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Landslide is one of the most common and severe geological disasters, which significantly endangers people's lives and properties. Therefore, adequate evaluation of landslide susceptibility is an important for disaster control and mitigation. We selected Pengyang County as the study area, and divided the indicators into different state levels by graded area ratio and landslide occurrence frequency distribution. Meanwhile, the levels of each indicator were assigned the unique scores according to the corresponding information values. Given that the issue of subjectivity in single AHP model significantly influence the model performance in landslide susceptibility prediction, a hybrid model, namely variable-weight based weighted information value (VW-AHP-IV) model, was applied in this paper for landslide susceptibility evaluation. The study area was classified into five classes by the natural breakpoint method: very high susceptibility area (8.26%), high susceptibility area (19.78%), medium susceptibility area (29.93%), low susceptibility area (31.57%) and very low susceptibility area (10.46%). In addition, this paper also discussed the influences of precipitation and human activities on landslide occurrence. According to the evaluation results and discussion, the study area was classified into three prevention and control areas: focus prevention and control area, sub-focus prevention and control area, and general prevention and control area. For each area, the corresponding prevention and control suggestions were proposed in order to reduce the occurrence of landslide disasters. [ABSTRACT FROM AUTHOR]

Selecting appropriate mapping units is vital for landslide susceptibility mapping as well as pattern investigation, given that various units-based analyses extensively control the prediction performances. This paper investigated the landslide development through the interpretation and field surveys along the Yunling–Yanjing segment of the Lancang River in southeastern Tibet, and fulfil LSM with the consideration of 15 conditioning factors. Two grid unit methods (single-point and multi-point patterns) and two slope unit methods were comparatively analyzed for model training and mapping of landslide susceptibility via machine learning algorithms. The results suggest that the landslides are preferentially distributed in an elevation range of 2000–4000 m, in a slope range of 20–40°, a local relief range of 1000–2500 m, and southwest-oriented slopes. The data extracted by the multi-point method denotes a higher representation of landslide development features. All models possessed positive prediction ability for landslide susceptibility, and the multi-point method based on grid unit performed the best with the AUC exceeding 0.9. The best-performing models indicated that zones of high and very high susceptibility mainly distributed adjacent to the mainstem and some tributaries of the Lancang River. Furthermore, the distribution of "safety islands" (the slopes less prone to landslides) along National Highway G214 was reasonably illustrated as well, which provides a hazard predictability for such an important transportation corridor along the deeply-incised valley in the Lancang River. This study demonstrates a theoretical basis for the regional disaster prevention and mitigation for human activity, and provides methodological references for landslide susceptibility evaluation in similar mountainous areas over the world. [ABSTRACT FROM AUTHOR]

Accurate assessment of seismic landslides hazard is a prerequisite and foundation for post-disaster relief of earthquakes. An Ms 5.7 earthquake occurring on September 7, 2012, in Yiliang County, Yunnan Province, China, triggered hundreds of landslides. To explore the characteristics of coseismic landslides caused by this moderate-strong earthquake and their significance in predicting seismic landslides regionally, this study uses an artificial visual interpretation method based on a planet image with 5-m resolution to obtain the information of the coseismic landslides and establishes a coseismic landslide database containing data on 232 landslides. Nine influencing factors of landslides were selected for this study: elevation, relative elevation, slope angle, aspect, slope position, distance to river system, distance to faults, strata, and peak ground acceleration. The real probability of coseismic landslide occurrence is calculated by combining the Bayesian probability and logistic regression model. Based on the coseismic landslides, the probabilities of landslide occurrence under different peak ground acceleration are predicted using a logistic regression model. Finally, the model established in this paper is used to calculate the landslide probability of the Ludian Ms 6.5 earthquake that occurred in August 2014, 78.9 km away from the macro-epicenter of the Yiliang earthquake. The probability is verified by the real coseismic landslides of this earthquake, which confirms the reliability of the method presented in this paper. This study proves that the model established according to the seismic landslides triggered by one earthquake has a good effect on the seismic landslides hazard assessment of similar magnitude, and can provide a reference for seismic landslides prediction of moderate-strong earthquakes in this region. [ABSTRACT FROM AUTHOR]

On October 2017, due to continuous rainfall, there was a massive river blockage induced by a landslide in Guang'an Village, Chongqing, China. Long-term monitoring analysis has shown that there remain four strong deformation areas on the slope, which seriously threaten the life and property of nearby residents. In this paper, a granular flow model and an elasto-visco-plasticity model were applied to reproduce and predict the landslide event that hit Guang'an Village. The results showed that the landslide gradually moved along the sliding surface, pushing loose deposits and blocking the Xixi River. The numerical reproduction results of the 2017 event are consistent with the actual slope deformation and failure process and deposit morphology. The simulated maximum depth-averaged velocity of this landslide was approximately 1.89 m/s, and the height of the landslide dam was approximately 10 m. After the landslide occurred in 2017, several large deformation areas appeared in the vicinity of the sliding area, and the right rear side of the sliding mass in area III has the largest deformation volume, accompanied by the most developed surface crack and the most intense deformation. There is a risk that the Xixi river will be blocked again. Therefore, with the same parameter and numerical model, a sliding–pushing–blocking dynamic prediction analysis of the strong deformation area III was conducted. The pushing motion of the mass in this area will reactivate the landslide mass observed in 2017. The maximum depth-averaged velocity of deformation area III was 0.5 m/s, and the maximum depth-averaged velocity of landslide deposition was 0.45 m/s. The length of the blocking dam formed by the mass of deformation area III along river was approximately 780 m, 30 m longer than that in 2017. The predicted height of the landslide dam was 14.5 m, approximately 4.5 m higher than that in 2017. The length of the landslide dam reservoir was predicted to be 2.55 km along the Xixi River, which may submerge the Waping Village. This study supports the landslide hazard prevention, reveals the whole movement process of sliding-pushing-blocking, and provides a new research idea and method for the landslide movement prediction. Hence, this study can serve as a reference for the hazard prevention and mitigation of such chain disasters. [ABSTRACT FROM AUTHOR]

