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As part of the Reimbursable Advisory Services on Accelerating Resilience to Disaster Risks, the World Bank worked with the Interior Ministry of Bulgaria to develop a proposal for the National Disaster Risk Profile of Bulgaria. The purpose of this document, which is the fourth technical annex to the proposal for the National Disaster Risk Profile, is to provide particular conclusions, information, and techniques that were utilised to evalu-ate the risk of landslides in Bulgaria. For the first time at the national level, landslide risk assessment procedures based on ISO/IEC 31010:2019 Risk assessment techniques are being developed and applied in Bulgaria. The five primary categories in which the results are presented are physical safety, economic security, social well-being, environmental security, and security of tangible assets and critical infrastructure. The uncertainty (confidence) parameter is utilised, and it is subdivided into the following three basic categories: low uncertainty, medium uncertainty, and high uncertainty. The produced results demonstrate consistent applicability to empirical data analysis and real-world situations.

Currently, many models are extensively employed for landslide displacement forecast; however, the potential impact of structural parameters on the accuracy of model predictions remains inadequately addressed. Additionally, the optimization algorithm commonly utilized in the realm of landslide displacement forecast still faces certain challenges, including a restricted search area and a propensity to converge on local optimal solutions. In this study, a new comprehensive prediction model is proposed. To begin with, the conventional Aquila optimizer algorithm (AO) has undergone improvement in three key areas: population initialization in Tent chaotic mapping, global optimal individual update on adaptive weight factor, and population update based on opposition-based differential evolution. The improved Aquila optimizer algorithm (IAO) and AO have been evaluated through benchmark function comparisons to verify the effectiveness of three improvement strategies. The results demonstrate the effectiveness of it, especially in terms of its enhanced convergence speed, optimization accuracy, and robustness. This led to the utilization of IAO for parameter optimization of variational mode decomposition (VMD) and bidirectional long short-term memory neural network (BiLSTM), ultimately constructing the IVMD-IAO-BiLSTM prediction model. Then, taking the Qili landslide in Zhejiang Province of China as an example, combined with two years’ worth of independent monitoring data of rainfall, groundwater level, and surface displacement, the displacement series is predicted based on IVMD-IAO-BiLSTM. Under the same prediction conditions, it is compared with the multi-factor-multi-scale model proposed by Xiong C and ten commonly used prediction models. The results validated the superiority of the IVMD-IAO-BiLSTM model, which had the lowest root mean square error and mean absolute error among all prediction results at 0.14 and 0.09, respectively. Therefore, IVMD-IAO-BiLSTM effectively harnesses the enhancing effect of structural parameters, providing a new approach for early warning and risk assessment of landslides with its notable prediction accuracy and performance. [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]

Regional-scale swarms of subparallel linear topographic features, known as lineament domains, are a common feature of planetary surfaces. Lineament domains are superficial manifestations of the crustal stress field trajectory. Notably, one of the effects of active tectonics is seismicity. Italy is one of the most seismically active regions in the Mediterranean, with many destructive earthquakes that have occurred in past centuries. Here, we assess the seismic meaning of the main lineament domain in the tectonically active region of Central Italy. We describe the use of an automated analysis of satellite imagery coupled with spatial grid analysis to identify three lineament domains of the Central Apennines. Spatial and azimuthal comparisons of the main lineament domain (i.e., the Apennine Domain), with the known locations of earthquakes (moment magnitude of Mw > 5.5) that occurred during the past century, revealed the most seismically active tectonic areas and their spatial distributions. Further, we present a conceptual seismo-geodynamic model for the Central Apennines, which is characterized by regional arching and explains the presence of an extensional tectonic regime in the upper crustal layer of the active Apennines fold-and-thrust belt. [ABSTRACT FROM AUTHOR]

The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. These models' performances are limited as landslide databases used in developing them often miss crucial information, e.g., underlying movement types. This study introduces a method of discerning landslide movements, such as slides, flows, and falls, by analyzing landslides' 3D shapes. By examining landslide topological properties, we discover distinct patterns in their morphology, indicating different movements including complex ones with multiple coupled movements. We achieve 80-94% accuracy by applying topological properties in identifying landslide movements across diverse geographical and climatic regions, including Italy, the US Pacific Northwest, Denmark, Turkey, and Wenchuan in China. Furthermore, we demonstrate a real-world application on undocumented datasets from Wenchuan. Our work introduces a paradigm for studying landslide shapes to understand their underlying movements through the lens of landslide topology, which could aid landslide predictive models and risk evaluations. This study analyzes the 3d shapes of landslides and introduces a method to discern landslide movements, such as slides, flows and falls. [ABSTRACT FROM AUTHOR]

