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The evaluation of landslide runout behavior plays a vital role in the risk assessment of landslides. In this study, the runout behavior of a landslide that occurred on December 10, 2019, in the Three Gorges Reservoir region, China, known as the Kamenziwan landslide, is studied with the discrete element method. A field survey is first conducted to study the geological characteristics and failure mechanism of this landslide. The survey results indicate that this landslide is possibly caused by the long-term fluctuation of the reservoir water level. Then, a three-dimensional (3-D) numerical model of this landslide is constructed on the basis of the terrain data obtained before and after the landslide occurrence. The critical input parameters to the numerical model, in terms of the contact micro-parameters among particles, are calibrated with the machine learning method and uniaxial compression tests. To depict the effectiveness of the built numerical model, the simulation results are compared with the video snapshots taken by a local resident. Finally, the runout behavior and the energy transformation of the landslide are analyzed based on the validated numerical model. [ABSTRACT FROM AUTHOR]

• CHIRPS outperforms the interpolated gauge-based precipitation in landslide prediction. • Urban areas experience lower landslide-trigger precipitation and soil moisture thresholds. • Dynamic urbanization increases the probability of landslides. • Roads and impervious proportions are the primary drivers of landslides in urban areas. Landslides pose a formidable natural hazard. Accurate risk assessment of landslides triggered by precipitation heavily relies on hydrometeorological factors, specifically precipitation and soil moisture. However, the insufficient ground-based observations and the coarse spatio-temporal resolution hinder the performance of landslide prediction. It is not clear what hydrometeorological thresholds and triggering mechanisms are more likely to trigger landslides in China, particularly in the context of rapid urbanization. To address these questions, this study investigated 1504 shallow landslide events in Chinese urban and non-urban areas from 2007 to 2019. It utilized daily 1 km soil moisture at various depths (20–100 cm) and multi-source precipitation datasets, including gauge-based gridded dataset, in conjunction with three multi-source fusion precipitation products (Multi-Source Weighted-Ensemble Precipitation − MSWEP, the Climate Hazards Group InfraRed Precipitation with Station dataset − CHIRPS, and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement − GPM-IMERG), along with dynamic urban impervious area datasets. It aims to determine the optimal multi-source precipitation predictor, the critical soil moisture depth that triggers landslides, and to establish the hydrometeorological thresholds for landslides. Additionally, the impact of urbanization on landslide occurrences was estimated by comparing antecedent precipitation accumulation, soil moisture, and impervious surface ratio dynamics between urban and non-urban areas. The results indicated that a combination of 2-day cumulative CHIRPS precipitation and 100 cm soil moisture provided the most accurate predictions for landslides in urban regions of China (accuracy = 88.5 %), outperforming interpolated ground-based observations and other fusion products. Specifically, landslides become more prone when antecedent cumulative rainfall surpasses 97.42 mm in 2 days and soil moisture exceeds 39.6 % m3/m3 saturation in China. Urban areas experienced high antecedent precipitation levels, lower precipitation (64.40 mm) threshold and soil moisture threshold (38.9 %), and shorter durations at landslide sites compared to non-urban areas (71.90 mm, 41.4 %, and 7 days, respectively). The process of urbanization is observed to decrease soil moisture levels while concurrently elevating rainfall amounts. This phenomenon, combined with anthropogenic activities, including distance from roads and urban impervious surface expansion, contributes to 44.6 % of the causes of landslides by reducing slope stability and increasing the presence of loose material. These findings have implications for landslide warnings in urban areas with limited measurements and contribute to understanding urbanization's impact on landslide risks in developing nations. [ABSTRACT FROM AUTHOR]

The widely distributed loess–mudstone strata structure in the Loess Plateau of China is one of the fundamental reasons for the susceptibility and group occurrence of seismic landslides in the area. This study took the loess landslides induced by the Haiyuan earthquake as a case study. Through extensive field investigations, the loess cover thickness, mudstone dip angle, slope height, and slope angle were chosen as the influencing factors. Together with mathematical statistics and geotechnical tests, two models, one of a loess–mudstone slope and the other of a pure loess slope, were constructed. Their seismic stabilities under different slope angles and slope heights were compared to analyze the influence of the underlying mudstone strata on the loess slope seismic stability. Meanwhile, a sensitivity analysis of these strata factors affecting the seismic stability of the loess–mudstone slope was carried out based on the orthogonal test. The results showed that ignoring the underlying mudstone strata would lead to smaller calculation results than the actual situation. The sensitivity influence of the factors on the loess–mudstone slope seismic stability comprised the slope angle, loess cover thickness, slope height, and mudstone dip angle from high to low. [ABSTRACT FROM AUTHOR]

