Historical Landslide Data Research Articles

The spatial-temporal evolution patterns of landslide-oriented resilience in mountainous city: A case study of Chongqing, China

Cities, as complex systems with multi-interconnected subsystems, face significant challenges from both rapid urbanization and climate change. Ensuring high resilience in urban areas is essential for managing these dynamic risks effectively. This study introduces an innovative, data-driven approach to quantitatively analyze the spatial-temporal evolution patterns of urban resilience, validated through a case study of Chongqing, a representative mountainous city in China. Based on historical landslide data from Chongqing (2010–2020), which includes 4464 events, along with indicator data from the Chongqing Statistical Yearbook, we developed a comprehensive assessment framework. This framework incorporates 33 variables, covering indicators of physical-environmental resilience (PER) and socio-economic resilience (SER). The model integrates the Random Forest (RF) algorithm, Analytic Hierarchy Process (AHP), and Coupling Coordination Degree (CCD) model. Key findings include: (1) Social development in mountainous cities like Chongqing follows a point-to-area pattern. Although there is an overall increase in SER, the CCD in more developed areas (Chongqing urban circle) was generally higher than in less developed areas (northeastern and southeastern Chongqing) (2) The PER model demonstrated exceptional performance (AUC values consistently above 0.95). Spatiotemporal evolution models reveal that Chongqing maintains a high overall PER. Notably, from 2019 to 2020, the proportion of administrative units classified as highly resilient peaked at 24.5%, marking a historical high. (3) Multi-year average rainfall primarily impacts PER (ranked first), while Gross Domestic Product (GDP) significantly affect SER. The development of multi-dimensional recovery indicators provides a robust framework for assessing resilience against landslides in mountainous cities. The CCD model illustrates the importance of regional dynamic coordinated development in resilience trajectories. This study provides a detailed blueprint for the scientific development of resilient mountainous cities, emphasizing the need for a spatial-temporal perspective on resilience and the benefits of coordinated regional development.

Seismic landslide susceptibility assessment using principal component analysis and support vector machine

Seismic landslides are dangerous natural hazards that can cause immense damage to human lives and property. Susceptibility assessment of earthquake-triggered landslides provides the scientific basis and theoretical foundation for disaster emergency management in engineering projects. However, landslide susceptibility assessment requires a massive amount of historical landslide data. Evidence of past landslide activities may be lost due to changes in geographical conditions and human factors over time. The lack of landslide data poses difficulties in assessing landslide susceptibility. The aim of this study is to establish a generalized seismic landslide susceptibility assessment model for applying it to the Dayong highway in the Chenghai area, where earthquakes occur frequently but with a lack of landslide data. The landslide data used comes from the 2014 Ludian Ms (Surface wave magnitude) 6.5 earthquake in a region with geographical conditions similar to those in the Chenghai area. The influencing factors considered include elevation, slope, slope aspect, distance to streams, distance to faults, geology, terrain wetness index, normalized difference vegetation index, epicenter distance and peak ground acceleration. The frequency ratio method is used to eliminate influencing factors with poor statistical dispersion of landslides. Principal component analysis (PCA) is utilized to reduce the dimensionality of landslide conditioning factors and to improve the transferability of the assessment model to different regions. A support vector machine model is used to establish the susceptibility assessment model. The results show that the accuracy of the PCA–SVM model reaches 93.6%. The landslide susceptibility of the Chenghai area is classified into 5 classes, with the “Very high” landslide susceptibility class accounting for 0.63%. The 13-km section in the middle of the Dayong highway, which accounts for 8.9%, is identified as the high-risk area most obviously impacted by seismic landslides. This study provides a new approach for seismic landslide susceptibility assessment in areas lacking in landslide inventory data.

Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi’an Region, China

This study aims to delineate landslide susceptibility maps using the Analytical Hierarchy Process (AHP) method for the Great Xi’an Region, China, which is a key planning project for urban construction in Shaanxi Province, China from 2021 to 2035. Multiple data as elevation, slope, aspect, curvature, river density, soil, lithology, and land use have been considered for delineating the landslide susceptibility maps. Spatially thematic layers and distributed maps of all the aforementioned parameters were created in a GIS environment. Determine the relative importance of these thematic layers in the occurrence of landslides in the study area concerning historical landslide data to assign appropriate weights. Landslide sensitivity maps were generated by a weighted combination in a GIS environment after being analyzed by the AHP method. The sensitivity maps were categorized as “very high (11.06%), high (19.41%), moderate (23.03%), low (28.70%), and very low (17.80%)”. Overlay analysis of the test data with the LSM showed that the moderate to very high landslide susceptibility zones were able to contain 82.58% of the historic landslides. The results of the study help determine the landslide-prone areas in the area and provide a reference for subsequent construction. In addition, the analysis of landslide susceptibility in the area contributes to the study of landslides in similar loess sites.

