian Peninsula, well known as an area of frequent, small and shallow slope instability phenomena. Landslide susceptibility assessment in the area around the city of Buzet was performed using a deterministic landslide susceptibility model in the LS-RAPID software. LS-RAPID was developed to analyze stability at one single location, but the performed analysis has shown that LS-RAPID can be used as a powerful tool in landslide susceptibility and hazard assessment on regional scale. The objective of this paper is to establish the influence of the runout potential on the enlargement of the landslide-susceptible zones, due to expansion of the failure area around the initial failure zone. Performed analysis of rainfall return periods shows the frequency of landslide occurrence and provides the possible correlation with the time component of landslide hazard in the area.