Advanced Risk Assessment Research Articles (Page 1)

Flooding is one of the most frequent and destructive natural disasters worldwide, with intensifying socioeconomic and environmental consequences linked to rapid urbanisation and climate change. This review examines flood risk delineation and assessment in Nigeria within a broader Global South perspective, synthesising evidence from peer-reviewed studies that employ remote sensing, GIS-based techniques, and multi-criteria decision analysis. The analysis reveals persistent challenges that undermine effective flood risk management, including incompatible datasets, limited stakeholder participation, and inadequate integration with formal planning systems. To address these gaps, the study introduces the GIS-Integrated Flood Risk Management (GIFRM) Framework, a conceptual model that integrates high-resolution risk mapping, adaptive infrastructure design, sustainable urban planning, and participatory governance. GIFRM advances resilience discourse beyond hazard mapping, offering a practical bridge between science, policy, and implementation by aligning technical geospatial analysis with actionable planning solutions. Comparative case insights from flood-prone countries such as Bangladesh, India, and Kenya highlight transferable strategies, including community-led data integration, modular infrastructure approaches, and localised zoning reforms. The review concludes by critically examining the operational disconnect between advanced geospatial risk assessment and its application in resource-limited, rapidly urbanising settings. It reframes flood risk assessment as an interdisciplinary planning tool with global relevance, delivering lessons for disaster preparedness, urban sustainability, and climate resilience. In the face of escalating hydrometeorological extremes, this research offers applied strategies for embedding GIS technologies into adaptive policy frameworks, positioning flood risk management as a core driver of sustainable development.

Tsunamis pose significant threats to South Chinas coastal cities, with the Manila subduction zone as the primary risk source, where 2500-year return period wave heights exceed 3m in high-risk areas. While recent studies advanced risk assessment, analyzed coastal risk, explored Pearl River Estuary terrain effects, and proposed a full-chain framework, they lack integration with building layout optimization. This study develops a layout optimization framework for these cities: it uses Lius framework to zone areas into three risk levels (high: >3m wave/2m inundation; medium: 13m/0.52m; low: <1m/<0.5m) by integrating multi-source data, and links to evacuation research. Results propose targeted strategies: high-risk zones need strict density control and coastal green buffers; medium zones require reduced spacing and wave-resistant materials; low zones prioritize evacuation alignment. The framework bridges risk assessment and planning, providing a scientific basis for coastal resilience, though it relies on existing data.

<span lang="EN-US">Standard risk assessment approaches are sometimes time-consuming and subjective. In order to overcome these challenges an innovative method will be presented in this article by mixing sentiment analysis and machine learning (ML). The suggested technique improves the effectiveness, precision, and scope of risk insights when it comes to the detection of feelings in logs via the use of automated data collection. The research examines several different ML classifiers and makes use of a deep learning model that has been pre-trained to evaluate risks in logs that are multi-linguistic. This proves the adaptability and scalability of our technique when used in a multilanguage setting. This combination of sentiment analysis and ML are a significant advancement in comparison to traditional approaches since it enables real-time processing and delivers important insights into the management of organizational risks.</span>

Assessing cold exposure risk during cold waves in Beijing using high spatiotemporal resolution population data and temperature variations.

Microplastic pollution in water and sediment in the Henan section of the Yellow River based on the MultiMP comprehensive evaluation method.

An advanced cybersecurity risk assessment framework: Integrating vulnerabilities and exploitation techniques for systematic attack path analysis in multilayered power system networks

Application of the FUCOM-SAW model for comprehensive risk assessment of contaminated sediments: A case study of the great Bačka Canal and Begej river.

Introduction/Objective: Hazard analysis as one of the main study subjects in ergo nomics and occupational health and safety (OHS) risk assessment is a critical requirement for ensuring the health and safety of workers in work environments. Current hazard analysis ap proaches in the literature may have some shortcomings. This study aims to provide more relia ble assessments in the hazard analysis process by overcoming the shortcomings of classical ap proaches. Methods: This study proposes a new hazard analysis approach based on the integration of the Fermatean Fuzzy set and the multi-criteria decision-making (MCDM) method MOORA. Results: The proposed approach is used to perform a hazard analysis of a company operating in the metal industry, which is one of the sectors where occupational accidents and occupational diseases most often occur. As a result of the application, the hazards and associated risks in the aluminum metal company are prioritized. Conclusion: This study provides an advanced risk assessment technique for ergonomists and OHS professionals to make better decisions in hazard analysis studies.