Landslide susceptibility zoning based on Rock Engineering System application to the Tehran case study

AbstractLandsliding and slope instability pose significant risks to human settlements and infrastructure. This study aims to develop a landslide susceptibility map for the mountainous regions of Tehran province, Iran. The rock engineering approach was employed at a regional scale, utilizing a database of regional landslides. The semi‐quantitative Rock Engineering System method was applied, considering 10 conditioning factors. The model was validated using historical landslide data, showing strong correlation. The majority of landslides occurred within medium and high susceptibility zones. Geological units, particularly the Karaj Formation's claystone layers, displayed a clear association with landslide susceptibility. The rock engineering approach yielded a highly accurate susceptibility map. Findings can inform future territorial planning in the area.

Spatial Distribution Analysis of Landslide Deformations and Land-Use Changes in the Three Gorges Reservoir Area by Using Interferometric and Polarimetric SAR

Landslides are geological events that frequently cause major disasters. Research on landslides is essential, but current studies mostly use historical landslide data and do not reflect dynamic, real-time research results. In this study, landslide deformations and land-use changes were used to analyze the landslide distribution in Fengjie County and Wushan County in the Three Gorges Reservoir Area (TGRA) by using interferometric and polarimetric SAR. In this study, the mean annual rate of landslide deformations was obtained using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) for the ALOS-2 (2014–2019) data. Land-use changes were based on the 2007 and 2017 land-use results from dual-polarization ALOS-1 and ALOS-2 data, respectively. To address the problem of classification accuracy reduction caused by geometric distortion in mountainous areas, we first used texture maps and pseudocolor maps synthesized with dual-polarization intensity maps to perform classification with random forest (RF), and then we used coherence and slope maps to run the K-Means algorithm (KMA). We named this the secondary classification method. It is an improvement on the single classification method, exhibiting a 94% classification accuracy, especially in rugged areas. Combined with land-use changes, GIS spatial analysis was used to analyze the spatial distribution of landslides, and it was found that the landslide rate was significantly correlated with the type after change, with a correlation coefficient of 0.7. In addition, land-use types associated with human activities, such as cultivated vegetation, were more likely to cause landslide deformation, which can be used to guide local land-use planning.

Landslide Susceptibility Mapping by Fusing Convolutional Neural Networks and Vision Transformer

Landslide susceptibility mapping (LSM) is an important decision basis for regional landslide hazard risk management, territorial spatial planning and landslide decision making. The current convolutional neural network (CNN)-based landslide susceptibility mapping models do not adequately take into account the spatial nature of texture features, and vision transformer (ViT)-based LSM models have high requirements for the amount of training data. In this study, we overcome the shortcomings of CNN and ViT by fusing these two deep learning models (bottleneck transformer network (BoTNet) and convolutional vision transformer network (ConViT)), and the fused model was used to predict the probability of landslide occurrence. First, we integrated historical landslide data and landslide evaluation factors and analysed whether there was covariance in the landslide evaluation factors. Then, the testing accuracy and generalisation ability of the CNN, ViT, BoTNet and ConViT models were compared and analysed. Finally, four landslide susceptibility mapping models were used to predict the probability of landslide occurrence in Pingwu County, Sichuan Province, China. Among them, BoTNet and ConViT had the highest accuracy, both at 87.78%, an improvement of 1.11% compared to a single model, while ConViT had the highest F1-socre at 87.64%, an improvement of 1.28% compared to a single model. The results indicate that the fusion model of CNN and ViT has better LSM performance than the single model. Meanwhile, the evaluation results of this study can be used as one of the basic tools for landslide hazard risk quantification and disaster prevention in Pingwu County.