Shallow landslides pose a widely growing hazard and risk, globally and particularly in Mediterranean areas. The implementation of adequate adaptation and mitigation measures necessarily requires the development of practical and affordable methodologies and technologies for assessing the shallow landslides hazard and its territorial impact. The assessment of shallow landslide hazard maps involves two different and sequential steps: the susceptibility and the runout analysis, respectively, aimed at the identification of the initiation and the propagation areas. This paper describes the application in the Giampilieri and Briga Villages area (Sicily, Italy) of a shallow landslide risk process at a basin scale with an innovative approach in the runout assessment segment. The runout analysis was conducted using specific GIS tools employing an empirical–geometric approach at a basin scale. The exposure and vulnerability values of the elements at risk were assigned using a qualitative and semi-quantitative approach, respectively. The results highlight the effectiveness of the procedure in producing consistent runout hazard and risk assessments in the valley areas where the more important and vulnerable exposed elements are located. This study contributes to addressing the public administration demand for valuable and user-friendly tools to manage and drive regional planning. [ABSTRACT FROM AUTHOR]

Landslides represent geological hazards wherein a part of a slope loses its static equilibrium and initiates movement. Once this movement begins, it becomes crucial to evaluate the land-slide runout distance (LRD). Currently, there exist numerous tools for estimating LRD, among which geometrical approximations stand as one of the most popular. These empirical models are particularly useful for wide-scale studies, aiding in the scale-down of the problem by identifying the critical areas. This study examines the application of geometrical approximations in the Colombian north–east Andean region. Within this area, a sampling of 49 was conducted using photogrammetric techniques, enabling the morphometrical characterization of each study unit. The results showcase the relationship between geometrical characteristics and LRD in the studied area, considering both land use and geomorphological settings. By exploiting these relationships, the study compares the estimation of LRD using various empirical models, many of which are already employed by practitioners within the studied region. For instance, the relationships in literature display a relative error in the estimation ranging around −50% and 100%. Furthermore, this research proposes new relationships for estimating LRD, enhancing the error estimations in a range between 0% and 50%, highlighting both the advantages and limitations of such empirical estimations. Consequently, it contributes new data to enrich the field of LRD studies. [ABSTRACT FROM AUTHOR]

Landslides are widespread phenomena that occur in any terrestrial area with slopes, causing massive property damage and, in the worst-case scenario, loss of human life. This propensity to suffer losses is particularly high for developing countries due to their urban development, population growth and drastic land use changes. Social and economic consequences of landslides can be reduced through detailed planning and management strategies, which can be aided by risk analysis. In this study, we performed a detailed quantitative risk analysis for landslides in the whole of Central Asia (4 000 000 km2). Landslide-induced risk was computed, using a 200 m spatial resolution, in terms of exposed population and expected economic losses to buildings and linear infrastructures (roads and railways). The purpose of our study is to produce the first regional-scale landslide risk assessment for Central Asia in order to inform regional-scale risk mitigation strategies, and it represents an advanced step in the landslide risk analysis for extremely broad areas. [ABSTRACT FROM AUTHOR]

Earthquake-triggered landslides (ETLs) are characterized by their extensive occurrences, having wide distributions. The conventional human–computer interaction extraction method is often time-consuming and labor-intensive, failing to meet the demands of disaster emergency response. There is a pressing need for a swift detection of ETLs. In this study, we introduce an ETLs extraction method (MAST) combining morphological analysis edge recognition with a Swin-Transformer (SWT) deep learning model, which is specifically designed for landslide extraction. The MAST model adopts a hierarchical construction approach akin to convolution neural networks, aiding in tasks such as target detection and semantic segmentation. To enhance the accuracy of landslide edge extraction, we incorporate an edge recognition algorithm based on the morphological analysis into the MAST model. This algorithm leverages morphological operations to extract the features of landslide boundaries. It effectively addresses issues such as discretization and irregularization of the extracted landslide boundaries, leading to more precise delineation of landslide boundaries. Drawing on UAV data collected from Wan Dong Village, De Tou Town, Sichuan Luding, China, during the 2022 Ms 6.8 Luding Earthquake, we conducted automated extraction of ETLs utilizing the MAST model. Experimental results demonstrate the superior performance of the MAST model compared to the traditional full convolution neural network (FCN) model and normal SWT model. The MAST model exhibits enhanced value in landslide extraction. Notably, it demonstrates a significant advantage in boundary extraction. Employing the Boundary IoU metric to evaluate the accuracy of ETLs extraction, the MAST model outperforms the SWT and FCN models at various distances. [ABSTRACT FROM AUTHOR]

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