High-risk Areas Research Articles

Spatiotemporal dynamics of landslide susceptibility under future climate change and land use scenarios

Abstract Mountainous landslides are expected to worsen due to environmental changes, yet few studies have quantified their future risks. To address this gap, we conducted a comprehensive analysis of the eastern Hindukush region of Pakistan. A geospatial database was developed, and logistic regression was employed to evaluate baseline landslide susceptibility for 2020. Using the latest CMIP6 models under three Shared Socioeconomic Pathways (SSPs) and the CA-Markov model, we projected future rainfall and land use/land cover patterns for 2040, 2070, and 2100, respectively. Our results reveal significant changes in future rainfall and land use patterns, particularly in the long-term future (2070 and 2100). Future landslide susceptibility was then predicted based on these projections. By 2100, high-risk landslide areas are expected to increase substantially under all SSP scenarios, with the largest increases observed under SSP5-8.5 (56.52%), SSP2-4.5 (53.55%), and SSP1-2.6 (22.45%). By 2070, high-risk areas will rise by 43.08% (SSP1-2.6), 40.88% (SSP2-4.5), and 12.60% (SSP5-8.5). However, by 2040, the changes in high-risk areas are minimal compared to the baseline, with increases of 9.45% (SSP1-2.6), 1.69% (SSP2-4.5), and 7.63% (SSP5-8.5). These findings provide crucial insights into the relationship between environmental changes and landslide risks and support the development of climate risk mitigation, land use planning, and disaster management strategies for mountainous regions.

Spatial modeling and ecological suitability of Ebola virus disease in Africa.

This paper looks into the MaxEnt model in a trial to comprehend the ecological and environmental conditions that propagate and drive the spread of Ebola Virus Disease in Africa. We use the MaxEnt model to assess risk determinants associated with the occurrence and distribution of EVD, taking into account non-correlated variables such as neighborhood mean temperature, rainfall, and human population density. Our findings indicate that among the factors that significantly shape the geographical distribution of EVD risk are human population density, annual rainfall, temperature variability, and seasonality. The model used is both reliable and accurate (the average value for training AUC was 0.987); it can be used as a valuable approach for the prediction of infectious disease outbreaks. High-risk areas are primarily identified in the western and central regions of Africa, with some of the others in the east also vulnerable. This further calls for specified public health interventions and enhanced surveillance in specified hotspots, contributing to global efforts to predict and mitigate risks associated with EVD outbreaks more adequately. The findings further support that it remains imperative to conduct additional research, including socio-economic and cultural variables, to enhance the understanding of how environmental factors contribute to the emergence and transmission of Ebola.

The effect of posture regulation training on musculoskeletal disorders, fatigue level and job performance in intensive care nurses

BackgroundIntensive care units (ICUs) are one of the high-risk working areas in terms of musculoskeletal disability and ergonomic risks including the environment and posture factors. Correct posture technique is often ignored by nurses working in these units. This study was conducted to determine the effect of posture regulation training on work-related musculoskeletal disorders, fatigue level and job performance in nurses working in ICUs.MethodsThis quasi-experimental study with one-group pretest-posttest design included 64 intensive care nurses. The nurses received posture regulation training in three different sessions. The post-test was administered four months after the posture regulation training.ResultsThe nurses reported to frequently have aches, pains and discomfort in the neck, upper back and lumbar regions. After the posture regulation training, their level of pain, ache and discomfort in the neck, right and left shoulder, upper back, lower back and right/left foot areas decreased significantly (p < 0.05). The posture regulation training reduced the levels of behavior/severity and affect, which are sub-dimensions of fatigue, and increased the level of contribution to work, which is a sub-dimension of job performance (p < 0.05).ConclusionThe posture regulation training decreased the level of symptoms in the neck, shoulder, upper back, lower back and foot regions of intensive care nurses and partially improved their fatigue level and job performance. Therefore, posture regulation training should be added to in-service training programs and permanent measures should be taken for ergonomic risks in ICUs.

Ecological features and insecticide resistance of Aedes albopictus in Xi'an, a high-risk dengue transmission area in China.

