Hazard Scenarios Research Articles

Systematizing the risk management process in clinical radiotherapy practice: Recommendations of the working group on risk management of the DGMP

PurposeThe Deutsche Gesellschaft für Medizinische Physik [German Society of Medical Physics] has recently published two coherent reports, No. 25 and No. 28, detailing the design and implementation of a risk management (RM) process for German radiotherapy (RT) departments. This study offers an overview and background of the efforts behind these reports. Methods and MaterialsFor three years, up to nine medical physicists (MPs) with practical RM experience held weekly meetings to develop recommendations for a clinical RM process. Care was taken to ensure that the recommendations were equally applicable to RT departments of various sizes. A process-based method derived from the failure mode and effects analysis (FMEA) was created to identify and address risks from unintentional radiation exposure. This method was applied to exemplarily analyze the hazardous scenarios in breast RT using surface guidance and deep inspiration breath hold (DIBH) techniques. Three common criticality methods—risk matrix, risk priority number, and action priority—were applied, and each step was schematically explained for first-time users. Each report was peer-reviewed by two radiation oncologists and 11 MPs. ResultsIn report No. 25, basic requirements were outlined for running the RM process, conducting risk assessments, and monitoring clinical procedures. A three-year plan-do-check-act cycle was proposed for continuous improvement. In report No. 28, general process lists for external beam radiotherapy (EBRT), brachytherapy, and radionuclide therapy were designed. Based on the EBRT process list, 45 hazardous scenarios in the surface-guided breast RT in DIBH were identified. Two scenarios were used to illustrate handling instructions for the three criticality methods. ConclusionsThe recommendations provide clinical MPs and other health professionals with a pragmatic approach to RM, balancing both the needs of smaller practices and larger clinics in Germany. The risk of unintended exposures of patients is viewed acceptable once it has been lowered to a state that is as low as reasonably achievable.

Self-reconfigurable multifunctional memristive nociceptor for intelligent robotics

Abstract Artificial nociceptors, mimicking human-like stimuli perception, are of significance for intelligent robotics to work in hazardous and dynamic scenarios. One of the most essential characteristics of the human nociceptor is its self-adjustable attribute, which indicates that the threshold of determination of a potentially hazardous stimulus relies on environmental knowledge. This critical attribute has been currently omitted, but it is highly desired for artificial nociceptors. Inspired by these shortcomings, this article presents, for the first time, a Self-Directed Channel (SDC) memristor-based self-reconfigurable nociceptor, capable of perceiving hazardous pressure stimuli under different temperatures and demonstrates key features of tactile nociceptors, including ‘threshold,’ ‘no-adaptation,’ and ‘sensitization.’ The maximum amplification of hazardous external stimuli is 1000%, and its response characteristics dynamically adapt to current temperature conditions by automatically altering the generated modulation schemes for the memristor. The maximum difference ratio of the response of memristors at different temperatures is 500%, and this adaptability closely mimics the functions of biological tactile nociceptors, resulting in accurate danger perception in various conditions. Beyond temperature adaptation, this memristor-based nociceptor has the potential to integrate different sensory modalities by applying various sensors, thereby achieving human-like perception capabilities in real-world environments.

Navigating Concurrent Disasters: Lessons learned from a Hospital Evacuation Amidst a Pandemic and an Earthquake.

The concurrent challenges of the COVID-19 pandemic and a significant earthquake in Izmir on October 30, 2020, presented a unique scenario for disaster management and response. This study focuses on the impact of the earthquake, which resulted in 117 fatalities, including 1 due to drowning, and injured 1034 individuals, alongside widespread structural damage including to the Izmir Democracy University Buca Seyfi Demirsoy Training and Research Hospital. The objective is to assess the activation and implementation of the hospital disaster plan amidst the ongoing pandemic. Through a retrospective evaluation of all actions undertaken as per the Hospital Disaster Emergency Plan within the Disaster Management cycle, this study examines the decision-making process for the hospital evacuation on October 30, 2020, the evacuation of COVID-19 patients, and the strategies employed to increase hospital capacity. Of 216 patients hospitalized at the time of the earthquake, 65 were transferred to other facilities under COVID-19 protocols. The prolonged nature of pandemics and the likelihood of secondary disasters underscore the importance of comprehensive risk assessments and dynamic disaster planning, considering simultaneous multiple hazards. This study suggests the inclusion of multi hazard scenarios and diverse evacuation methods by using types of ambulances, such as ground, helicopter, and boat.