Using Deep Learning to Formulate the Landslide Rainfall Threshold of the Potential Large-Scale Landslide

The complex and extensive mechanism of landslides and their direct connection to climate change have turned these hazards into critical events on a global scale, which can have significant negative influences on the long-term sustainable development of nations. Taiwan experiences numerous landslides on different scales almost every year. However, Typhoon Morakot (2009), with large-scale landslides that trapped people, demonstrated the importance of an early warning system. The absence of an effective warning system for landslides along with the impossibility of its accurate monitoring highlighted the necessity of landslide rainfall threshold prediction. Accordingly, the prediction of the landslide rainfall threshold as an early warning system could be an effective tool with which to develop an emergency evacuation protocol. The purpose of this study is to present the capability of the deep learning algorithm to determine the distribution of landslide rainfall thresholds in a potential large-scale landslide area and to assess the distribution of recurrence intervals using probability density functions, as well as to assist decision makers in early responses to landslides and reduce the risk of large-scale landslides. Therefore, the algorithm was developed for one of the potential large-scale landslide areas (the Alishan D098 sub-basin), Taiwan, which is classified as a Type II Landslide Priority Area. The historical landslide data, maximum daily rainfall, 11 topographic factors from 2004 to 2017, and the Keras application programming interface (API) python library were used to develop two deep learning models for landslide susceptibility classification and landslide rainfall threshold regression. The predicted result shows the lowest landslide rainfall threshold is located primarily in the northeastern downstream of the Alishan catchment, which poses an extreme risk to the residential area located upstream of the landslide area, particularly if large-scale landslides were to be triggered upstream of Alishan. The landslide rainfall threshold under controlled conditions was estimated at 780 mm/day (20-year recurrence interval), or 820 mm/day (25-year recurrence interval). Since the frequency of extreme rainfall events caused by climate change is expected to rise in the future, the overall landslide rainfall threshold was considered 980 mm/day for the entire area.

Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas

Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 105 m2, the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy.

Expert based landslide susceptibility mapping for energy infrastructure planning

Landslides are a significant problem in the hilly region. Objective of this study is to prepare landslide susceptibility map of Almora district, India using expert weightage technique. The topography characteristics, geological characteristics and drainage characteristics of the region are considered as causative factors of landslides. The weightage to factors and sub-factors is assigned by expert opinion. Geographic information system is used to convert the causative factor maps in layers. The expert weightage are based on the judgment of expert and the historical landslide data. The weighted layers are overlaid in GIS environment and the final output map shows the landslide susceptibility in increasing order. The landslide susceptibility index varies from 7 to 45 in the region. Lower landslide susceptibility index (LSI) shows lower probability of occurrence of landslides and hence higher suitability for development of new infrastructure projects. The landslide susceptibility map can be used by planners and engineers.

An Overview: Relationship of Geological Condition and Rainfall with Landslide Events at East Malaysia

Tropical country like Malaysia is rich with residual soil and nurtured with high rainfall amount on average 2,550 mm per year. From 2009 until 2018, there are many landslide events reported in the news at Ranau, Sabah and Canada Hill Miri, Sarawak that occurred during rainy season and the landslide recurs within same location over the years. The objective of this study is to determine the relationship of landslide events with geological condition and rainfall at Ranau, Sabah and Canada Hill Miri, Sarawak. Historical landslide data were obtained from local news, previous researchers, and local authorities. Integrated review was conducted to meet the objective. In summary, both areas are prone to landslide due to the high average amount rainfall recorded and the geological properties that are susceptible to landslide occurrence namely shale interbedded with sandstone. Sandstone and shale contact are easily accessible by water weaken the contact surface lead to landslides incidents. Besides, Shale classified as highly plastic soil due to high amount of clay. Clay soil depends on its matric suction to sustain its strength towards sliding. Thus, increasing of pore pressure from rain infiltration reduce the matric suction and eventually reduce the shear strength. Ranau is located at seismically active area compare to Miri and other locations in Sabah crossing Lobou-Lobou fault, Mensaban fault and Mesilou fault. Theoretically, slope instability due to earthquake happened because the cementation of soil may be broken and lead to lesser roughness between soil surfaces resulting in reduction of internal friction angle and cohesion of soil.
 HIGHLIGHTS
 
 Rainfall-induced landslides are the common landslides phenomenon worldwide including East Malaysia.
 However, due to the high number of seismic activity, Earthquake-induced landslides should not be neglected as one of slope failure phenomena in East Malaysia
 Combinations of both Rainfall and Earthquake are required to be investigated due to the presence of both landslide triggering factors from historical data at East Malaysia
 
 GRAPHICAL ABSTRACT

GIS-Based Landslide Susceptibility Mapping using Logistic Regression, Instability Index, and Support Vector Machine: Case Study of the Jingshan River, Taiwan

Many techniques have been developed to produce landslide susceptibility maps. However, mapping the spatial distribution of landslide susceptibility involves critical steps. This study evaluated susceptibility to the occurrence of shallow landslides in upstream areas of the Jingshan River, Taiwan, where heavy rainfall has resulted in increased landslide occurrence and reservoir sedimentation. The landslide susceptibility case study was conducted using logistic regression, an instability index method, and support vector machine (SVM). A selection procedure was first developed to identify the factors influencing landslide occurrence. Historical landslide data were used to assess the corresponding parameters of each model. The receiver operating characteristic curve was then used to evaluate the accuracy of model results. The results indicated that the instability index method underestimated landslide susceptibilities in areas near the river. The instability index method was highly affected by the classifications of model factors. Of the models, the SVR model was the most accurate regarding landslide susceptibility in the study area. Employing this framework, governments may implement suitable, cost-effective improvements to structural measures and land-use planning.