Dengue, a mosquito-borne viral disease, has occurred in many cities in China, and it tends to spread to higher latitudes (Huang et al., 2023). Xi'an, situated in central-west China, has witnessed an increase imported cases in the past few years, raising concerns of local dengue transmission. It is crucial to investigate the population density of Aedes albopictus and its insecticides resistance to enhance early warning of dengue fever. Eight sampling sites in eight counties (YT, BL, WY, CH, YL, LN, LT, ZZ) of Xi'an city were surveyed by larval dipping and human-baited double net trap biweekly from June 2021 to September 2022. The Breteau Index (BI, number of positive containers per 100 houses) and Container Index (CI, the percentage of containers containing larvae or pupae) were used to assess larval density, and the human-baited double net trap (HDN, the number of Ae. albopictus females collected per person per hour) to indicate human bating rate (HBR). Meanwhile, the association between the meteorological factors and mosquito density was analyzed. The Ae. albopictus adult insecticides resistance was evaluated by the World Health Organization (WHO) standard resistance bioassay. Adult females were exposed to insecticide-impregnated paper for 1 h, then transferred to the recovery tube, and mortality rate was calculated after 24 h. According to the Implementation Plan for National Vector Surveillance (2016), resistance status was classified into three levels based on mortality: <80% mortality as resistant, between 80% and 98% mortality as possibly resistant, and >98% mortality as sensitive. From June 2021 to September 2022, a total of 1,065 houses were surveyed for water holding containers, and 99 of 430 water holding containers were checked to be positive for Ae. albopictus larvae and pupae. A total of 1,048 Ae. albopictus females were collected. The average BI, CI and HBR were 10.39, 21.41, and 11.20 female/man/hour in 2021 and 8.86, 20.86, and 11.63 f/m/h in 2022, respectively. The findings showed that the BI exceeded 5 in most months and reached above 20 in specific months. The CI varied in different months and monitoring sites, with the highest CI in August 2021 and July 2022. The discarded tires had the highest positivity rate, with up to 40.32% testing positive for Ae. albopictus larvae. The monthly average temperature showed a positive correlation with CI (r = 0.77), and the monthly BI was positively associated with CI (r = 0.93). The BI, CI, and HBR were significantly higher in the rainy season than other seasons. The bioassay results showed that the mortality rate of Ae. albopictus at the YT monitoring site was 76.92%, indicating resistance to deltamethrin. The mortality rate of Ae. Albopictus at BL, WY, CH, YL, LN, LT, and ZZ sampling sites were varying from 81.25%∼100%, suggesting possibly resistant or still sensitive to beta-cypermethrin, alpha-cypermethrin, malathion, chlorpyrifos, and propoxur.

RescueNow: Real-time SOS and Predictive Women's Safety System

This paper presents a comprehensive women’s safety application designed to address the growing concerns of personal safety through technological intervention. The system integrates multiple advanced features such as real-time SOS alerts, geofencing to detect high-risk areas, AI-driven crime predictions, and multiple SOS activation methods, including voice commands, shake detection, and button press. Additionally, the app employs silent recording functionality to gather evidence in distress situations and incorporates secure communication channels for contacting law enforcement. The application also supports community-driven safety ratings and crime reporting. Tested with real-world scenarios, the system demonstrates high accuracy in predictive analytics and provides a seamless, scalable solution for enhancing women’s safety. This project contributes to the field of personal safety technologies by introducing a multi-faceted, AI-enabled framework for real-time risk mitigation. Key Words: Women’s safety, SOS alerts, AI crime prediction, geofencing, real-time GPS tracking, predictive analytics, Firebase Cloud Messaging, Twilio, silent recording, crime reporting.

Peri-Urban Floodscapes: Identifying and Analyzing Flood Risk Areas in North Bhubaneswar in Eastern India

Peri-urban catchment areas are increasingly susceptible to floods due to rapid land use transformations and unplanned urban expansion. This study comprehensively examines flood vulnerability in the rapidly developing peri-urban areas of North Bhubaneswar, focusing on significant changes in Land Use/Land Cover (LULC) and hydrological dynamics from 2004 to 2024, utilizing Geographic Information System (GIS) tools. The analysis reveals substantial shifts in land use patterns, with the urban footprint expanding by 71.8%, cropland decreasing by 21.7%, and forest areas by 13.6%. These changes have led to increased impervious surfaces, resulting in higher surface runoff and decreased groundwater recharge, thereby exacerbating flood risks in the region. The GRID-based vulnerability analysis classifies 90 villages within the catchment area based on their vulnerability levels, identifying 20 villages as high-risk areas requiring urgent attention, 44 villages as medium vulnerable, and 26 villages as low vulnerable. These classifications are based on factors such as proximity to drainage networks, slope, geomorphology, and LULC characteristics, with areas near drainage channels and low-lying regions being prone to flooding. The analysis integrates multiple factors to provide a comprehensive assessment of flood risk, highlighting the need for sustainable land use planning, conservation of vegetated areas, and the implementation of advanced flood prevention strategies in the peri-urban areas. Extending this research to other fringe regions could offer further valuable insights, guiding flood prevention and sustainable development strategies for areas undergoing significant land use transformations to effectively mitigate future flood risks.