Advanced Traffic Conflict Detection and Risk Assessment Using Multi-Scale Video Analysis: A YOLOv8 Modified and Attention-Enhanced Safety Metrics Evaluation

Integrating Global Path Information With Advanced Risk Assessment: An Enhanced Potential Field Method for Intelligent Connected Vehicles Local Path Planning

The increasing frequency and intensity of hydrological events driven by climate change, particularly floods, present significant challenges for the design, construction, and maintenance of bridges and culverts. Additionally, the inadequate capacity of existing structures has resulted in substantial financial burdens on governments due to flood-related damages and the costs of their rehabilitation and replacement. A further concern is the oversight of existing hydraulic design standards, which primarily emphasize structural capacity and flood height, often overlooking broader social and environmental implications as two main pillars of sustainability. This oversight becomes even more critical under changing climatic conditions. This paper proposes a flood risk-based framework for the sustainable design, construction, and modification of bridge and culvert infrastructure in response to climate change. The framework integrates flood risk modeling with environmental and socio-economic considerations to systematically identify and assess vulnerabilities in existing infrastructure. A multi-criteria analysis (MCA) approach is employed to rapidly evaluate and integrate climate change, social, and environmental factors, such as population density, industrial activities, and the ecological impacts of floods following construction, alongside conventional hydrologic and hydraulic design criteria. The study utilizes hydrologic and hydraulic analyses, incorporating transportation networks (including roads, railways, and traffic) with socio-economic data through a GIS-based flood risk classification. Two case studies are presented: the first prioritizes the replacement of existing main bridges and culverts in the Ankara River Basin using the proposed MCA framework, while the second focuses on substructure sizing for a planned high-speed railway section in Mersin–Adana–Osmaniye–Gaziantep, Türkiye, accounting for climate change and upstream reservoirs. The findings highlight the critical importance of adopting a comprehensive and sustainable approach that integrates advanced risk assessment with resilient design strategies to ensure the long-term performance of bridge and culvert infrastructure under climate change.

Subject. The article deals with the development of advanced risk assessment method for retail investment products (mutual funds, ETFs, and structured notes) purchased on the Russian stock market. Objectives. The main goal is to create a qualitative stress testing model that takes into account the interdependence of risk factors. Methods. The study employs machine learning modeling, in particular, the vector error correction model (VECM) method enabling to take into account the non-stationarity of time series and cointegration relationships between macroeconomic indicators. Statistical tests for stationarity, multicollinearity, and cointegration were conducted to select the factors. We applied the cross-validation approach and calculation of the forecast error using the MAPE metric. Results. We selected and trained a model based on the vector error correction model method. It helped consider the non-stationarity of time series and interrelationship of risk factors, which made the estimates of other models incorrect. The final mean absolute percentage error was 19.41%, which means that the forecast is highly accurate. The risk factors involved in the modeling were inflation [1] (both food basket and wages), exchange rates, oil and the key rate, given their mutual influence on each other [2]. Conclusions. The offered method will enable a deeper and more comprehensive stress test of retail investment products based on the vector error correction model machine learning method. The integrated approach to assessing risk factors will increase investor protection by better informing about the maximum level of losses under adverse market conditions.

Abstract - Cyclones are powerful natural disasters that frequently impact the coastal regions of Tamil Nadu, causing significant socio-economic and infrastructural damage. The 2024 Fengal Cyclone severely affected the Cuddalore and Viluppuram districts, highlighting the urgent need for advanced disaster risk assessment and management strategies. This study assesses the impact of the Fengal Cyclone in Tamil Nadu using Geographic Information System (GIS) techniques, specifically ArcGIS, to map and analyze cyclone vulnerability zones. By integrating key physical parameters such as slope, elevation, land use and land cover, drainage density, and precipitation, the research identifies and classifies flood risk zones along the coast. The resultant thematic maps provide spatial insights into cyclone risk, enabling more effective disaster preparedness, mitigation planning, and resource allocation. The findings support local authorities, urban planners, and disaster management teams in developing targeted interventions to reduce the adverse effects of future cyclonic events in the region. Keywords - Cyclone, GIS Technique, Flood risk zone map.

