Lands and populations are the most direct and core disaster-bearing bodies in floods. Accurate and comprehensive identification their attributes is critical for differentiated flood prevention and mitigation strategies. However, two key challenges persist in current practices. First, the accuracy of urban land function (ULF) identification based on machine learning is constrained by data and grid scale, yet research on their impacts remains insufficient. Second, location-based service (LBS) data has sampling bias and nighttime distortion in characterizing dynamic population distribution, and its spatial resolution is insufficient for high-precision flood simulations. For ULF identification, abundant comparison schemes are generated through data traversal and multi-scale fusion, and an ensemble learning model is constructed to select the optimal ULF identification scheme. This avoids the accuracy uncertainty caused by subjective selection, and the results provide reliable data support for economic loss assessment and subsequent population spatial interpolation. For dynamic population distribution, a human-land relationship matching method based on spatiotemporal behavioral laws is proposed to reduce the impact of data bias. Meanwhile, spatial downscaling is achieved through regional division, land type and area weight calculation, generating dynamic population distribution maps with high spatiotemporal resolution. The results support the analysis of population mobility’s impact on flood risk. Hydraulic simulation is coupled with GIS (geographic information system) analysis to construct a grid-based diagnostic framework for multi-attributes of disaster-bearing bodies, including land function, population size, water depth, and spatial location. Case studies show that this framework provides reliable support for the accurate and comprehensive identification of flood disaster-bearing bodies.

Despite the advantages of using Bayesian networks for probabilistic risk assessment, adoption in practice has been limited due to the lack of realistic, facility-scale studies. Scaling up from systems to facility-level safety assessments poses challenges in (i) integrating external hazards and their cascading effects, and (ii) resolving non-homogeneity of various technical and human reliability models. The novelty of the study is in formalising risk integration using Bayesian networks, at facility scale, and demonstrating its effectiveness in addressing associated challenges. A Bayesian network-based multi-hazard risk framework is introduced and demonstrated for a nuclear power plant subject to flooding and earthquake hazards, capturing dependencies among hazards and consequences. Individual reliability models – conventionally extraneous to facility-wide risk models – are included as subnetworks by using Bayesian network-based surrogate models for technical systems and a Bayesian networks approach for human reliability modelling. Two approaches are used for subnetwork integration – object-oriented and unified Bayesian networks. The unified approach allows for prediction, diagnostics and inter-causal reasoning since Bayesian inference is bi-directional. Conversely, in the object-oriented approach, diagnostics are limited to within individual subnetworks and as a consequence the model can potentially neglect dependencies between objects. However, the object-oriented model requires only 50 % of the computational memory and consumes less than 25% of the runtime as the unified network, while improving visual clarity of the risk model. The model reveals key insights – for example, variations in operator stress or available response time during a hazard event can result in up to a 77 % change in top event probability – demonstrating its effectiveness in capturing critical relationships in complex, facility-scale risk scenarios. These findings can be used to suitably allocate resources towards risk mitigation and plant safety management.

Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role in enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has been no review grounded in bibliometric analysis in this field. To explore the research evolution and knowledge frontier in the field of maritime safety for autonomous shipping, a bibliometric analysis was conducted using 719 publications from the Web of Science database, covering the period from 2000 up to May 2024. This study utilized VOSviewer, alongside traditional literature analysis methods, to construct a knowledge network map and perform cluster analysis, thereby identifying research hotspots, evolution trends, and emerging knowledge frontiers. The findings reveal a robust cooperative network among journals, researchers, research institutions, and countries or regions, underscoring the interdisciplinary nature of this research domain. Through the review, we found that maritime safety machine learning methods are evolving toward a systematic and comprehensive direction, and the integration with AI and human interaction may be the next bellwether. Future research will concentrate on three main areas: evolving safety objectives towards proactive management and autonomous coordination, developing advanced safety technologies, such as bio-inspired sensors, quantum machine learning, and self-healing systems, and enhancing decision-making with machine learning algorithms such as generative adversarial networks (GANs), hierarchical reinforcement learning (HRL), and federated learning. By visualizing collaborative networks, analyzing evolutionary trends, and identifying research hotspots, this study lays a groundwork for pioneering advancements and sets a visionary angle for the future of safety in autonomous shipping. Moreover, it also facilitates partnerships between industry and academia, making for concerted efforts in the domain of USVs.