Economic and social development, the state of the environment and a propensity for disasters are closely intertwined. Therefore, environmental policy integration (EPI) across development and disaster management (DM) policies and plans is important. Pakistan as a country is highly vulnerable to climate-induced environmental changes and associated disasters. In this paper, the extent to which its national environment and climate change policy, disaster risk reduction (DRR) policy as well as disaster management (DM) plans are aligned is established, based on a review of government documents and expert opinions. In this context, a particular emphasis is put on China's Belt and Road Initiative (BRI) that led the China–Pakistan Economic Corridor Plan (CPEC; 2017–2030). While environmental assessment (EA) is currently not conducted for any DM policies and plans, DM and EA are well integrated into provincial environmental protection acts, in national as well as most provincial DM plans. It is concluded that a regulatory framework to guide EPI in DM for BRI and CPEC projects is needed. [ABSTRACT FROM AUTHOR]

Accurately assessing the susceptibility of debris flow disasters is of great significance for reducing the cost of disaster prevention and mitigation, as well as disaster losses. Machine learning (ML) models have been widely used in the susceptibility assessment of debris flow disasters. However, these models often have randomness in the selection of non-disaster data, which can lead to redundant information and poor applicability and accuracy of susceptibility evaluation results. To address this issue, this paper focuses on debris flow disasters in Yongji County, Jilin Province, China; optimizes the sampling method of non-disaster datasets in machine learning susceptibility assessment; and proposes a susceptibility prediction model that couples information value (IV) with artificial neural network (ANN) and logistic regression (LR) models. A debris flow disaster susceptibility distribution map with higher accuracy was drawn based on this model. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), information gain ratio (IGR), and typical disaster point verification methods. The results show that the rainfall and topography were found to be decisive factors in the occurrence of debris flow disasters, and the IV-ANN model established in this study had the highest accuracy (AUC = 0.968). Compared to traditional machine learning models, the coupling model produced an increase in economic benefit of about 25% while reducing the average disaster prevention and control investment cost by about 8%. Based on model's susceptibility map, this paper proposes practical disaster prevention and control suggestions that promote sustainable development in the region, such as establishing monitoring systems and information platforms to aid disaster management. [ABSTRACT FROM AUTHOR]

Large earthquakes not only directly damage buildings but also trigger debris flows, which cause secondary damage to buildings, forming a more destructive earthquake-debris flow disaster chain. A quantitative assessment of building vulnerability is essential for damage assessment after a disaster and for pre-disaster prevention. Using mechanical analysis based on pushover, a physical vulnerability assessment model of buildings in the earthquake-debris flow disaster chain is proposed to assess the vulnerability of buildings in Beichuan County, China. Based on the specific sequence of events in the earthquake-debris flow disaster chain, the seismic vulnerability of buildings is 79%, the flow impact and burial vulnerabilities of damaged buildings to debris flow are 92% and 28% respectively, and the holistic vulnerability of buildings under the disaster chain is 57%. By comparing different vulnerability assessment methods, we observed that the physical vulnerability of buildings under the disaster chain process is not equal to the statistical summation of the vulnerabilities to independent hazards, which implies that the structural properties and vulnerability of buildings have changed during the disaster chain process. Our results provide an integrated explanation of building vulnerability, which is essential for understanding building vulnerability in earthquake-debris flow disaster chain and building vulnerability under other disaster chains. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) is essential for the spatial prediction of landslides and risk prevention. Physically based LSM models are confined by oversimplifications of physical processes and limited information about soil properties. Data-driven LSM models may give reliable results only when the training and the testing data have high similarity, and application in regions with different geological conditions is often inapplicable. This paper proposes four hybrid data-driven and physical models and compares these models in terms of cross-regional generalization ability and prediction uncertainty. The effects of physical module performance on the hybrid model are analyzed. For the physical modules of the four hybrid models, two-dimensional (2D) physically based models, TRIGRS and the infinite-slope stability models (ISSMs), and the three-dimensional (3D) physically based model, Scoops3D, are adopted. The data-driven modules all adopt the convolutional neural network (CNN) model. Two towns in the Three-Gorge Reservoir area of China are used as the training and testing areas. The results show that all hybrid models have better generalization ability than using the data-driven module exclusively. The prediction uncertainty is significantly reduced by pre-selecting training samples using the physical module. The optimal hybrid model is the one that integrates CNN and ISSM (under the saturated condition). It is then applied to a new region (Wushan County) to further validate the generalization ability. It can make accurate predictions without calibrating the trained model using new data from the validation area. Finally, the effectiveness of model averaging for improving the prediction performance is verified. Using model averaging, the AUC value in the validation area yields 0.834, which is even higher than the original realizations, with AUC values ranging from 0.683 to 0.817. Therefore, the optimal hybrid model can be directly used for LSM in the Three-Gorge Reservoir, and the findings can provide valuable guidance for generalization ability improvement and prediction uncertainty reduction of LSM models in other countries and regions. [ABSTRACT FROM AUTHOR]