Development of the Landslide Susceptibility Map of Attica Region, Greece, Based on the Method of Rock Engineering System

The triggering of slope failures can cause a significant impact on human settlements and infrastructure in cities, coasts, islands and mountains. Therefore, a reliable evaluation of the landslide hazard would help mitigate the effects of such landslides and decrease the relevant risk. The goal of this paper is to develop, for the first time on a regional scale (1:100,000), a landslide susceptibility map for the entire area of the Attica region in Greece. In order to achieve this, a database of slope failures triggered in the Attica Region from 1961 to 2020 was developed and a semi-quantitative heuristic methodology called Rock Engineering System (RES) was applied through an interaction matrix, where ten parameters, selected as controlling factors for the landslide occurrence, were statistically correlated with the spatial distribution of slope failures. The generated model was validated by using historical landslide data, field-verified slope failures and a methodology developed by the Oregon Department of Geology and Mineral Industries, showing a satisfactory correlation between the expected and existing landslide susceptibility level. Having compiled the landslide susceptibility map, studies focusing on landslide risk assessment can be realized in the Attica Region.

Landslide hazard assessment of the Neom promising city, northwestern Saudi Arabia: An integrated approach

Landslides hazard as one of the major environmental threats of the Neom promising city in northwestern Saudi Arabia has been assessed through data integration of several sources including topographic, geologic, and seismologic data sets. Moreover, Landsat imagery and historical landslide data are introduced in assessing landslide susceptibility of the study area. All these data oriented to generate eight thematic layers representing eight landslide conditioning factors. These factors arranged according to their relative importance for landslide susceptibility mapping as follows: slope angle, elevation, curvature, aspect, earthquake density, fault density, drainage density, and lineaments density. These factors were assessed using GIS through a numerical rating scheme for thematic layers. The ranks and weights were given in association with intensive field studies and authors’ experiences. The estimated values of Landslide Potential Index (LPI) varies from 32 up to 107. Then, the resultant landslide susceptibility map classifies the landslide hazards into three relative classes of susceptibility: high, moderate, and low. Furthermore, this map was tested through several field checks and the extracted historical landslides within Neom area as well. This landslide susceptibility map will boost decision-making authority while executing the developing projects throughout the Neom promising area either avoiding strategic facilities or applying corrective geotechnical procedures to minimize the landslide hazardous impact for Neom city.

Utilization of google maps for depicting landslide pattern in Indonesia

The historical landslide data in GIS (Geographic Information System) environment is valuable to estimate pattern in landslides distribution and frequency, which are useful for landslide hazard analysis and mitigation. By using the landslide reports released officially by The National Agency for Disaster Management (BNPB), those non-spatial data need to be converted into the spatial ones. The reports primarily contain location, date of event, impact and triggering factor. This study is exploring Google Maps which is a web mapping service to process the historical landslide data of Indonesia. By preparing historical landslide data in the form of spreadsheet, Google Maps directly can change the whole data into a custom map ‘landslide distribution map’. The attribute can be edited, the map can change interactively, and vice versa. The appearance of the custom map can be styled by a certain column so its statistical information comes up. A landslide distribution map produced in Google Maps can be shared to others and can be exported as a GIS layer for further analysis. This article shows the utilization of facilities provided by Google Maps to prepare and analyse the landslide inventory map.

Landslide Hazard Risk Assessment and Landuse Planning of Mayoon, Hunza, GilgitBaltistan, Pakistan

Gilgit-Baltistan, Pakistan is highly vulnerable and hazard-prone area according to National DisasterManagerment Authority, based on frequency of avalanches, landslides, glacier lake outburst floods, rockfall and flashfloods. These hazards have been quite frequent since 2010, potentially due to changing climatic conditions and uniquetectonic setting resulting in massive destruction, economic loss and human migration. In this study, geospatial techniques(GIS/RS) were used to identify landslide hazard with elements at risk. The resultant maps will be used for betterplanning and resilience of local communities. Landslide area has been marked based on field observations (GPS data).Risk category is ranked high, medium and low based on field observations, geological setting and historical landslidedata. There are six offshoots of MKT crossing parallel to each other along KKH from Chalt to Ahmadabad. Chalt fault iscrossing nearby the Mayoon landslide, which may affect the entire valley. This study identifies landslide as a majorhazard in the area.