The increasing amount of activities at sea, including the development of offshore wind parks, result in a more confined space for shipping, requiring the assessment of risk changes regarding nautical safety and the design of potential mitigation measures. The main contribution of this paper is the transparent evaluation of allision probabilities, based on an event-based approach. This enables a structural consideration of conditional probabilities, and supports uniting quantitative and qualitative analyses. The event-based approach allows evaluating the outcomes from various perspectives: scales, conditions, behaviour and dependencies. The analysis outcomes are represented in a concept called “event table”, from which these perspectives can be extracted. Consequently, from this single data structure, insights can be gained ranging from spatial variations of the risk (highly detailed or global patterns), to detailed distinction between the most important influencing factors (varying from vessel type to environmental condition). It is furthermore possible to switch between wind-park specific risks and assessment of operational and strategic risk-mitigating measures for the entire area. The core feature of incorporating multiple perspectives not only allows various views on the safety risks, providing a better understanding of the most important contributing factors, as well as effectiveness of intervention measures. Our analysis shows the added value of additional distance between shipping lanes and wind parks in the spatial design, and we demonstrate how our multi-perspective approach supports the strategic and operational decisions around the availability and deployment of emergency response vessels.

Maritime Autonomous Surface Ships (MASS) are advancing the shipping industry, requiring a mixed waterborne transport system (MWTS) where human supervision provides a supporting role for maintaining safety and efficiency, particularly in complex scenarios. This study explores the dynamics of seafarers’ trust in MASS during collision avoidance (CA) scenarios involving a vessel approaching from the starboard side. An empirical study with 26 participants representing diverse maritime experience levels examined how time, demographic factors, and collision avoidance strategies influence trust. Using a linear mixed model (LMM), trust was found to fluctuate across navigation stages: gradual accumulation during the routine navigation stage, sharp dissipation during strategy determination and execution stages, and partial recovery at the final stage. Strategies aligned with maritime regulations and appropriately timed evasive actions fostered higher trust, while overly early or imminent actions reduced trust. Additionally, a factor analysis consolidated the five trust dimensions, including dependability, predictability, anthropomorphism, faith, and safety, into two aspects: System Competence, encompassing the first four dimensions, and Situational Safety, representing safety-related trust. Furthermore, Bayesian Network (BN) is developed to model trust in the autonomous decision-making of MASS, integrating human observers demographics and situational factors. The model captures sequential trust dependencies, revealing the cascading effects of trust across various stages and the role of System Competence in shaping overall trust in the entire decision-making process. These findings provide actionable insights for designing MASS that support trust-building and optimise collision avoidance strategies, contributing to safer and more efficient autonomous maritime operations.

Objective of this paper is to study how reliability standards, expressed as probabilities of dike segment failure, can be practically updated to improve opportunities for risk-based dike design and planning. The approach to assess the economic optimal flood probability, used by the Dutch Delta Committee (1958, in this paper referred to as Van Dantzig), is adapted to reflect time-dependent effects of a.o. climate change and subsidence. Furthermore, the approach is adapted to reflect overtopping instead of overflow and it is extended to include reinforcements over time. A comparison of the results of the Adapted Van Dantzig approach with the economic optimal probabilities used as input for the recently formalised Dutch standards (2017) is performed for 73 dike segments in the Netherlands, showing good agreement. Following the Adapted Van Dantzig approach, an analytical relation is developed for economic optimal design horizons, dependent on the dike design, and characteristics of load, investment, climate effect, and economic growth. Finally, a dynamic and simple-to-use approach is developed to enable updating of the economic optimal reliability based on a proposed design and investment planning. This can serve to consider whether an existing reliability standard still fits adequately or needs updating.

Collision avoidance in maritime navigation, particularly between autonomous and conventional vessels, involves iterative and dynamic processes. Traditional path planning models often neglect the behaviours of surrounding vessels, while path predictive models tend to ignore ship interaction features essential in collision scenarios. This study proposes a decision-making framework for collision avoidance, particularly focused on the interaction between autonomous and conventional vessels. The framework integrates a human-preference-aware navigational trajectory prediction model into the collision avoidance process, enhancing the decision-making capabilities in dynamic and interactive environments. We first model human-controlled ship navigational preferences using a Long Short-Term Memory (LSTM) autoencoder combined with K-means clustering, by extracting key preferences from ship pairs identified through AIS data. These preferences, which reflect strategic trajectory adjustments in response to collision risks, are then incorporated into trajectory prediction using a Multi-Task Learning Sequence-to-Sequence (seq2seq) attention LSTM model. The predicted trajectories provide a basis for the decision-making framework, including a local path planner and a trajectory tracking controller, designed to dynamically adjust the predicted reference path for collision-free navigation and ensure its accurate tracking. The framework was validated using AIS data from the port of Rotterdam, identifying four distinct navigational preferences by combining an LSTM-autoencoder and clustering techniques and demonstrating improved prediction accuracy compared to other existing models. Simulation tests demonstrate that the framework utilises the predicted trajectories to inform decision-making, ensuring accurate path tracking while dynamically addressing collision risks for autonomous ships. By providing preference-aware and adaptive reference trajectories, the framework reduces the likelihood of MASS trajectory misinterpretation by conventional ships, thereby supporting proactive collision avoidance in mixed waterborne transport environments.

