Seismic Landslide Susceptibility Research Articles

Seismic landslide susceptibility evaluation model based on historical data and its application to areas with similar environmental settings

Seismic landslide susceptibility evaluation models are usually built on the basis of historical sample data; however, the evaluation results are often unsatisfactory when the environmental settings differ between the historical sample data region and application region. Therefore, similarity between the environmental settings is important for the application of such models. In this paper, a seismic landslide susceptibility evaluation model was first built using data from the 2008 Ms 8.0 Wenchuan earthquake-induced landslide, and the model was then used to evaluate the 2022 Ms 6.8 Luding earthquake area. In addition, the grade of susceptibility is typically represented by the landslide density, which is insufficient for capturing the details of landslides, such as their sizes, frequencies, and spatial distribution patterns. The authors therefore use a large and concentrated landslide as the susceptibility grade for the Luding earthquake area. The test results demonstrate that these two areas have similar background environments. The area under the curve (AUC) value of the receiver operating characteristics (ROC) curve of the evaluation accuracy for the model applied to the Luding earthquake area is 0.889, which indicates relatively high accuracy. Besides, the results also demonstrate that the evaluations are consistent with the disaster situation of the Moxi Platform, Wandong Village, as well as the Dagangshan Hydropower Station area. Therefore, it is reliable to apply the susceptibility evaluation model based on the Wenchuan earthquake data to the Luding earthquake area. These results show that better evaluations can be obtained based on environmental similarity tests between the areas used for historical data modeling and areas to which the models are applied.

Advancing seismic landslide susceptibility modeling: a comparative evaluation of deep learning models through particle swarm optimization

Advancing seismic landslide susceptibility modeling: a comparative evaluation of deep learning models through particle swarm optimization

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

The accuracy of Seismic Landslide Susceptibility Maps (SLSMs) is imperative for the prevention of seismic landslide disasters. This study enhances the precision of SLSMs by integrating nine distinct machine learning methodologies with the GeoDetector version 0.0.4 tool to filter both numerical and physical factors contributing to landslide susceptibility. The dataset comprises 2317 landslide instances triggered by the 2013 Minxian Ms = 6.6 earthquake, from which redundant factors were pruned using the Recursive Feature Elimination technique. Subsequent evaluations of the optimized factors, both individually and in combination, were conducted through Frequency Ratio analysis and Factor Interaction assessment. The study juxtaposes the Area Under the Receiver Operating Characteristic Curve (AUC) and the accuracy of nine machine learning models before and after factor optimization. The findings indicate an increase in AUC from a maximum of 0.989 to 0.992 in the Random Forest model, and an 8.37% increase in AUC for the SVM model, signifying a notable enhancement in the stability across all models. The establishment of the SLSM notably elevated the frequency ratio in high-risk zones from 50.40 to 85.14, underscoring the efficacy of combining machine learning and detector optimization techniques in sustainable practices. This research proposes a universal framework aimed at eliminating redundancy and noise in SLSMs and hazard risk assessments, thereby facilitating sustainable geological disaster risk management.

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 Mapping and Interpretation in the Upper Minjiang River Basin

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

Assessment of seismic landslide susceptibility of bedrock and overburden layer slope based on shaking table tests

The landslide susceptibility assessment (LSA) of bedrock and overburden layer slopes subjected to earthquake action remains a challenging issue. In this study, an LSA method was proposed for the seismic design of slope engineering. First, a frequency response function was used to identify the vibration characteristics of the slope based on signal processing. The characteristic factors and assessment process of the LSA are then proposed. Based on the similarity principle, the method was applied to three groups of shaking table tests with different overburden thicknesses. The results show that with the increase of the thickness of the overburden layer, the landslide susceptibility of the bedrock and overburden layer slope increases, and the critical amplitude of the seismic landslide continuously decreases. The seismic LSA indexes of the model tests calculated by the proposed method show better robustness than those calculated by the transfer coefficient method. Through linear fitting, the critical amplitude of the seismic landslide was predicted more accurately. A limitation of this method is that multiple acceleration-measuring data points are required for earthquakes and the excitation data must be stable. The feasibility of this method need to be verified for complex multistructured slopes in the future.