Three-dimensional intelligent monitoring and early warning technology for tailings ponds based on spatiotemporal fusion of multisource big data.

To solve the difficult problems of tailings dam instability and environmental pollution, multisource information perception, prediction and early warning technology for tailings dams are investigated. Taking a tailings pond in China as an example, a three-dimensional visualization intelligent management platform based on the spatiotemporal fusion of multisource big data is established to realize intelligent real-time monitoring, prediction and early warning of tailings dams. A monitoring system for air-space-ground integration was developed via high-resolution optical image recording, unmanned aerial vehicles (UAVs), radar, video surveillance and displacement sensors. This approach can realize pollution monitoring and efficient identification of high-risk areas. In addition, a machine learning algorithm is used to mine spatiotemporal data in detail. The Alibaba cloud platform was adopted to develop a data integration framework. Multisensor spatiotemporal data from tailings dams and multisource monitoring data from the environment were integrated. The integrated management of tailings dam environmental monitoring, pollution and stability assessment is realized. A stability prediction model for tailings dams based on multisource data is proposed. The temporal and spatial information of various forms of data is analysed from multiple levels and perspectives. Rapid prediction of the disaster situation and stability of tailings ponds is realized. Moreover, a tailings pollution assessment model based on a neural network is integrated with multisource information to establish a multifactor environmental pollution neural network assessment model and a multilevel warning platform. This work can provide technical support for intelligent monitoring and early warning systems for tailings ponds.

EU’s AI Regulation Approaches and Their Implication for Human Rights

The rapid development of Artificial Intelligence (AI) technologies presents significant opportunities, but also introduces critical challenges, particularly concerning the protection of human rights. In response, the European Union (EU) has developed comprehensive AI regulations, most notably the AI Act, positioning itself as a leader in global AI governance. However, questions remain regarding the effectiveness of these laws in safeguarding key human rights such as privacy, equality, autonomy, and freedom. This study aims to assess the sufficiency of the EU’s current AI regulations in protecting human rights, analyze the specific impacts of AI technologies on privacy and equality, and explore emerging legal trends in AI regulation within the EU and their broader global implications. Utilizing a mixed-method approach, this research integrates legislative analysis, comparative research, scientific forecasting, and interdisciplinary inquiry. The study critically examines the provisions of the AI Act and other relevant EU regulations, comparing them with international legal frameworks to identify gaps and future challenges. The findings reveal that, while the EU’s AI regulations offer foundational protections, there are significant deficiencies in addressing complex issues such as privacy, algorithmic bias, and transparency, particularly in high-risk areas like healthcare, law enforcement, and autonomous systems. The study also highlights that existing regulations are inadequate in managing the rapid integration of AI technologies across sectors and that marginalized groups are especially vulnerable to the risks posed by algorithmic decision-making systems. Furthermore, the EU’s AI regulatory framework is poised to become a global benchmark, but there is an urgent need for more adaptive and flexible legal mechanisms to keep pace with the dynamic nature of AI advancements. In conclusion, the study emphasizes the need for legal reforms to bridge the current gaps in privacy protection and address algorithmic bias, ensuring the robust protection of human rights in the age of AI.

Centering and uplifting youth voice in planning for a more resilient climate future in rural Colorado: a case study of a student resilience team asking for change