Inundation and evacuation of shoreline populations during landslide-triggered tsunamis: an integrated numerical and statistical hazard assessment

Abstract. The volcanic island of Stromboli (southern Tyrrhenian Sea, Italy) is renowned for its persistent, periodic, low-intensity explosive activity, whose spectacular manifestations attract tens of thousands of tourists every year. However, sporadic more intense major explosive and effusive eruptions and paroxysms pose serious threats to the island. In addition to direct hazards, granular slides of volcanic debris and pyroclastic avalanches, which can rapidly reach the sea and potentially generate tsunamis, are often associated with such unpredictable eruptive activity. Due to the very fast propagation of the tsunami around the island and the consequent short tsunami warning time (ranging from less than a minute to only a few minutes), mitigation efforts and evacuation from the Strombolian coast must be carefully planned. In this paper, we describe a new GIS-assisted procedure that allows us to combine the outputs of an ensemble of 156 pre-computed landslide-generated tsunami hazard scenarios (with variable landslide volume, position, and density), statistical exposure data (i.e. the number of inhabitants and tourists), and digital geographic information to obtain a quantitative (scenario-based) risk analysis. By means of the analysis of the road network and coastal morphology, we develop a model with routes and times to reach a safe area from every pixel in the inundated area and an appraisal of the time needed to escape versus the wave arrival time. This allows us to evaluate and quantify the effectiveness of potential risk mitigation by means of evacuation. The creation of an impact score linking the predicted inundation extent and the tsunami warning signals is intended, in the long term, to be used to predict the intensity of future tsunamis and to adapt evacuation plans accordingly. The model, here applied to Stromboli, is general and can be applied to other volcanic islands. Evacuating an island hosting several thousand tourists every summer with very little warning time underlines the absolute necessity for such mitigation efforts, aimed at informing hazard planners and managers and all other stakeholders.

Enhancing safety in the storage of hazardous molecules: The case of hydroxylamine

Handling large quantities of thermally unstable compounds in storage vessels can result in severe accidents due to runaway reactions. Therefore, developing inherently safe design strategies for storage equipment is crucial for enhancing chemical plant reliability and preventing hazardous scenarios. The Frank-Kamenetskii theory (FKT) of self-heating offers practical tools for designing procedures to address phenomena that could lead to uncontrolled chemical reactions. This study introduces a design procedure based on the FKT for creating intrinsically safe storage vessels. An expanded FKT version incorporating parametric sensitivity analysis has been developed to improve the method's reliability. To understand self-heating phenomena, stability and performance diagrams were created, relating critical design parameters (e.g., the critical value of the Frank-Kamenetskii number) and verification parameters (e.g., maximum reached dimensionless temperature) to the dimensionless activation energy (γ). Additionally, the proposed design strategy includes a procedure for designing relief systems to mitigate the risk of equipment explosions from runaway reactions. The applicability of this procedure was tested using two cases: (I) an aqueous solution containing 50% w/w hydroxylamine (HA) and (II) a 50% w/w HA aqueous solution with 1% w/w hydroxylamine hydrochloride (HA-derived salt). Results indicate that for large γ values, the traditional FKT formulation and the expanded theory yield similar vessel designs. However, for finite γ values (γ≤100), the refined FKT version allows for less conservative storage equipment design. Combining DIERS guidelines with standard procedures for relief systems results in a more versatile and consistent protocol. However, incorporating relief systems is often impractical for large storage vessels due to the excessively large venting areas required for runaway reactions. In such cases, intrinsically safe vessel designs become the only feasible solution to prevent catastrophic incidents.