Seismic landslide susceptibility mapping using machine learning methods: A case study of the 2013 Ms6.6 Min-Zhang earthquake

Seismic landslide susceptibility mapping using machine learning methods: A case study of the 2013 Ms6.6 Min-Zhang earthquake

Regional seismic landslide susceptibility assessment considering the rock mass strength heterogeneity

Rock mass strength parameters are critical when applying the permanent-displacement analysis to landslide susceptibility assessment. Previous studies use limited measured data to assign a constant value for each lithology. Since the rock mass strength is spatially heterogeneous, assigning a constant value to each lithology cannot effectively reflect the spatial heterogeneity of rock mass strength, reducing the reliability of seismic landslide susceptibility assessment. Here we present an analysis of a well-documented seismic landslide inventory and the lithological environment, and develop an empirical model to quantify the rock mass strength heterogeneity. The rock mass strength heterogeneity was subsequently taken as an input of the permanent-displacement analysis to assessing regional seismic landslide susceptibility. Results on two case studies show that considering the rock mass strength heterogeneity would significantly improve seismic landslide susceptibility assessment. The findings of this study are valuable for earthquake emergency rescue and post-earthquake land plans.

Using Persistent Scatterer Interferometry for Post-Earthquake Landslide Susceptibility Mapping in Jiuzhaigou

Earthquakes cause a huge number of landslides and alter the regional landslide risk distribution. As a result, after a significant earthquake, the landslide susceptibility maps (LSMs) must be updated. The study goal was to create seismic landslide susceptibility maps containing landslide causative variables which are adaptable to great changes in susceptibility after the Jiuzhaigou earthquake (MS 7.0) and to perform a rapid update of the LSM after the earthquake by means of the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) technique. We selected the territory of Jiuzhaigou County (southwestern China) as the study region. Jiuzhaigou is a world-renowned natural heritage and tourist area of great human and ecological value. For landslide susceptibility mapping, we examined the applicability of three models (logistic regression, support vector machine, and random forest) for landslide susceptibility mapping and offered a strategy for updating seismic landslide susceptibility maps using DS-InSAR. First, using logistic regression, support vector machine, and random forest techniques, susceptibility models of seismic landslides were built for Jiuzhaigou based on twelve contributing variables. Second, we obtained the best model parameters by means of a Bayesian network and network search, while using five-fold cross-validation to validate the optimized model. According to the receiver operating characteristic curve (ROC), the SVM model and RF model had excellent prediction capability and strong robustness over large areas compared with the LR models. Third, the surface deformation in Jiuzhaigou was calculated using DS-InSAR technology, and the deformation data were adopted to update the landslide susceptibility model using the correction matrix. The correction of deformation data resulted in a susceptibility class transition in 4.87 percent of the research region. According to practical examples, this method of correcting LSMs for the continuous monitoring of surface deformation (DS-InSAR) was effective. Finally, we analyze the reasons for the change in the revised LSM and point out the help of ecological restoration in reducing landslide susceptibility. The results show that the integration of InSAR continuous monitoring not only improved the performance of the LSM model but also adapted it to track the evolution of future landslide susceptibility, including seismic and human activities.

Construction of small sample seismic landslide susceptibility evaluation model based on Transfer Learning: a case study of Jiuzhaigou earthquake

Construction of small sample seismic landslide susceptibility evaluation model based on Transfer Learning: a case study of Jiuzhaigou earthquake

Landslide susceptibility assessment at Kathmandu Kyirong Highway Corridor in pre-quake, co-seismic and post-quake situations