Rural communities are increasingly facing challenges from climate-related natural hazards such as drought, wildfire, and flood. These cascading hazards threaten social, emotional, physical, and economic well-being, and pose unique challenges to rural areas which may already struggle to find enough resources. Yet, rural areas are rich in social capital, creativity, and resilience, and future leaders are learning how to build agency and increase their community’s resilience in school settings. Students in Estes Park, Colorado, have experienced major climate-fueled hazards in their lifetimes, surviving the floods in 2013 and the East Troublesome and Cameron Peak wildfires in 2020. At Estes Park Middle School, the Environmental Resilience Team (ERT) is an afterschool group of students who are actively participating in promoting public awareness, mitigation, and policy-making to increase resilience to natural hazards. Supported by the Hazard, Education, Awareness, and Resilience Task Force (HEART Force) Program, a comprehensive curriculum that immerses students in the study of their community’s resilience to floods and fires, in conjunction with the Earth Force Environmental Civic Action resources, students and educators gained the tools needed to spearhead community discussions on resilience, all while striving to mitigate and minimize future risks. In this paper, we tell the story of the Environmental Resilience Team’s campaign to pass statewide legislation to implement Wildland Urban Interface (WUI) Building Codes in high-risk areas. Students visited the Colorado State Capitol, met with their State Senator and Governor to lobby for the passage of the bill. This initiative builds upon years of action projects, including service projects to mitigate fire risk in vulnerable populations within the mountain town of Estes Park and build community education and awareness through the distribution of “go-bags” to community members. By including youth voice in public decision-making and planning for climate resilience, community leaders and policymakers can develop more holistic plans that include multigenerational input. This research is part of a broader study across implementations of HEART Force in several classrooms that showed that the HEART Force program helped students develop a sense of agency and grow in their understanding of community resilience.

Analyzing road traffic accidents through identification and prioritization of accident-prone areas on the dembecha to injibara highway segment in amhara region, ethiopia

Every year, millions die in road accidents globally, imposing significant economic and humanitarian costs. While road traffic accidents are a major health concern, many developing countries, including Ethiopia, struggle to address this issue effectively. Ethiopia ranks second in East Africa for severe road traffic accidents, highlighting the need for improved injury reduction strategies. This study introduces a novel approach by chronologically identifying and prioritizing accident black spots in the studied area, Ethiopia. This method provides a valuable tool for transportation authorities and traffic police to target high-risk areas for immediate intervention. Focusing on the Dembecha-Injibara highway segment, the study employs both descriptive and inferential analyses, using the Zegeer method to calculate accident rates. It also uses factors of weight contributing to road traffic accidents and their severity to rank accident-prone areas. The findings reveal that areas near Finote Selam, Banja, and Burie are highly prone to severe accidents, with specific accident frequencies and priority values identified. Recommendations are offered to address these high-risk areas and mitigate severe traffic accidents in the study region.

Application of Modified Skin Stretching for Soft Tissue Defect Reconstruction in the Ankle and Foot: A Retrospective Report.

The failure rate of foot and ankle soft tissue defect reconstruction with flap is relatively high, often posing a significant burden on patients. The aim of this study is to explore the effectiveness of repeated stretch sutures in repairing skin and soft tissue defects of the ankle and foot. Twenty-three patients with ankle and foot skin and soft tissue defects were retrospectively analyzed between February 2016 and February 2019. Sutures were repeatedly stretched every 3-5 days. Local skin grafting was performed if necessary after wound surfaces disappeared or exposed tendons and bones were covered by soft tissue. Wound healing time, postoperative healing area, Vancouver Scar Assessment Scale, sensation, and function of the new skin were evaluated. Healing time was 17-35 (24.43 ± 5.29) days. Ten patients wholly healed, and 13 healed by approximately 70.08% ± 6.59%. The Vancouver Scar Assessment Scale average score was 2.83 ± 1.19 points, of which 15 cases were excellent (0-3 points) and 8 cases were good (4-7 points). The sensation and function of the new skin after repair were equivalent to those of normal skin after the last follow-up. Applying repeated tension sutures on the skin and soft defects of the ankle and foot reduced the skin graft area and decreased complex high-risk surgical flaps' use and transplantation area.

Advanced Predictive Structural Health Monitoring in High-Rise Buildings Using Recurrent Neural Networks

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.

Risk of invasive MDRO infection in MDRO-colonized patients.

In this study, we aim to estimate the risk of developing clinical multidrug-resistant organism (MDRO) infection with carbapenem-resistant Enterobacterales (CRE), methicillin-resistant Staphylococcus aureus (MRSA), or vancomycin-resistant enterococci (VRE) in colonized patients compared with non-colonized admitted to high-risk areas with a main focus on CRE colonization/infection. Retrospective cohort study conducted at a tertiary care facility. This study included patients enrolled in active surveillance testing (AST) for CRE, MRSA, or VRE during the year 2021. Development of relevant invasive infection within 365 days of the AST result was collected as the primary outcome. The association between MDRO colonization and infection was calculated using the risk ratio. The prevalence of CRE organisms and carbapenemase genes is presented. A total of 19,134 ASTs were included in the analysis (4,919 CRE AST, 8,303 MRSA AST, and 5,912 VRE AST). Patient demographics were similar between colonized and non-colonized groups. Colonization was associated with an increased risk of infection in the 3 cohorts (CRE, MRSA, and VRE), with risk ratios reported as 4.6, 8.2, and 22, respectively. Most patients (88%) develop CRE infection with the same colonizing carbapenemase gene. Oxa-48/NDM Klebsiella pneumoniae was the most common organism detected in CRE infection. The study demonstrated that colonization with CRE, MRSA, or VRE is a risk factor for developing infections caused by the respective bacteria. The high percentage of match between carbapenemase genes detected in colonization and infection indicates that screening results might be used to inform infection management and treatment.