Integrated physical safety–cyber security risk assessment based on layers of protection analysis

The extensive application of information technology in process industries has increased production efficiency but has also introduced new risks. Therefore, it is necessary to systematically analyse the risks within factories to ensure the stable operation of their production systems. This study proposes an integrated risk assessment method based on layers of protection analysis (LOPA), which combines physical safety and cyber security analyses to provide comprehensive risk assessments for the process industry. The method first identifies the hazardous scenarios and protection layers relevant to a process facility. It then identifies potential cyberattack types and existing countermeasures. Subsequently, the functional impacts of attacks on protection layers and potential coupling relationships are discussed. Using common vulnerability scoring system (CVSS) and semi-quantitative methods, the probability of attack is determined to optimize the probability of failure on demand (PFD) of the protection layers. Finally, a case study of a steam separator in a catalytic cracking unit is used to quantitatively explore the potential attacks and risks of coupled protection layers. The application of Bayesian network (BN) is used for further validation of the method. This study offers a novel quantitative tool for risk assessment in the process industry, which can enhance the security and reliability of industrial production and control systems.

Expecting the expected – learning from the past to provide forward scenarios through geomorphic change detection, monitoring and modeling

This paper tells the story of a landslide, its origins, its activity and the actions undertaken to mitigate the risks that it poses. The Rotolon landslide is a large Deep-seated Gravitational Slope Deformation (DGSD), located in the Eastern Italian Alps, whose unremitting movements provide a sediment supply for large-scale debris flow events. It has been active since at least 1789, threatening the valley where the thermal baths town of Recoaro Terme is located. In 2010, a major reactivation of the landslide channelled 320,000 m3 of material towards the town causing significant concern, the evacuation of the exposed population and threatening several buildings. A few days after the emergency, the personnel of the Research Institute for Geo-Hydrological Protection of the Italian Research Council (IRPI-CNR) deployed a monitoring system consisting of an automatic total station, several extensometers and web cameras to monitor the evolution of the unstable slope and set up an early warning and alarm system equipped with sirens to warn the local population. Subsequently, after the emergency phase, the 2010 event was studied through multi-temporal LiDAR Digital Terrain Models (DTMs) and modeling. The authors have continued monitoring and actively studying this landslide ever since. In 2020, a new LiDAR survey of the area allowed assessment of the sediment dynamics within the catchment through the comparison with the post-2010 event LiDAR DTM. Based on DEM of difference maps, new insight into the behaviour of the catchment emerged, which appears to be influenced both by natural processes and anthropic activities. The authors were able to assess the amount of sediment that could be stored in the channel without causing overflooding and to calculate the expected damage to the built environment should another event occur. Furthermore, the considerable amount of data collected by the monitoring system throughout the years has allowed identification of two active sectors of the DGSD that may be prone to detachment, and through numerical modeling, future hazard scenarios were produced. Based on these outcomes, a second-tier targeted monitoring campaign was implemented, consisting of a network of four permanent GNSS receivers, additional topographic benchmarks and web cameras, and a new early warning protocol, in an ever-updating cycle of monitoring, modeling and mitigation.

Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios

Flash floods are highly destructive, and their frequency and intensity are expected to escalate due to climatic changes. This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. To predict FFS, we evaluated twelve flood-influencing variables: elevation (EL), slope (SL), aspect (AS), drainage density (DD), distance to stream (DS), topography roughness index (TRI), stream power index (SPI), topographic wetness index (TWI), soil permeability (SP), precipitation (PR), land use and land cover (LULC) and normalized difference vegetation index (NDVI). Earth observation data, field surveys, and past flood records were used to create a detailed flood inventory. Among the machine learning models tested, the random forest (RF) algorithm outperformed others, including support vector machine (SVC), logistic regression (LR), and extreme gradient boosting (XGBoost), and was subsequently used for flood susceptibility mapping based on future precipitation projections under two Sixth Coupled model intercomparison project (CMIP6) climate change scenarios: SSP1-2.6 and SSP5-8.5. Our findings indicated that the areas at high to very high risk of flooding are projected to increase significantly under both the SSP1-2.6 and SSP5-8.5 scenarios. Initially, around 38 % of the studied region had high to very high flood susceptibility, but this is expected to rise to 40–42 % over the projected time periods. These spatial delineations of flood-prone areas can provide guidance for developing effective mitigation and adaptation strategies to address the adverse impacts of flash flooding in the hilly river basins of Bangladesh.