Landslide Hazard Risk Assessment and Landuse Planning of Mayoon, Hunza, GilgitBaltistan, Pakistan

Gilgit-Baltistan, Pakistan is highly vulnerable and hazard-prone area according to National DisasterManagerment Authority, based on frequency of avalanches, landslides, glacier lake outburst floods, rockfall and flashfloods. These hazards have been quite frequent since 2010, potentially due to changing climatic conditions and uniquetectonic setting resulting in massive destruction, economic loss and human migration. In this study, geospatial techniques(GIS/RS) were used to identify landslide hazard with elements at risk. The resultant maps will be used for betterplanning and resilience of local communities. Landslide area has been marked based on field observations (GPS data).Risk category is ranked high, medium and low based on field observations, geological setting and historical landslidedata. There are six offshoots of MKT crossing parallel to each other along KKH from Chalt to Ahmadabad. Chalt fault iscrossing nearby the Mayoon landslide, which may affect the entire valley. This study identifies landslide as a majorhazard in the area.

A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling

In this study, the main goal is to compare the predictive capability of Support Vector Machines (SVM) with four Bayesian algorithms namely Naïve Bayes Tree (NBT), Bayes network (BN), Naïve Bayes (NB), Decision Table Naïve Bayes (DTNB) for identifying landslide susceptibility zones in Pauri Garhwal district (India). First, landslide inventory map was built using 1295 historical landslide data, then in total sixteen influencing factors were selected and tested for landslide susceptibility modelling. Performance of the model was evaluated and compared using Statistical based index methods, Area under the Receiver Operating Characteristic (ROC) curve named AUC, and Chi-square method. Analysis results show that that the SVM has the highest prediction capability, followed by the NBT, DTNBT, BN and NB, respectively. Thus, this study confirms that the SVM is one of the benchmark models for the assessment of susceptibility of landslides.

A national assessment of landslide hazard from Outside Party Slopes to the rail network of Great Britain

In recent years, a number of high profile landslide events have caused disruption, derailments or damage to railway infrastructure in Great Britain. A landslide susceptibility model of the entire railway network was created, designed to give a national overview of potential landslide hazard originating from Outside Party Slopes. The current assessment was compiled using Geographic Information System (GIS) techniques and desktop modelling to apply a structured analysis of each buffered Earthwork Inspection 5 Chain ( c . 100 m; EI5C). Data analysed along the network included the BGS GeoSure instability model and newly updated national models for debris flow, earth flow and rock fall, supported by historical landslide data. In order to further focus the Outside Party Slope zone, a buffer of External Natural Geological Influence (BENGI) was created using a 5 m Digital Terrain Model. Landslide susceptibility for each EI5C was categorized using a ‘Classification of Hazards on Outside Party Slopes’ (CHOPS) score; representing the modelled potential for landslide hazard. The outputs were combined as a series of matrices to present the CHOPS and Network Rail Derailment Criticality Band interactions. This research will allow further focused analysis of the network, in order to prioritize and direct future investigation and policy decisions.

Unsupervised landslide risk dependent terrain segmentation on the basis of historical landslide data and geomorphometrical indicators.

Unsupervised landslide risk dependent terrain segmentation on the basis of historical landslide data and geomorphometrical indicators.

Integration of multicriteria evaluation and cellular automata methods for landslide simulation modelling

The representation and modelling of physical and natural systems are mostly implemented using partial differential equations. Physical processes such as flooding, tsunamis, avalanches, earthquakes and landslides undeniably possess complex systems behaviour. Modelling such dynamic phenomena can be adequately addressed by using geocomplexity – complex systems theory and cellular automata (CA). This study develops a novel approach that couples geographic information systems (GIS), multicriteria evaluation (MCE) and CA to simulate shallow landslide flows occurring in urban areas. The landslide susceptibility map produced from the MCE model was used as one input for the CA model. The high-resolution digital elevation model (DEM) is used to calculate topographic variables such as slope gradient, aspect gradient, stream power index, topographic wetness index, and flow direction which were all used in model design. The developed MCE-CA simulation model was tested on historical landslide data in Metro Vancouver, Canada. The spatial extents of the landslide simulations were compared with actual data to test the model simulation outcomes. The developed model has a potential to become useful tool that can aid urban planners and emergency workers in identifying and mitigating threats due to landslides.