Risk Assessment Framework Research Articles

Framework for urban flood risk assessment – a hydrological modelling approach: the case of Nilambur, Kerala

Framework for urban flood risk assessment – a hydrological modelling approach: the case of Nilambur, Kerala

Fraud Factor Analysis Hexagon in Detecting Financial Report Fraud In Listed Companies in Indonesia: A Systematic Literature Approach

Financial reporting fraud is a significant challenge across industries, undermining stakeholder trust and the reliability of financial data. The Fraud Model Hexagon expands upon earlier models, like the Fraud Triangle and Fraud Diamond, by introducing six elements pressure, opportunity, rationalization, capability, ego, and collusion that offer a comprehensive view of the factors enabling fraud. This literature review systematically examines the model's effectiveness in detecting financial fraud within Indonesian corporations, especially those listed on the Indonesia Stock Exchange or connected to political and governmental entities. Ten studies from 2018 to 2024 were analyzed, focusing on each Fraud Hexagon element's role in fostering fraud. Findings highlight that pressure, opportunity, and collusion are primary drivers of fraud in politically connected sectors, with capability and rationalization further facilitating fraud, while ego emboldens managers to act without fear of repercussions. The Fraud Hexagon provides a nuanced framework for risk assessment, prompting recommendations for stronger internal controls and ethical standards. It also serves as a valuable diagnostic tool for auditors and regulators, particularly in politically sensitive sectors. This study contributes to understanding the social and psychological dimensions of financial fraud and offers a foundation for future cross-industry research.

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

Pressure-Induced Microvascular Reactivity With Whole Foot Loading Is Unique Across the Human Foot Sole.

Foot sole plantar pressure generates transient but habitual cutaneous ischemia, which is even more exacerbated in atypical gait patterns. Thus, adequate post-occlusive reactive hyperaemia (PORH) is necessary to maintain skin health. Plantar pressure regional variance during daily tasks potentially generates region-specific PORH, crucial for ischemic defence. The current work investigated regional PORH across the human foot sole resulting from stance-like loading. A loading device equipped with an in-line laser speckle contrast imager measured blood flux before, during, and after whole-foot loading for 2 and 10 min durations at 15% and 50% body weight. Flux was compared between six regions: the heel, lateral arch, medial arch, and fifth, third, and first metatarsals (MT). Baseline flux was significantly greater in the 1MT and 3MT than all other regions. Loading occluded the heel, 5MT and 3MT more than all other regions. Regional PORH peak, time to peak, area under the curve, and recovery rate were ranked between regions. The 3MT, followed by 5MT, overall had the strongest PORH response, suggesting a heightened protection against ischemia compared to other regions. This work highlights regional variations within a healthy foot, providing a framework for future ulcer risk assessments and interventions to preserve foot health.

A New Framework for Condition and Risk Assessment for Sustainable Management of PT Bridges

A New Framework for Condition and Risk Assessment for Sustainable Management of PT Bridges

Climate Risk Assessment Framework in Real Estate: A Focus on Flooding

Climate change exacerbates flood hazards, resulting in risks to real estate values that should be considered by long-term investors. Flood risk presents two major financial risks: market risk and credit risk. Market risk refers to potential property value loss, and credit risk increases the likelihood of mortgage defaults. However, methods and comprehensive data for quantifying global real estate flood risks are lacking. To address this problem, this paper proposes two flood risk assessment frameworks: the local-oriented approach (LOA) and global-oriented approach (GOA). Two hazard and three vulnerability assessment methods are also introduced to support these frameworks. The LOA vulnerability estimates of regions with complete records are required to support the GOA. Taiwan was selected as an example for the LOA assessment, and the results were used to estimate vulnerability overseas in GOA assessments. The results of case studies for buildings located in four cities in different countries were compared. The proposed framework enables investors and asset owners to globally quantify climate risks in real estate, even when the available data are incomplete. Users can choose the most appropriate approach on the basis of the available data and their tolerance for uncertainty.

A machine learning framework for seismic risk assessment of industrial equipment

The paper aims to propose a novel machine learning framework for seismic risk assessment of industrial facilities. In this respect, a compound artificial neural network model is employed, which is based on two different artificial neural network models in series. The first artificial neural network is a regression model employed to generate samples of a vector-valued intensity measure. The second one is a classification model that is used to predict structural damage, starting from the outcomes of the first artificial neural network model. The datasets used for training and validation of the two artificial neural networks are based on hazard-consistent accelerograms and numerical analyses that are performed with an efficient finite element model of the structure. The methodology entails a preliminary feature selection phase for the identification of the aforementioned vector-valued of intensity measures that better classifies the damage/no-damage condition of the structure. This phase is implemented through the principal component analysis method. Subsequently, the Metropolis–Hastings algorithm is used to generate samples of a selected intensity measure, feeding the first ANN model. In turn, the chosen features are used as input parameters of the second ANN model to generate samples of damage/no-damage events. Using the two ANN in series, the mean annual frequency of exceeding a specific limit state is derived. The proposed framework is validated using a typical multi-storey steel frame, focusing on the seismic risk assessment of a vertical storage tank located at the first floor of the primary structure. The proposed method exhibits some clear advantages of combining numerical models with ANN techniques, mainly related to: a reduced computational time; the avoidance of any prior information on the probabilistic model of fragility curves; and the use of model-driven data.