As dams age, operating costs increase and associated risks intensify. Addressing the mechanisms influencing dam economic life is crucial for accurate assessment and operation management. Dam economic life is affected by social and ecological environmental factors, whose types, relationships, and influence degree vary over time. In this study, influencing factors were identified across five dimensions: safety, cost, benefit, social impact, and ecological and environmental impact. Using the Random Forest method, the interaction degrees among individual factors were analyzed. Using on Markov theory, the Decision-making Trial and Evaluation Laboratory (DEMATEL) method, and the Interpretative Structural Modeling Method (ISM), a time-varying analysis model was constructed to reveal the mechanisms of these influencing factors. The model was applied to the Luhun Reservoir in Luoyang City, Henan Province, China. Results showed that during the first 20 years of dam operation, factors such as total reservoir capacity and management level considerably affected dam economic life. Over the next 20 years, total reservoir capacity and population protection became the dominant factors. Dam economic life can be extended using management measures, including improving storage capacity utilization, enhancing silt control, and increasing flood control storage capacity.

The advancement of smart shipping and autonomous navigation relies on Automatic Identification System (AIS) data, which provides essential ship trajectory information. However, raw AIS data lacks semantic context, making behavior annotation crucial for understanding navigation tasks and processes. Existing research faces two challenges: (1) a lack of clarity on which semantics should be abstracted for effective behavior annotation, and (2) insufficient consideration of spatial interactions between ship maneuvering and the navigational environment, particularly topological interactions. These issues complicate data extraction and hinder machine learning-based applications such as explainable trajectory prediction. This paper proposes a comprehensive framework for semantic annotation and indexing of ship behavior. The framework deconstructs ship behavior into a unified data structure using a relational database, where three types of behavior semantics are defined, including atomic, topological, and traffic behavior. Atomic behaviors (e.g., move and stop) are extracted to annotate raw trajectories, while topological behaviors, describing interactions between trajectories and the environment, are modelled using an improved Dimensionally Extended 9-Intersection Model (DE-9IM). The combination of these semantics enables the annotation of higher-level traffic behavior. The model is further evaluated via behavior annotation statistics, demonstrating its effectiveness in annotation and indexing high-level ship behavior.

In recent years, the relationship between academia and the fossil fuel industry has become a focal point of intense debate. This concern arises from the fear that corporate funding might skew research activities. A significant development in this area is the adoption of policies by a Dutch university, and discussions in several others, prohibiting research funded by the fossil fuel industry. These policies aim to safeguard academic freedom and integrity. Despite this, there has been little discussion on the myriad challenges, implications, and possible unintended consequences, particularly in the realm of safety-and-security research. As such, this manuscript delves into the complex transition towards a fossil-fuel-free society, examining it through the lenses of safety science and sociotechnical systems. It emphasizes the vital importance of collective responsibility in ensuring systemic safety and security as we navigate towards achieving the sustainable development goals. This journey requires a delicate balance between the objectives of safety and sustainability, along with a deep understanding of the security implications of decreasing our dependence on the fossil fuel industry. The strategy of distancing academic research from fossil fuel industries, commonly seen as a positive step, also demands a nuanced consideration of its broader impacts, including the setting of precedents for addressing other existential and systemic risks. Instead, we argue for the establishment of robust governance structures rooted in restorative justice principles. Such frameworks can facilitate productive dialogue with underrepresented groups, motivate the fossil fuel industry towards sustainable practices, and safeguard the integrity of scholarly research. This approach not only addresses immediate concerns related to fossil fuels but also lays the groundwork for a more inclusive and equitable model of climate risk research, essential for tackling the multifaceted challenges of our era.