This article aims to summarize the previous researches, and explore the application of Systematic Risk and Idiosyncratic Risk in CAPM model. The paper talks about Critical Analysis of CAPM Assumptions, Beta Calculation and Interpretation, and gives some Examples from Empirical Data to analyze the authors main viewpoints toward the application of systematic risk and idiosyncratic risk. In summary, this research underscores the significance of systematic risk in the context of calm market conditions and offers valuable insights for both academic and practical domains. It lays the groundwork for future studies and applications that aim to enhance risk management and investment strategies in the face of ever-evolving financial landscapes. And the paper recommends that future research should delve deeper into the mechanisms of systematic risk during periods of market calm. It suggests exploring the application of advanced risk assessment tools and the development of dynamic investment strategies that can adapt to changing market conditions.

The increasing interconnectedness and complexity of global financial markets have increased the stakes for advanced risk assessment methods. Traditional financial stress testing based on static rule-based models, historic datasets, and past crisis data which is poorly suited to address the nonlinear relationships and rapidly evolving risk factors characteristic of modern economies. This paper explores the use of Artificial Intelligence (AI) in financial stress testing with machine learning (ML), deep reinforcement learning (DRL), and generative AI to simulate systemic economic shocks and predict financial instability better. It also considers social media activities, geopolitical situations, climate change, pandemics, global financial markets, emerging technologies. This study provides a mechanized AI-based implementation plan for financial stress testing, data engineering pipeline profiling, model selection methodologies (LSTMs, GANs, and XGBoost), and real-time risk monitoring approaches. Financial institution case studies such as the Federal Reserve, Bank of England, and hedge funds such as BlackRock show how AI enhances prediction accuracy, reduces risk assessment cycles, and provides real-time financial crisis management approaches. In addition, this paper also opens up the prospects of Quantum AI, DeFi risk modeling, and digital twins powered by AI to revolutionize systemic risk analysis and crisis forecasting in finance. Our findings show that AI-powered financial stress tests are capable of significantly enhancing risk resilience, early warning, and global financial stability. Studies on XAI methods, audit architectures under regulatory directives, and the combination of quantum computing with AI-powered financial modeling for enhancing financial sector risk assessment even further is a direction that should be explored in future research.

This technical article examines the transformative impact of predictive analytics and banking systems integration on the financial sector, with a particular focus on two exemplary implementations: PenFed Credit Union's PANGEN Project for credit card processing and the International Finance Corporation's iPortal and iDesk applications. This article explores how these innovations enhance core banking functionalities through advanced risk assessment algorithms, personalized credit offerings, cloud-based architectures, and API-driven integration. Additionally, the article investigates how AI-driven due diligence, smart fund disbursement, and real-time monitoring capabilities are revolutionizing fund management processes from initiation to disbursement, ultimately delivering improved operational efficiency, regulatory compliance, and customer experience in modern banking environments.

For many years, steel moment-resisting frames (SMRF) with welded beam-column connections were thought to be the best lateral-force-resisting system for buildings in high-seismic regions. However, the 1994 Northridge earthquake revealed the important vulnerabilities of what are now referred to as pre-Northridge connections, which experienced unanticipated brittle fractures in many buildings in the Los Angeles Metropolitan Region. There are several tall SMRF buildings with pre-Northridge connections worldwide, raising serious safety concerns about their performance during future earthquakes. To investigate possible undiscovered damage in these types of connections, an advanced probabilistic regional seismic risk and damage assessment is conducted on 97 tall SMRF buildings in the Financial District of San Francisco, California, with pre-Northridge beam-column connections that were subjected to the 1989 Loma Prieta earthquake. This study aims to identify the buildings, floor levels, and orientations more likely to have experienced brittle fractures during the Loma Prieta earthquake. Results indicate that despite this earthquake being only moderate in magnitude with an epicenter approximately 95 km away from the Financial District, peak inter-story drift ratios in the tall buildings reach 0.65%. Median peak probabilities of yielding and fracture of beam-column connections do not exceed 37% and 12%, respectively. As a result of ground motion directionality and differences in grid plan in the city, buildings located south of Market Street experienced considerably greater building responses and probabilities of damage than buildings located north of Market Street. Estimates of damage from this study suggest that some pre-Northridge beam-column connections likely fractured during the Loma Prieta earthquake, but the fractures were not as widespread as in the 1994 Northridge earthquake. However, earthquakes with either higher magnitudes or closer source-to-site distances to the city of San Francisco may cause significant damage to SMRF buildings constructed before the Northridge earthquake.