Immune Responses of Club Cells in Fish: A Review

The primary line of defense against pathogens from the environment is often fish epidermis tissue. Nevertheless, little is understood about the physiological mechanisms that underlie the non-specific and/or specific protection that these cells can offer. The exact nature of the relationship between the evolution of ostariophysan fish club cells and chemical warning signals is still unknown and controversial. Fish epidermis layer cells comprise mucus cells, lymphocytes, macrophage cells, cuboidal and squamous epithelial cells, and cells specific to certain fish species. Club cells, also called "alarm cells," are chemical alarms that sound in the event of a potentially hazardous scenario. These cells will burst in the presence of a predator, releasing pheromones that, if the skin is physically damaged, trigger an avoidance and terror reaction. In sturgeon larvae, mucus cells were visible in week 1, but club cells did not appear until week 4. Due to their later development during ontogenesis and after wounding, club cells may not have as much of a protective effect during wound healing as filament or mucus cells. Club cells are mostly found in the epidermis of the skin, and it is thought that when they work in tandem with mucus and goblet cells to fight infections, they serve as the body's first line of defense.

A systemic approach for assessing infrastructure component importance in hazard-prone communities

Investing in pre-event disaster mitigation interventions for physical infrastructure, such as structural retrofits and enhancements, can be costly due to limited resources. To prioritize investments, infrastructure components are ranked by their criticality within the overall system. To be effective in real-world deployment, this approach must account for the complex interactions between components, as failures can occur simultaneously across large geographical areas due to the hazard footprint. As a result, hazard-specific uncertainties and spatial correlations may lead to distinctive failure patterns. In this study, we propose a novel data-driven framework leveraging Monte Carlo simulation, that harnesses the individual realizations to capture and model realistic component damage patterns under a specified hazard scenario. This framework addresses a gap in literature by moving beyond traditional methods that often treat component failures as independent events. By capturing the interdependence between bridges, primarily through failure interactions, and system-wide effects, our method provides a more comprehensive criticality assessment. The simulation data provides a foundation for the framework, which applies to a wide variety of infrastructure networks and performance metrics. To demonstrate the method's effectiveness, a simplified transportation network from Shelby County, TN subjected to an earthquake event is analyzed. The proposed framework provides an effective approach for component ranking, suitable for decision-making where human intuition and simple methods are insufficient. Its broad applicability suggests a potential for large-scale and interdependent network problems.

Modeling and Simulation of Flood Hazard Scenarios on Lowly Morphometric Drainage Terrain in Rivers State, Nigeria

Modeling and Simulation of Flood Hazard Scenarios on Lowly Morphometric Drainage Terrain in Rivers State, Nigeria

A risk assessment of a gas pressure reduction station system with confidence for dealing with imprecisions and unknowns