Security risks, such as sabotage and cyberattacks, are an increasing threat to business and government processes. They originate from malicious human action, of which often exact historical information is lacking. Thus, the judgment and assessment of security professionals is the primary input for security risk management, a subjective probabilistic approach. In this study, we explore the information sources professionals, in both the physical and cybersecurity domain, use for this purpose, improving understanding of their daily praxis. Sources of security risk information are collected, their quality and trustworthiness is assessed, and their use is analyzed. Quality is assessed by experienced security practitioners applying the NATO system for intelligence evaluation, with source intention as additional criterion. Actual use is analyzed among security professionals. The results consist of a comparative ranking of both assessed quality and daily use of sources. Experts are ranked first for perceived quality and are also most relied upon in daily praxis, and individual/personal experience comes second. The additional criterion of source intention explained the lower level of use of information from science. This study provides the basis for enhancing security risk management by a more conscious selection of sources.

A reduction in phishing threats is of increasing importance to organizations. One part of this effort is to provide training to employees, so that they are able to identify and avoid phishing emails. Yet further, simulated phishing emails are used to test whether employees will both identify and report a suspicious email. We worked with a partner bank to examine a repository of many thousands of reported emails from a behavioural perspective. We divide reported emails into categories and examine reporting trends over time relative to training and phishing simulation campaigns. Among our findings, the level of reporting of benign emails is comparable to the number of malicious emails reported, and we see indications that training and simulations amplify the reporting of benign emails. Our analysis uncovers reporting patterns for unique reporters per email campaign as a promising indicator for the security-related culture around phishing prevention. Evidence from our analysis informs recommendations, such as providing reporting infrastructure for reporting not only malicious emails, but also benign but suspicious work-related emails, in a manner that minimises the disruption for users erring on the side of caution when assessing emails.

This study focuses on measuring the influence of critical Human and Organizational Factors (HOFs) on human error occurrence in structural design and construction tasks within the context of the Dutch construction industry. The primary research question addressed in this paper concerns the extent of HOFs’ contribution to human error occurrence. To answer this question, the Classical Model for Structured Expert Judgement (SEJ) is employed, enabling experts to provide their judgments on task Human Error Probability (HEP) influenced by different HOFs, which are subsequently aggregated mathematically. SEJ is chosen as a suitable approach due to the limited availability of applicable data in the construction sector. As a result, the impacts of HOFs are quantified as multipliers, representing the ratio between the observed or evaluated task HEP and its baseline value. These multipliers are then compared with corresponding multipliers from existing Human Reliability Analysis methods and studies. The findings reveal that fitness-for-duty, organizational characteristics and fragmentation exhibit the most pronounced negative effects, whereas complexity, attitude and fitness-for-duty demonstrate the most significant positive impacts on task performance. These results offer valuable insights that can be applied to enhance structural safety assurance practices.

This study investigates the enhancement of Maritime Autonomous Surface Ships (MASS) navigation and path-planning through the integration of ontology-based knowledge maps (KM) with the Dynamic Window Approach (DWA), a fusion termed KM-DWA. The ontology-based KM model is important for MASS navigation, offering a framework for situational awareness, including contextual information fusion and decision-making evidence. This research enriches the KM model with collision avoidance rules from the International Regulations for Preventing Collisions at Sea (COLREGs), building upon our previous work on MASS's efficient and COLREGs-compliant navigation in encounter scenarios. The model provides navigational context, covers COLREGs rules and environmental factors, and recommends MASS actions for various scenarios as suggested by COLREGs. Moreover, an adapted DWA, tailored to maritime navigation, accounts for specific constraints and safety measures for MASS, utilising KM-derived situational awareness as constraints in its cost function for path planning. A significant innovation introduced here is a tiered safety distance model featuring proactive, defensive, and collision buffers to ensure rule-compliant and effective collision avoidance. This scheme enables MASS to take timely collision avoidance actions at both proactive and defensive distances, in line with COLREGs recommendations. The effectiveness of the KM-DWA algorithm is validated by comparing it with the basic DWA algorithm in single- and multi-vessel encounter scenarios. The experiment outcomes illustrate the integrated approach's superiority in terms of COLREGs compliance and collision avoidance rate, emphasising its ability to support COLREGs-compliant decision-making and enhance situational awareness in autonomous maritime operations.

The development of advanced ship positioning and intelligent sensing technologies has transformed navigation at sea, moving beyond reliance on captains’ experience and standard routes. The trajectories traversed by ships at sea contain valuable data that can be mined to map maritime transportation networks and inform intelligent navigation systems. Ship trajectory data at scale enables discovery of the underlying network of maritime routes, providing key insights for applications like intelligent navigation, abnormal behavior detection, trajectory prediction, and maritime traffic pattern analysis. This study reviews the development of research on maritime route networks (MRNs) derived from ship trajectory data. It summarizes the technical process to construct a MRN, contrasting approaches for identifying waypoints, extracting routes, and representing the overall maritime traffic network structure. Finally, this study explores potential applications of MRNs and anticipates promising future research directions in this domain.