As climate change intensifies, urban populations face growing threats from frequent and severe heatwaves, underscoring the urgent need for advanced risk assessment frameworks to inform adaptation strategies. This systematic review synthesizes methodological innovations in urban heatwave risk assessment (2007–2024), analyzing 259 studies through bibliometric analysis (CiteSpace 6.4.R1) and multi-criteria evaluation. We propose the hazard–exposure–vulnerability–adaptability (HEVA) framework, an extension of Crichton’s risk triangle that integrates dynamic adaptability metrics and supports high-resolution spatial analysis for urban heatwave risk assessment. Our systematic review reveals three key methodological gaps: (1) Inconsistent indicator selection across studies; (2) limited analysis of microclimatic variations; (3) sparse integration of IoT- or satellite-based monitoring. The study offers practical solutions for enhancing assessment accuracy, including refined weighting methodologies and high-resolution spatial analysis techniques. We conclude by proposing a research agenda that prioritizes interdisciplinary approaches—bridging urban planning, climate science, and public health—while advocating for policy tools that address spatial inequities in heat risk exposure. These insights advance the development of more precise, actionable assessment systems to support climate-resilient urban development.

Risk assessment in tunnel construction using the drilling and blasting method presents a complex multi-criteria decision-making challenge due to numerous interacting factors. This study develops an advanced risk assessment model integrating game theory-based combination weighting with nonlinear fuzzy analytic hierarchy process (FAHP). The methodology establishes a comprehensive risk evaluation system through the systematic coupling of a work breakdown structure (WBS) and a risk breakdown structure (RBS), effectively combining subjective weights from an analytic hierarchy process (AHP) with objective weights derived through principal component analysis (PCA). A specialized nonlinear operator addresses the inherent fuzziness in the risk evaluation processes. The model is applied to the Daliangshan No. 1 Tunnel flat guide entrance drilling and blasting construction section, with the risk level determined to be high. Detailed analysis further revealed that the detonation network reliability and ventilation system performance constituted the most significant secondary risk elements. Comparative validation demonstrates the model’s superior accuracy over conventional methods in both weight determination and risk classification. The results demonstrate the effectiveness of the proposed model in improving risk assessment accuracy and supporting decision-making in complex tunnel construction environments.

With the development of society and the advancement of technology, the application of electricity in modern life has become increasingly widespread. However, the risk of electrical fires has also significantly increased. This paper thoroughly investigates the causes, classifications, and challenges of electrical fires in special environments, and summarizes advanced detection and extinguishing technologies. The study reveals that the causes of electrical fires are complex and diverse, including equipment aging, improper installation, short circuits, and overloading. In special environments such as submarines, surface vessels, and aircraft, the risk of electrical fires is higher due to limited space, dense equipment, and difficult rescue operations. This paper also provides a detailed analysis of various types of electrical fires, including cable fires, electrical cabinet fires, transformer fires, battery fires, data center fires, and residential fires, and discusses their characteristics and prevention and control technologies. In terms of detection technology, this paper summarizes the progress of technologies such as arc detection, video detection, and infrared thermography, and emphasizes the importance of selecting appropriate technologies based on specific environments. Regarding extinguishing technologies, this paper discusses various means of extinguishing, such as foam extinguishing agents, dry powder extinguishing agents, and fine water mist technology, and highlights their advantages, disadvantages, and applicable scenarios. Finally, this paper identifies the limitations in the current field of electrical fire prevention and control, emphasizes the importance of interdisciplinary research and the development of advanced risk assessment models, and outlines future research directions.