Kathmandu Kyirong Highway (KKH) is one of the most strategic Sino-Nepal highways. Low-cost mitigation measures are common in Nepalese highways, however, they are not even applied sufficiently to control slope instability since the major part of this highway falls still under the category of feeder road, and thus less resources are made available for its maintenance. It is subjected to frequent landslide events in an annual basis, especially during monsoon season. The Gorkha earthquake, 2015 further mobilized substantial hillslope materials and damaged the road in several locations. The aim of this research is to access the dynamic landslide susceptibility considering pre, co and post seismic mass failures. We mapped 5,349 multi-temporal landslides of 15 years (2004–2018), using high resolution satellite images and field data, and grouped them in aforementioned three time periods. Landslide susceptibility was assessed with the application of ‘certainty factor’ (CF). Seventy percent landslides were used for susceptibility modelling and 30% for validation. The obtained results were evaluated by plotting ‘receiver operative characteristic’ (ROC) curves. The CF performed well with the ‘area under curve’ (AUC) 0.820, 0.875 and 0.817 for the success rates, and 0.809, 0.890 and 0.760 for the prediction rates for respective pre, co and post seismic landslide susceptibility. The accuracy for seismic landslide susceptibility was better than pre and post-quake ones. It might be because of the differences on completeness of the landslide inventory, which might have been possibly done better for the single event based co-seismic landslide mapping in comparison with multitemporal inventories in pre and post-quake situations. The results obtained in this study provide insights on dynamic spatial probability of landslide occurrences in the changing condition of triggering agents. This work can be a good contribution to the methodologies for the evaluation of the dynamic landslide hazard and risk, which will further help to design the efficient mitigation measures along the mountain highways.

Seismic landslide susceptibility assessment based on ADASYN-LDA model

Seismic landslide susceptibility (SLS) assessment can be used to estimate the susceptibility of landslides induced by an earthquake, which has great significance for emergency measure making and land use planning to mitigate the landslide hazard and risk. At present, one of the key problems in SLS evaluation is that the sample is extremely unbalanced, that is, the number of landslide units is usually far less than that of non-landslide units, which easily leads to poor prediction performance of SLS model. In addition, when using high-resolution mapping units, the sample size greatly increases, and thus the training time cost is considerable especially for complicated nonlinear artificial intelligence such as the Support Vector Machine and the Neural Networks. In view of this, this paper proposes an SLS model (called ADASYN-LDA) combining ADASYN sample method and linear discriminant analysis (LDA), and evaluates it using 2014 Ludian earthquake event. The results show that the two ADASYN-LDA models are highly efficient in training, and have good prediction performance with the high AUC of 0.8737 and 0.8535 respectively, and with the balanced accuracy of 0.7950 and 0.6174 respectively. Compared with the traditional LDA model without ADASYN method, ADASYN-LDA model improves the results of the SLS assessment affected by the sample imbalance. The further evaluation is needed in other earthquake events, and the further improvement can be made with more landslide conditioning factors and seismic landslide data set.

Distribution Pattern of Coseismic Landslides Triggered by the 2017 Jiuzhaigou Ms 7.0 Earthquake of China: Control of Seismic Landslide Susceptibility

On 8 August 2017 an earthquake (MS7.0) occurred within Jiuzhaigou County, Northern Aba Prefecture, Sichuan Province, China, triggering 4834 landslides with an individual area greater than 7.8 m2 over a more than 400 km2 region. Instead of correlating geological and topographic factors with the coseismic landslide distribution pattern, this study has attempted to reveal the control from seismic landslide susceptibility mapping, which relies on the calculation of critical acceleration values using a simplified Newmark block model. We calculated the average critical acceleration for each cell of the gridded study area (1 km×1 km), which represented the seismic landslide susceptibility of the cell. An index of the potential landslide area generation rate was defined, i.e., the possible landsliding area in each grid cell. In combination with PGA (peak ground acceleration) distribution, we calculated such indexes for each cell to predict the possible landslide hazard under seismic ground shaking. Results show that seismic landslide susceptibility plays an important role in determining the coseismic landslide pattern. The places with high seismic landslide susceptibility tends to host many landslides. Additionally, the areas with high potential landslide area generation rates have high real landslide occurrence rates, consistent with dominant small-medium scale landslides by this earthquake. This approach can aid assessment of seismic landslide hazards at a preliminary stage. Additionally, it forms a foundation for further research, such as the rapid evaluation of post-earthquake landslides and identifying highly impacted areas to help decision makers prioritize disaster relief efforts.

Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event

A landslide susceptibility map, which describes the quantitative relationship between known landslides and control factors, is essential to link the theoretical prediction with practical disaster reduction measures. In this work, the artificial neural network (ANN) model, a promising tool for mapping landslide susceptibility, was adopted to evaluate the coseismic landslide susceptibility affected by the 2013 Minxian, Gansu, China, Mw5.9 earthquake. The evaluation was based on the landslide inventory of this event containing 6479 landslides, and the terrain, geological and seismic factors from database available. During the analyses, two ANN models were applied: considering the entire factors aforementioned (CS model) and excluding seismic factors above (ES model). The success and predictive rates of ANN models and the cumulative percentage curves of susceptibility maps obtained from the models all indicate that the CS model has a relatively better performance than the ES model. However, the comparison of overlapping susceptibility areas suggests that 52.8% of the very high susceptibility areas derived from the CS model coincide with the ES model; and for the very low susceptibility areas, this proportion is 73.55%. Thus, it can be concluded that the assessment based on existing earthquake-induced landslides and the ES model could provide better background information for seismic landslide susceptibility mapping and disaster prevention.

Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau

Seismic landslides are the most harmful natural events in mountainous areas worldwide. They are secondary disasters triggered by earthquakes and can cause a great number of casualties and significant damage to infrastructure. Three areas, Wenchuan, Lushan and Jiuzhaigou, which are highly prone to seismic landslides, were selected for this study. The aim of this study is to compare the prediction accuracy and spatial generalization ability of the logistic regression (LR) and random forest (RF) models in seismic landslide susceptibility mapping. First, using the LR and RF techniques, the susceptibility models of seismic landslides were developed in Wenchuan and Lushan based on thirteen influencing factors. Then, the accuracy of the susceptibility mapping was evaluated by the area under the curve (AUC) values of the receiver operating characteristic (ROC) curves. The results showed that both RF models have excellent prediction capability and strong robustness over large areas compared with the LR models. The AUC values of the RF and LR models were 0.811 vs. 0.946 and 0.905 vs. 0.969 in Wenchuan and Lushan, respectively. Second, we used the seismic landslides of Wenchuan and Lushan to develop the better model (RF), and then applied the developed RF model to produce the landslide susceptibility mapping of Jiuzhaigou County. The prediction accuracy for Jiuzhaigou dropped to 0.704. The results show that the RF model developed in Wenchuan and Lushan has robustness and spatial generalization ability over large areas. However, many landslides that had not been identified by the model, were located in the scenic area of Jiuzhaigou, a popular tourist destination. This suggests that intensive human activities could increase the landslide susceptibility. Thus, we added a new factor related to tourism to produce an improved RF model. The prediction accuracy of the altered model improved significantly, with the AUC value rising from 0.704 to 0.987. Thus, human engineering activities have a significant impact on landslides. Our study indicates that human engineering activities can increase the likelihood of landslides; hence, human engineering activities related to tourism should not be neglected in landslide susceptibility research. Appropriate disaster prevention and mitigation measures should be taken for the twelve geoparks of western China with peak ground acceleration ≥0.2 g.

A method for quick assessment of earthquake-triggered landslide hazards: a case study of the Mw6.1 2014 Ludian, China earthquake

Rapid assessment of the distribution of earthquake-triggered landslides is an important component of effective disaster mitigation. The effort should be based on both seismic landslide susceptibility and the ground shaking intensity, which is usually measured by peak ground acceleration (PGA). In this paper, we address this issue by analyzing data from the Mw6.1 2014 Ludian, China earthquake. The Newmark method of rigid-block modeling was applied to calculate the critical acceleration of slopes in the study area, which serve as measurement of slope stability under seismic load. The assessment of earthquake-triggered landslide hazard was conducted by comparing these critical accelerations with the distribution of known PGA values. The study area was classified into zones of five levels of landslide hazard: high, moderate high, moderate, light, and very light. Comparison shows that the resulting landslide hazard zones agree with the actual distribution of earthquake-triggered landslides. Nearly 70% of landslides are located in areas of high and moderately high hazard, which occupy only 17% of the study region. This paper demonstrates that using PGA, combined with the analysis of seismic landslide susceptibility, allows a reliable assessment of earthquake-triggered landslides hazards. This easy-operation mapping method is expected to be helpful in emergency preparedness planning, as well as in seismic landslide hazard zoning.