Landslide susceptibility mapping and similar case matching based on case library: a case study of Xinjing landslide, China

Landslide susceptibility mapping (LSM) predicts potential landslide risks based on historical landslides, playing a crucial role in landslide prevention and control. This study proposes a novel approach for LSM in open-pit mines, leveraging a comprehensive landslide case database. This method addresses the limitations of traditional LSM approaches, such as sensitivity to historical landslide records, low-quality inventories, and limited dataset size. By expanding the training set and incorporating case-based reasoning (CB-LSM) using Long Short-Term Memory (LSTM) for machine learning, the model demonstrated a 10% improvement in predictive accuracy compared to statistical models, showing strong applicability even in areas with low-quality landslide inventories. Additionally, this study enhances the practical utility of LSM by introducing a similar case matching method, employing Fuzzy C-Means (FCM) clustering, which links high-risk areas identified by LSM with specific landslide prevention measures. The proposed approach not only improves the accuracy of landslide predictions but also strengthens the connection between LSM results and effective landslide prevention strategies. Given the higher risks associated with landslides in open-pit mines, the proposed method significantly lowers the threshold for applying LSM in these environments, offering a valuable tool for safeguarding personnel and property.

Epidemiology and Ecology of Usutu Virus Infection and Its Global Risk Distribution.

Usutu virus (USUV) is an emerging mosquito-transmitted flavivirus with increasing incidence of human infection and geographic expansion, thus posing a potential threat to public health. In this study, we established a comprehensive spatiotemporal database encompassing USUV infections in vectors, animals, and humans worldwide by an extensive literature search. Based on this database, we characterized the geographic distribution and epidemiological features of USUV infections. By employing boosted regression tree (BRT) models, we projected the distributions of three main vectors (Culex pipiens, Aedes albopictus, and Culiseta longiareolata) and three main hosts (Turdus merula, Passer domesticus, and Ardea cinerea) to obtain the mosquito index and bird index. These indices were further incorporated as predictors into the USUV infection models. Through an ensemble learning model, we achieved a decent model performance, with an area under the curve (AUC) of 0.992. The mosquito index contributed significantly, with relative contributions estimated at 25.51%. Our estimations revealed a potential exposure area for USUV spanning 1.80 million km2 globally with approximately 1.04 billion people at risk. This can guide future surveillance efforts for USUV infections, especially for countries located within high-risk areas and those that have not yet conducted surveillance activities.

Multi-hazard, multidimensional disaster risk validation in selangor’s three urban districts, Malaysia

Considering the increasing impact of natural hazard-related disasters in Malaysia, understanding and evaluating integrated disaster risk must consider multi-hazard and multidimensional vulnerability at the local level, particularly in developing industrial nations like Malaysia. Therefore, this study proposes an improved methodology for conducting an integrated disaster risk assessment index (IDRI) mapping to measure disaster risk within local administrative boundaries in Malaysia. In this study, multi-hazard spatial overlapping combined two common hazards in Malaysia, which are floods and landslides. This study used multidimensional vulnerability, which encompasses six dimensions, 16 subdimensions, and 54 indicators. Researchers applied this approach in three urban districts of Selangor, Malaysia: Sepang, Kuala Langat, and Hulu Langat, which are located within the Langat River catchment and consist of 17 subdistricts. Overall, 32.9% of the total study area was found to be at risk, with 4.3% in the very high-risk area. In comparison with the latest flood events in 2021, the IDRI components were highly correlated with disaster impact. In conclusion, the contribution of this study provides a novel perspective on disaster risk assessment by addressing several types of hazards and multidimensional vulnerability, as compared to the previous methodology focusing on a single hazard and a physical vulnerability factor.

Predicting the Invasion Risk of the Highly Invasive Acacia mearnsii in Asia under Global Climate Change.