Process systems are sensitive and vital industrial facilities. Disturbances in their performance may cause harm to the environment,humans,or significant economic damage. In risk assessment of chemical process industries, the available data, information, and knowledge are typically rare, limited, and often unrealistic. This issue poses a challenge to conducting a credible quantitative risk assessment and effects the robustness of the results. To address these challenges, this work proposes a methodology based on the Dempster-Shafer theory of evidence as the reasoning framework. It incorporates risk identification, analysis, and mitigation phases to ensure a thorough analysis of risks and the integration of proactive risk reduction strategies. The approach aims to model the worst-case hazard scenario and assess associated risks using various methods such as FMECA, Bow-Tie, Credal Network, and Dempster-Shafer theory. The proposed approach models imprecision and data ambiguity using intervals and associated belief mass. This extension provides a basis for addressing the fundamental problem of prior ignorance about the distribution of the observed data, which is prevalent in data mining applications. A new approach is proposed that utilizes Belief and Plausibility curves, similar to a Cumulative Distribution Function, to propagate uncertainty, enhance criticality discrimination, and determine cumulated belief measures. This approach is applied in analyzing the failure modes identified in FMECA and is further extended through the credal network for comprehensive risk assessment. Results show how to express irrelevant and independent judgments, and how to work out with inferences in credal networks. This issue is often overlooked, but if properly addressed it represents the key to ultimately drawing reliable conclusions and fully utilizing the system's available data. A case study of the City Gate Station system was used to verify the application potential of the proposed approach.

An integration methodology of safety and security requirements for autonomous vehicles

Safety and security co-engineering is one of the latest challenge in autonomous vehicle (AV) development. Efficiently integrating safety and security requirements during co-engineering is a new issue. Most functional safety and security analysis methods do not directly derive safety requirements, and improper handling of their relationship can affect system design and timelines. This article aims to use large language models (LLMs) to assist in the collaborative work of functional safety and security analysis. The main contributions are as follows: First, we propose three types of formulations to summarize hazard scenarios and threat scenarios and use LLMs to extract functional safety requirements and security requirements from them. Second, we utilized the three LLMs to perform relationship checks on the extracted functional safety requirements and security requirements. The results showed that the majority of the checks were correct and consistent, with only a small portion requiring manual intervention, significantly reducing human labor. Through these methods, we demonstrate the potential and efficiency of LLMs in the collaborative analysis of functional safety and security.

High-risk powered two-wheelers scenarios generation for autonomous vehicle testing using WGAN

Objective Autonomous vehicles (AVs) have the potential to revolutionize the future of mobility by significantly improving traffic safety. This study presents a novel method for validating the safety performance of AVs in high-risk scenarios involving powered 2-wheelers (PTWs). By generating high-risk scenarios using in-depth crash data, this study is devoted to addressing the challenge of public road scenarios in testing, which often lack the necessary complexity and risk to effectively evaluate the capabilities of AVs in high-risk situations. Method Our approach employs a Wasserstein generative adversarial network (WGAN) to generate high-risk scenes, particularly focusing on PTW scenarios. By extracting 314 car-to-PTW crashes from the China In-depth Mobility Safety Study–Traffic Accident database, we simulate outcomes using PC-Crash software. The data are divided into scenes at 0.1-s intervals, with WGAN generating numerous high-risk scenes. By using a cumulative distribution function (CDF), we sampled and analyzed the vehicle’s dynamic information to generate complete scenarios applicable to the test. The validation process involves using the SVL Simulator and the Baidu Apollo joint simulation platform to evaluate the AV’s driving behavior and interactions with PTWs. Results This study evaluates model generation results by comparing distributions using Wasserstein distance as an indicator. The generator converges after approximately 200 epochs, with the iterator converging quickly. Subsequently, 10,000 new scenes are then generated. The distribution of several key parameters in the generated scenes can be found to approximate that of the original scenes. After sampling, the usability of generated scenarios is 64.76%. Virtual simulations confirm the effectiveness of the scenario generation method, with a generated scenario crash rate of 16.50% closely reflecting the original rate of 15.0%, showcasing the method’s capacity to produce realistic and hazardous scenarios. Conclusions The experimental results suggest that these scenarios exhibit a level of risk similar to the original crashes and are effective for testing AVs. Consequently, the generated scenarios enhance the diversity of the scenario library and accelerate the overall testing process of AVs.