Landslides frequently disrupt roadway networks in mountainous regions worldwide. Because of the relatively long roadway extension and low roadway density in mountainous regions, the occurrence of a landslide hazard along a local road segment will cause traffic paralysis on the individual roadway and will further impact regional roadway network accessibility. This paper establishes an integrative risk assessment framework based on risk theory and complex network theory to combine the results of landslide susceptibility mapping along roadways and impact assessment on the roadway network. Through an analysis of the relationship between various geo-environmental conditioning factors and historical landslides along roadways, the support vector machine (SVM) model is used to assess landslide susceptibility across the regional roadway network. Both topological connectivity of the entire roadway network and transport accessibility between local residents are considered in the impact assessment on roadway networks. To illustrate the effectiveness of the proposed risk assessment framework, a case study of the roadway network in Fengjie County, China, which is prone to landslide occurrence, is conducted. The resulting landslide risk heatmap of Fengjie County's roadway network is generated using ArcGIS software. The most critical road segments are identified as being highly susceptible to landslides, and if they are disrupted, the entire roadway network will suffer significant performance loss. The results can support adaptive strategies for landslide mitigation, preparedness, and emergency response services, as well as improve roadway plans to reduce exposure and associated consequences by adding new links to the existing roadway network. [ABSTRACT FROM AUTHOR]

Pakistan has seen a burst of infrastructure development recently due to the increased connection between Asia and East Europe. The China–Pakistan Economic Corridor is a project between China and Pakistan aimed to improve the regional infrastructure that would ultimately enhance the connection between Asia and Eastern Europe. However, the active tectonics of Pakistan could put this infrastructure at risk if it is not built to the highest hazard prevention standard. This study reports the ground motion hazard by using the probabilistic seismic hazard assessment approach and the areal seismic source model. The seismic hazard maps of the China–Pakistan Economic Corridor in Pakistan are derived using the Cornell–McGuire (1968–1976) approach, which takes into account all earthquakes (25AD-2020) that occurred in Pakistan and nearby regions, the newest ground motion prediction equations, and an updated seismotectonic source model of Pakistan. The final ground motion intensities are attained as peak ground acceleration and 5% damped spectral acceleration at T = 0.2 s and 1.0 s for 475- and 2475-year return periods (estimated for bedrock site conditions). The results are displayed as color-coded maps that represent the amplitude deviation of ground motion. From the spatial evaluation of the maps, a peak ground acceleration value of 0.40–0.52 g for the 475-year return period and a spectral acceleration (0.2 s) value of 1.66–2.13 g for 2475-year return period are mostly observed on the northern and western routes. The central and eastern routes are mostly characterized by a peak ground acceleration value of 0.22–0.24 g for the 475-year return period and a spectral acceleration (0.2 s) value of 0.95–1.13 g due to diffused seismicity and lower number of faults in this region. The ground motion intensity values obtained in this study can be utilized for the seismic design of all kinds of infrastructure and bridges along the CPEC routes in accordance with the Building Code of Pakistan, the International Building codes, and the load and resistance factor design codes published by American Association of the State Highway and Transportation Officials. [ABSTRACT FROM AUTHOR]