Acacia mearnsii, among the 100 worst invasive weeds worldwide, negatively impacts native biodiversity, agriculture, and natural ecosystems. Global climate change, characterized by rising temperatures and altered precipitation patterns, enhances the risk of A. mearnsii invasion in Asia, making it crucial to identify high-risk areas for effective management. This study performed species distribution modeling using the maximum entropy (MaxEnt) algorithm to predict the potential introduction and spread of A. mearnsii under various climate scenarios based on shared socio-economic pathways (SSP2-4.5 and SSP5-8.5). Currently, only 4.35% of Asia is invaded, with a high invasion risk identified in six countries, including Bhutan, Lebanon, and Taiwan, where more than 75% of their areas are threatened. Under future climate scenarios, 21 countries face invasion risk, among which 14 countries, such as Georgia, Laos, Republic of Korea, and Turkey, are at moderate to very high risk, potentially encompassing up to 87.89% of their territories. Conversely, Northern Asian countries exhibit minimal changes in invasion risk and are considered relatively safe from invasion. These findings underscore that climate change will exacerbate invasion risks across Asia, emphasizing the urgent need for robust management strategies, including stringent quarantine measures and control efforts, to mitigate the threat of A. mearnsii expansion.

A Novel Bayesian Spatio-Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk.

Identification of areas of high disease risk has been one of the top goals for infectious disease public health surveillance. Accurate prediction of these regions leads to effective resource allocation and faster intervention. This paper proposes a novel prediction surveillance metric based on a Bayesian spatio-temporal model for infectious disease outbreaks. Exceedance probability, which has been commonly used for cluster detection in statistical epidemiology, was extended to predict areas of high risk. The proposed metric consists of three components: the area's risk profile, temporal risk trend, and spatial neighborhood influence. We also introduce a weighting scheme to balance these three components, which accommodates the characteristics of the infectious disease outbreak, spatial properties, and disease trends. Thorough simulation studies were conducted to identify the optimal weighting scheme and evaluate the performance of the proposed prediction surveillance metric. Results indicate that the area's own risk and the neighborhood influence play an important role in making a highly sensitive metric, and the risk trend term is important for the specificity and accuracy of prediction. The proposed prediction metric was applied to the COVID-19 case data of South Carolina from March 12, 2020, and the subsequent 30 weeks of data.

Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone

Abstract Based on probabilistic seismic hazard analysis, the seismic landslide hazard research considers the spatial and temporal distribution characteristics of seismic peak ground acceleration, which integrates the factors such as seismic intensity, location, and recurrence time. The occurrence of future earthquakes has certain randomness. This article presents the landslide hazard zoning of the eastern Kunlun fault zone and its surrounding faults, which is carried out under the action of horizontal ground motion with certain exceeding probability. According to the geological structure and seismicity characteristics of the study area, the potential source is divided. Based on the seismic hazard analysis and Newmark cumulative displacement evaluation model, the seismic landslide hazard in the study area is analyzed. The landslide probability is taken as the risk index. The seismic landslide hazard can be divided into five grades: extremely low-prone area, low-prone area, medium-prone area, high-prone area, and extremely high-prone area. In the results of seismic landslide risk zoning given in this article, the surrounding areas of Tazang fault and Minjiang fault are high-risk areas, which should be paid attention to.

Optimising hurricane shelter locations with smart predict-then-optimise framework

ABSTRACT Hurricanes pose an escalating threat to global communities, underscoring the urgent need for robust disaster response strategies. A pivotal component of these strategies involves the establishment of secure shelters. However, the inherent vulnerability of these shelters to hurricane damage frequently undermines their utility. This study introduces a Predict-then-Optimise (PTO) framework designed to support relief agencies in selecting optimal locations for emergency shelters, with an emphasis on minimising potential damage during hurricanes. Employing a two-phase approach, the framework initially predicts potential hurricane-induced damage losses, subsequently utilising these predictions to optimise shelter placement strategies. Nevertheless, conventional PTO methods in shelter planning may lead to suboptimal decisions, primarily because of potential discrepancies between predicted and actual damage losses, given the inherent uncertainties and complexities of hurricane impacts. To address these limitations, our study introduces an advanced smart Predict-then-Optimise (SPO) framework. This SPO framework more cohensively integrates the prediction and optimisation phases, thereby facilitating an adaptive and resilient response to the dynamic challenges posed by hurricanes. We demonstrate the effectiveness of this methodology through a case study in Miami-Dade County, Florida, where the SPO framework successfully identified optimal shelter locations, significantly reducing exposure to high-risk areas.