What we can learn about slope response to earthquakes from ambient noise analysis: An overview

Earthquake induced slope failures are responsible for a significant amount of life loss and damage, and their effective mitigation requires further advancements in our comprehension of slope behaviour under seismic shaking. One source of uncertainty in seismic landslide susceptibility assessment is the phenomenon of enhanced amplification of ground motion along down slope directions. This implies a strength demand beyond that estimated by standard slope stability analysis. An extensive accelerometer monitoring of slope dynamic response in areas exposed to seismic landslide hazard is unfeasible. An alternative approach can take advantage of recent development of reconnaissance techniques based on the analysis of ambient noise recorded by portable instruments. The most popular technique, known as Nakamura or HVNR method, consists in analysing H/V spectral ratios between Horizontal and Vertical components of Noise Recording, and allows the recognition of site resonance frequencies. The application of HVNR to complex site conditions typical of marginally stable slopes is often difficult and requires the development of “ad hoc” procedures both for acquisition and analysis of noise recording. Tests in different geologic and geomorphic settings show that an analysis of azimuthal variation of spectral ratios can reveal the presence and orientation of directional resonance, whereas the recognition of main resonance frequencies requires a proper selection of signals to be analysed. Efforts to evaluate amplification factors currently rely on numerical simulations, which in turn require S-wave velocity of slope materials. Ambient noise analysis in terms of velocity models can contribute through the inversion of H/V spectral ratios and surface wave velocity dispersion curves derived from the processing of multiple simultaneous noise recordings. However these applications require a correct identification of the nature of surface waves present in the noise recording.

Critical acceleration as a criterion in seismic landslide susceptibility assessment

The 2008 Wenchuan earthquake in China triggered approximately 200,000 landslides. This study examined a detailed landslide inventory for the event. Based on Newmark's method, the correlation between critical acceleration and landslide occurrence was analyzed for the Beichuan region in Sichuan Province, where various kinds of geohazards occurred due to the earthquake. The results indicate that critical acceleration is a good and reliable criterion to assess slope stability, and that slope gradient and material component are important factors influencing landslide occurrences. It was found that external forces behave differently in different directions. Critical acceleration in horizontal direction is more important for assessing the stability of steeper slopes. This knowledge will help in the understanding of the mechanism of earthquake-triggered landslides and facilitate the combined use of critical acceleration and landslide distribution maps for determining peak ground acceleration in a region without abundant seismic instrument records.

Susceptibility regional zonation of earthquake-induced landslides in Campania, Southern Italy

In this paper, we present a GIS-based method for regional zoning of seismic-induced landslide susceptibility and show its application to the territory of the Campania region, Southern Italy. The method employs only three factors that we believe are most significant in the susceptibility assessment: the type of outcropping rock/soil, the slope angle, and the MCS intensity. Each of the three parameters is quantified in terms of relative weight expressed as indices, and the resulting Seismic Landslide Susceptibility index of an area is given by the average of the indices of the first two factors multiplied by the index of the third factor. The result of this susceptibility zonation applied to Campania shows a good agreement between the distribution of the historical earthquake-triggered landslides and the highly susceptible zones.

Seismically-induced landslide susceptibility evaluation: Application of a new procedure to the island of Ischia, Campania Region, Southern Italy

In this paper we present an approach for evaluating landslide susceptibility in seismic areas. It is known that earthquake-induced landslide susceptibility is related to several, often interplaying, factors. Nevertheless, an effective grade-2 zonation should be characterized by a good balance between simplicity, quickness and reliability. The GIS-based procedure we present employs only three factors that we believe are the most significant in this susceptibility assessment: the type of outcropping rocks/soils, the slope angle and the MCS intensity. The local annual precipitation, certainly an essential factor, is considered here as a parameter whose seasonal pattern is constant in time and space. Each of the three parameters is expressed as a Significance percentage and the resulting Seismic Landslide Susceptibility level of an area is given by the average of the significances of the first two factors multiplied by the significance of the third factor. The procedure was set and tested on the volcanic island of Ischia (southern Italy), which was affected by several historical earthquake-induced landslides. The results of this susceptibility zonation test at Ischia show a very good match between the distribution of the sources of historical landslides and the areas we identified as the most susceptible ones.