Merging modelled and reported flood impacts in Europe in a combined flood event catalogue for 1950–2020

Abstract. Long-term trends in flood losses are regulated by multiple factors including climate variation, demographic dynamics, economic growth, land-use transitions, reservoir construction and flood risk reduction measures. The attribution of those drivers through the use of counterfactual scenarios of hazard, exposure or vulnerability first requires a good representation of historical events, including their location, their intensity and the factual circumstances in which they occurred. Here, we develop a chain of models that is capable of recreating riverine, coastal and compound floods in Europe between 1950 and 2020 that had a potential to cause significant socioeconomic impacts. This factual catalogue of almost 15 000 such events was scrutinized with historical records of flood impacts. We found that at least 10 % of them led to significant socioeconomic impacts (including fatalities) according to available sources. The model chain was able to capture events responsible for 96 % of known impacts contained in the Historical Analysis of Natural Hazards in Europe (HANZE) flood impact database in terms of persons affected and economic losses and for 81 % of fatalities. The dataset enables the study of the drivers of vulnerability and flood adaptation due to a large sample of events with historical impact data. The model chain can be further used to generate counterfactual events, especially those related to climate change and human influence on catchments.

Modeling the fire scenario in the diesel storage tank of the Iran’s specialized hospital using PHAST software

Background and Objective: Diesel storage tanks play a crucial role in hospitals and are constantly at risk of chemical release, explosion, and fire. The purpose of this study was to examine the consequences of a diesel tank leak in one of the Iran’s specialized hospitals. Materials and methods: This study utilized an applied research approach with scenario modeling using PHAST software version 8.4. A discharge rate of 1000 L/min was employed for the fixed-duration release scenario. The necessary information for the software, including tank and meteorological data, was collected. The mortality rate and danger radius were calculated and analyzed for two tank volumes of 23600 and 12000 m3 in conditions of Sabzevar region in winter and 1.5 F of the software. Results: According to the tank type and substance release into the environment, the software showed a pool fire. The most hazardous scenario was determined to be leakage from the 23600 m3 tank in Sabzevar's weather conditions (26 0C, wind speed 15 m/s in winter). The maximum and minimum radiation heat from the fire were calculated at 44.7 and 94.2 meters, respectively. The mortality rates for exposure levels of 37.5 kw/m2, 12.5 kw/m2, and 4 kw/m2 were found to be 98.7%, 6.5%, and approximately zero, respectively. Conclusion: Fire safety in hospitals is a critical issue that requires a thorough examination of the factors that can lead to ignition and explosion.

Effectiveness of regional risk mitigation policies in equitably improving connectivity to essential service during hurricane-induced floods

This study aimed to evaluate the effectiveness of regional disaster mitigation policies in providing equitable access to essential services during hurricane-induced floods in Southeast Louisiana. Studies have been conducted on access to essential service facilities during and after disasters. However, there is limited research on whether current regional disaster mitigation plans ensure equitable access to emergency facilities. To this end, a methodology was developed to assess the effect of the 2017 Louisiana Coastal Master Plan developed by the Coastal Protection and Restoration Agency (CPRA) on equity in connectivity to essential service facilities (hospitals and fire stations) in a case study region in southeastern Louisiana. First, a graph-based model was used to represent the road network and determine the connectivity of five demographic groups during hurricane-induced flood scenarios. Herein, a hurricane-induced flood scenario with a low return period (50 years) was chosen for future years up to 2065, considering sea level rise and subsidence. Roads with flood depths exceeding 30 cm were considered impassable and were removed from the network. Connectivity was then determined using breadth-first search. Monte Carlo simulations were performed to propagate uncertainties in flood depths. It was found that minority groups were at a higher risk of losing access to emergency services during the selected hazard scenario. Native American and Asian populations were found to be at the highest risk of disconnection, followed by the Hispanic and African-American populations. Overall, the coastal master plan did not affect the equity (or lack thereof) in connectivity to essential service facilities.