The assessment of earthquake-induced landslide hazards is an important prerequisite for disaster prevention and reduction in tectonic active areas. However, few studies have considered the amplification effect of site and topography on ground motion parameters and made the peak ground acceleration (PGA) correction. Based on Newmark's method, taking Litang County, Sichuan Province, China, as the study area, considering the site amplification effect and topographic amplification effect, this paper carried out the assessment of landslide hazards under the action of occasional earthquakes with an exceedance probability of 10% and rare earthquakes with an exceedance probability of 2%. The results show that the hazard of earthquake-induced landslides is higher in the high slopes of loose rock in the middle of Litang County and the steep rock slopes with large topographic relief in the northeast, and low in the southern plateau and central basins. The site and topographic conditions have a significant effect on the nonlinear amplification of PGA, and the corrected PGA is even magnified by 2–3 times in steep mountains. Compared with the occasional earthquakes, the influence of rare earthquakes on the initiation and movement distance of landslides is remarkably improved. This study can provide a valuable reference for potential earthquake-induced landslide hazard assessment and seismic landslide emergency response in Litang County. [ABSTRACT FROM AUTHOR]

On September 5, 2022, an Ms6.8 earthquake struck Luding County, Sichuan Province, China. Through creating a coseismic landslide prediction model, we obtained the spatial distribution of the triggered geological hazards immediately after the earthquake. Through collecting all available multi-source optical remote sensing images of the earthquake-affected area via UAV and satellite platforms, the exact information of coseismic landslide was achieved by pattern recognition and visual inspection. According to the current results, the Luding earthquake triggered 5336 landslides with a total area of 28.53km2. The spatial distribution of the coseismic landslides is correlated statistically to various seismic, terrain, and geological factors, to evaluate their susceptibility at regional scale and to identify the most typical characteristics of these failures. The results reveal that the coseismic landslides mainly occurred on the sides of the Xianshuihe fault (within 1.2 km) and Dadu River (within 0.5 km) in striped patterns. They are concentrated in the regions with an elevation range of 1000–1800 m, a slope range of 25–55°, and lithologies of acid plutonic rocks, mixed sedimentary rocks, and siliciclastic sedimentary rocks. Besides, the coseismic landslides of the Luding earthquake are smaller in size and shallower than those triggered by the 2008 Wenchuan earthquake and the 2017 Jiuzhaigou earthquake. Rapidly achieving the spatial locations and distribution patterns of the coseismic landslides enables to provide effective support and guidance to emergency rescue, risk mitigation, and reconstruction planning. [ABSTRACT FROM AUTHOR]

Landslides have claimed many lives and caused significant economic losses in recent years. Therefore, assessing the potential risk for landslide hazard prevention and mitigation is vital and the landslide travel distance is a key aspect in this process. In this study, the group method of data handling (GMDH) was used to predict the landslide travel distance. A total of 111 landslide datasets collected from the literature were used to construct and validate the proposed GMDH model. Five parameters were selected as the input parameters, including initial slope angle, body height, average body thickness, the logarithm of source volume, and body aspect ratio. The GMDH model was compared with the gene expression programming (GEP) model as well as four empirical models proposed by other researchers. The comparison results demonstrated that the predicted results of the GMDH model agreed well with the measured values, with a correlation coefficient of 0.9123, which was higher than the GEP model and the four empirical models. It was concluded that the proposed GMDH model had great potential in estimating the landslide travel distance. [ABSTRACT FROM AUTHOR]

Seismic slope stability analysis at regional scale (e.g., landslide hazard mapping, infrastructure slope management) is essential in areas that are susceptible to earthquake. Analytical infinite slope model is well suited and widely used for regional analyses which involve hundreds and thousands of slopes. Use of infinite slope model implicitly assumes shallow translational failure mechanism. However, earthquake can also cause deep rotational failure, which is more destructive and should also be considered. This study aims to develop closed-form solutions that can be efficiently used for seismic slope stability analysis at regional scale considering deep rotational failure mechanism. The existing research is critically reviewed first, and their shortcomings are identified. In contrast to the conventional "two-step" approach, a more efficient and versatile "one-step" approach is proposed in this study. The "one-step" approach can be used to calculate factor of safety for pseudo-static analysis and yield coefficient for displacement-based seismic analysis. To consider the effects of uncertainty from soil properties and seismic loading, the "one-step" approach is further extended for probabilistic analysis and application is demonstrated through a case study. The efficiency and versatility of the proposed "one-step" approach make it well suited for regional seismic slope stability analysis. [ABSTRACT FROM AUTHOR]