In recent years, dam failures have occurred frequently because of extreme weather, posing a significant threat to downstream residents. The establishment of emergency shelters is crucial for reducing casualties. The selection of suitable shelters depends on key information such as the number and distribution of affected people, and the effective capacity and accessibility of the shelters. However, previous studies on siting shelters did not fully consider population distribution differences at a finer scale. This limitation hinders the accuracy of estimating the number of affected people. In addition, most studies ignored the impact of extreme rainfall on the effective capacity and accessibility of shelters, leading to a low applicability of the shelter selection results. Therefore, in this study, land-use and land-cover change (LUCC) and nighttime lighting data were used to simulate population distribution and determine the number and distribution of affected people. Qualified candidate shelters were obtained based on screening criteria, and their effective capacity and accessibility information under different weather conditions were quantified. Considering factors such as population transfer efficiency, construction cost and shelter capacity constraints, a multi-objective siting model was established and solved using the non-dominated sorting genetic algorithm II (NSGA- II) to obtain the final siting scheme. The method was applied to the Dafangying Reservoir, and the results showed the following: (1) The overall mean relative error (MRE) of the population in the 35 downstream streets was 11.16 %, with good fitting accuracy. The simulation results truly reflect the population distribution. (2) Normal weather screening generated 352 qualified candidate shelters, whereas extreme rainfall weather screening generated 266 candidate shelters. (3) Based on the population distribution and weather factors, four scenarios were set up, with 63, 106, 73, and 131 shelters selected. These two factors have a significant impact on the selection of shelters and the allocation of evacuees, and should be considered in the event of a dam-failure floods.

Existing models for correlating global mortality rates with underlying country-specific factors overlook the variations in the effects of these factors on mortality across different countries. These may arise from social, cultural, and political complexities which are usually not measurable and are therefore referred to as unobserved heterogeneity in the statistical literature. Unobserved heterogeneity leads to biased parameter estimates in the models, erroneous inferences about the effects of factors contributing to mortalities, and ultimately inefficient policies. In this paper, latent class modelling is proposed for capturing such unobserved heterogeneity on the cases of traffic mortality and COVID-19 mortality. The ‘pyramid’ model of safety management is used as a common framework for model formulation. The proposed latent class model is an extension of the Negative Binomial (NB) model used in risk epidemiology. The model is tested with data from 105 countries, retrieved from international databases, including socioeconomic, infrastructure, exposure, transport, and COVID-19 variables. The results suggest that there exist two (different) latent country classes in both causes of mortality. The probability of a country belonging to a certain latent class is a much more efficient metric of country membership than previous deterministic groupings (e.g. income or geographic). Variables such as the elderly population, the GDP per capita or the level of motorization, have different effects in different country classes; these effects are not identifiable by conventional statistical modelling. The impact of ignoring unobserved heterogeneity in country mortality modelling is shown by comparing the results with those of conventional NB models.

Maritime Autonomous Surface Ships (MASS) have gained much attention as a safer and more efficient mode of transportation and a potential solution to reduce the workload of seafarers. Despite the highly sophisticated autonomous systems that enable MASS to make independent decisions, the presence of humans on board or in the loop of safety management highlights the need for effective human-machine interaction. However, a potentially systematic review of critical aspects of human-MASS interaction has not yet been conducted. In this paper, we aim to fill this gap by reviewing the literature related to human-MASS interaction from four crucial perspectives: the state of the art of human-MASS interaction, situational awareness for MASS, collision avoidance methods for MASS within a mixed waterborne transport system (MWTS), and human trust in MASS. Our review reveals that human-MASS interaction for safety and efficiency mainly focuses on four key aspects: (i) human factors, (ii) available technologies supporting the autonomy of MASS, (iii) system analysis and design for human-MASS interaction, and (iv) potential requirements regarding regulations. Moreover, we provide a detailed discussion of the three fundamental factors that influence human-MASS interaction, including situational awareness, decision-making for MASS in a mixed waterborne transport system, and human trust in the autonomous system of MASS. Finally, based on our analysis, we propose an integrated framework of human-MASS interaction in which these human factors are taken into account. We anticipate that these factors and their interaction will receive more attention to improve the safety and efficiency of MASS.