Managing compound events in the COVID-19 era: A critical analysis of gaps, measures taken, and challenges

Since the beginning of 2020, the focus has predominantly been on the prevention and control of the COVID-19 pandemic. However, the 'new normal' has drastically altered responses to other hazards, underscoring the need to prepare and plan for complex, multi-risk scenarios. Nevertheless, there's limited literature on disaster preparedness and response for compound hazard scenarios, particularly pandemic-natural hazard hybrids. The few studies that have explored concurrent hazards during the COVID-19 pandemic have largely focused on developed, high-income countries. Yet, compound events can exacerbate pre-existing socio-economic vulnerabilities in developing countries, straining systems and causing disproportionate impacts. This study addresses this lacuna by examining (1) emergency preparedness and response measures for Sri Lanka's Southwest and Northeast monsoons during the COVID-19 pandemic, and (2) the feasibility of current Disaster Risk Reduction (DRR) strategies for managing compound pandemic-natural hazard events in Sri Lanka. The research involves a desk-based policy analysis of disaster and health policies, key informant interviews with 26 officials, and focus group discussions with Public Health Inspectors from Ratnapura, Jaffna, Matara, and Gampaha. Findings indicate that while significant steps were taken, including impact scenario analyses, stakeholder meetings, and revised response measures, challenges such as resource constraints and limited capacity persisted. This study further demonstrates that the country's existing DRR strategies are characterised by gaps that may hinder capacities to effectively manage compound hazards. The findings of this study therefore encourage the implementation of structural changes and anticipatory action that adequately account for the complexity of risk supported by cross-sectoral collaboration. The study particularly highlights the importance of proactive scenario-based planning, developing composite risk matrices that account for multiple dimensions of vulnerability, fostering cross-sectoral and cross-level collaboration, and adapting existing early warning systems to effectively manage compound hazard scenarios.

Towards integrated flood management: Vulnerability and flood risk in the Ayeyarwady Delta of Myanmar

Despite the rising global flood risk, the impacts of flooding remain systematically underestimated, leading to significant consequences for particularly vulnerable river deltas. Most studies focus either on single hazards or social vulnerability while overlooking the interconnected dynamics of deltaic social-ecological systems. In response to the first priority of the Sendai Framework, which calls for an understanding of disaster risk in all its dimensions, we apply the Global Delta Risk Index in the Ayeyarwady Delta of Myanmar. We combine 55 indicators of social- and ecosystem vulnerability with 100-year, 500-year, and 1,000-year scenarios of pluvial, fluvial, and coastal flood hazards and exposure at the sub-delta scale. Using townships as units of analysis allows for bridging the gap between global and local case studies, providing insights that are meaningful for risk-informed development of the delta as a whole system. We also examine the distinctive characteristics that define the delta systems that are particularly prone to flooding. Our results reveal patterns and drivers of flood risk and vulnerability that affect at least 65 % of the delta's population and 60 % of its ecosystem, with self-reinforcing dynamics, but also those that contribute to the mutual resilience of both systems. We argue that the principles of integrated flood management should be applied to leverage scarce resources to simultaneously reduce risk, secure livelihoods and preserve ecosystem services.

Generalization of cut-in pre-crash scenarios for autonomous vehicles based on accident data

The utilization of high-risk test cases constitutes an effective approach to enhance the safety testing of autonomous vehicles (AVs) and enhance their efficiency. This research paper presents a derivation of 2052 high-hazard pre-crash scenarios for testing autonomous driving, which were based on 23 high-hazard cut-in accident scenarios from the National Automobile Accident In-Depth Investigation System (NAIS) through combining importance sampling and combined testing methods. Compared to the direct combination of the original distribution after sampling, the proposed method has a 2.92 times higher crash rate of 69.32% for the test case set in this paper. It also has a 5.8 times higher rate of triggering Automatic Emergency Braking (AEB), improving hazardous scenario coverage. Using the proposed method, the generated parameters of the cut-in accident scenario test set were compared with those of the cut-in test scenarios included in existing Chinese autonomous driving test protocols and standards. The velocity of the ego-vehicle obtained using the proposed method matched those in the existing protocols, whereas the velocity, time gap, and time to collision of the target vehicle were significantly lower than those existing protocols indicating scenarios obtained from accident data can enrich the selection of testing scenarios for autonomous driving.