Many counties in southwestern China were devastated by the Wenchuan earthquake, and this earthquake also induced several geological hazards. Therefore, evaluation of the eco-geological environment in the disaster area is essential for county restoration and ecological remediation. This research considers Pingwu County, which is located 89 km from the epicenter of the Wenchuan earthquake, as the study area to evaluate the eco-geological environment carrying capacity. First, three evaluation index layers are built for an eco-geological environment carrying capacity model after the earthquake, including the geological, ecological and social environments. Then, the evolutionary algorithm is used to modify the analytic hierarchy process (AHP). By optimizing the consistency ratio of the AHP, the most consistent evaluation matrix can be obtained. Finally, we calculate the eco-geological environment carrying capacity in Pingwu via the best consistent evaluation matrix and obtain a hierarchical map of the eco-geological environment carrying capacity through grid rasterization. The results indicate that the current eco-geological environment carrying capacity in Pingwu County mainly contains two states, equilibrium and surplus states. According to the hierarchical map, this study suggests that focusing on ecological protection and disaster monitoring in the eastern and northern areas of Pingwu, large infrastructures should be built in the central and southern areas, and the land use capability should be further improved. Moreover, construction in the central area of Pingwu should be optimized, the scale of regional construction should be controlled, and the original ecological species in the natural protection area should be maintained. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment (LSA) is a significant part of landslide research, which plays an important role in preventing landslide disasters. It has gained an increasing attention in both the academic and practice fields for the past two decades. However, there have been few bibliometric analyses on this topic, although bibliometric analysis can inspire future researchers by exploring the overall characteristics of the published literature. This article aims at collecting and analyzing the information of the abstracts, authors, institutions, countries, journals, funds, and keywords of the recent 4,732 papers published from 1999 to 2021 in Web of Science (www.webofscience.com). In particular, latent Dirichlet allocation (LDA), a machine learning and text analysis method, is utilized to analyze the abstract of each article to identify the hottest research topics related to LSA. The results revealed that: (1) The amount of annual publications related to LSA generally shows an increasing trend, which accounts for about 22% of the total landslide publications in 2021; (2) The author of Pradhan B, the institution of the Chinese Academy of Sciences, the country of China, the journal of Natural Hazards and the funding agency of the National Natural Science Foundation of China, are the productive performers in each aspect of LSA; and (3) Machine learning methods have gained a rapid increase in LSA in recent five years, which have become the most popular research topic. [ABSTRACT FROM AUTHOR]

Flash flood is one of the most devastating types of disasters. Roads are linear strip structures exposed to the natural environment and they are vulnerable to damage when flash flood occurs. Thus, an accurate assessment of flash flood vulnerability for roads is mandatory for the improvement of transportation flash flood forecasting and warnings. In this study, we constructed an assessment model for assessing the flash flood vulnerability of roads based on their exposure and disaster reduction capability. Then, considering the mechanism of flash floods acting on the road, we preliminarily established an index system with analytic hierarchy process (AHP), and used a support vector machine (SVM) to aggregate the indices. The use of SVM avoids the subjectivity of vulnerability evaluation. The Assessment results showed that 4.29% of the land area has high or extremely high vulnerability. The moderate vulnerability areas accounted for 5.19% of the land area. The low and extremely low vulnerability areas accounted for 15.81% of the land area; and the land area with no roads accounted for 43.94%. The suburban areas with dense roads in the southeast and the areas near the main roads in the northwest, had high and extremely high vulnerability. The low and extremely low vulnerability areas are mainly distributed in the areas with sparse roads and close to cities. The results of this study satisfy the need for the high precision and large-scale evaluation of flash flood vulnerability of roads. Based on the results of the vulnerability assessment, different disaster prevention and reduction measures should be taken in the northwest and southeast. The northwestern region should attach importance to the construction of roads and improve the disaster prevention and reduction system; while the southeastern region should plan layout and construction of new roads rationally and enhance the disaster resistance of existing roads. [ABSTRACT FROM AUTHOR]