A novel back analysis framework for the probabilistic risk assessment of subaerial landslide-induced tsunami hazard

A novel back analysis framework for the probabilistic risk assessment of subaerial landslide-induced tsunami hazard

Role of cybersecurity for a secure global communication eco-system: A comprehensive cyber risk assessment for satellite communications

In an age where global connectivity has become pivotal to socio-economic development, satellite communication (SATCOM) systems have become the backbone of modern telecommunication infrastructure. However, the increasing reliance on SATCOM also elevates the potential impact of cyber threats. Cyber risk assessment is a critical component of any satellite communications risk management strategy. It plays a pivotal role in identifying and managing risks to satellite communications, which helps stakeholders isolate the most critical threats and select the appropriate cybersecurity measures. To the best of our knowledge, the field of satellite communications lacks an established framework for cyber risk assessment. Moreover, previous research work has focused only on a limited number of security threats and categories. Therefore, in this paper, we propose a comprehensive risk assessment methodology to qualitatively assess the risk associated with satellite communications cyber threats, following the NIST special publication 800-30: Guide for Conducting Risk Assessments. We analyze existing literature and real-world scenarios to identify potential satellite communications cyber threats and employ the STRIDE threat model for threat modeling. We validate the proposed methodology by performing a risk assessment for the cyber threats identified. Finally, we discuss existing challenges and open research problems for satellite communications cyber risk assessment.

Practical drought risk assessment and management framework: A step toward sustainable modernization in agricultural water management

A proposed framework to assess and manage risks associated with Agricultural Water Off-farm Conveyance and Distribution Systems (AWOCDS), quantify potential failure due to drought-induced scenarios, and provide short to long-term recovery plans to mitigate drought impacts. It makes an intellectual contribution by creating a new proactive risk assessment focusing specifically on AWOCDS as a crucial part of agricultural water management. The risk assessment comprises two components − risk probability and consequence. The former is calculated by analyzing droughts via the streamflow drought index using the DrinC calculator. The latter is determined through AWOCDS’ performance evaluation using hydraulic simulation via HEC-RAS. Additionally, water distribution is assessed spatiotemporally using adequacy and equity indicators, and an integrated consequence is developed by applying the simple additive weighting method. AWOCDS’ rehabilitation and modernization projects worldwide include three practical practices in its risk management component. These practices involve modifying standard operating procedures (SOPs) by updating maintenance, upgrading manual-based operations, and developing a centralized Model Predictive Controller (MPC) for AWOCDS automatic operation. The proposed framework underwent testing in an arid region. Based on the risk assessment results, the AWOCDS operating system failure rate ranges from 70% to 96% during extreme-severe drought scenarios and 53% to 65% during moderate-mild drought scenarios. Risk management analysis shows that a short-term recovery plan reduces the drought risk by 4% monthly and 3% yearly. However, the risk of failure ranges from 50 to 94% in mild to extreme drought scenarios. Upgrading SOPs decreases impact by 4–11% in extreme, 4–7% in moderate-severe and 7–9% in mild-moderate drought. Automating the operating system with MPC reduces impact by 40–43% and 30–56% in mild-moderate and severe-extreme drought scenarios. This proactive-based strategy can help water managers plan for rehabilitation and improve surface water distribution reliability.

Molecular investigation of harmful cyanobacteria reveals hidden risks and niche partitioning in Kenyan Lakes

Despite the global expansion of cyanobacterial harmful algal blooms (cHABs), research is biased to temperate systems within the global north, such as the Laurentian Great Lakes. This lack of diversity represents a significant gap in the field and jeopardizes the health of those who reside along at-risk watersheds in the global south. The African Great Lake, Lake Victoria, is understudied despite serving as the second largest lake by surface area and demonstrating year-round cHABs. Here, we address this knowledge gap by performing a molecular survey of cHAB communities in three anthropogenically and ecologically important freshwater systems of Victoria's Kenyan watershed: Winam Gulf (Lake Victoria), Lake Simbi and Lake Naivasha. We identified a bloom of non-toxic Dolichospermum and toxic Microcystis in the Winam Gulf, with data suggesting sulfur limitation shapes competition dynamics between these two bloom-formers. Though we did not detect a bloom in Naivasha, it contained the largest diversity of cHAB genera amongst the three lakes. In turn, our results indicated methane metabolism may allow non-toxic picoplankton to outcompete cHAB genera, while suggesting Synechococcus spp. serves as a methane source and sink in this system. Lake Simbi exhibited a non-toxic Limnospira bloom at the time of sampling with very low abundances of cHAB genera present. Subsequently, these results were employed to design a cHAB screening and risk assessment framework for local stakeholders. Cumulatively, this work serves to increase cHAB research efforts on the international scale while serving as an impetus for cHAB monitoring on the local scale.

Modeling the health impact of wastewater contamination events in drinking water networks

Pathogen intrusion in drinking water systems can pose severe health risks. To better prepare in planning and responding to such events, computational models that capture the intrusion and health impact dynamics are needed. This study presents a novel benchmark testbed that integrates current knowledge on pathogen transport and fate in chlorinated systems and can assess infection risk from contamination events. The model considers organic matter degradation, chlorine decay mechanisms, pathogen inactivation kinetics, as well as stochastic water demands.We studied modeling of wastewater intrusion events that can occur anywhere within a chlorinated and non-chlorinated network. We applied the Quantitative Microbial Risk Assessment framework focusing on three pathogens: enterovirus, Campylobacter, and Cryptosporidium, and their respective dose-response models. Synthetic household-level water demand time series were used to model the individual water consumption timing and calculate the infection risk (exposure via ingestion).Model outcomes indicate that while chlorination aids mitigation, larger contaminations can still lead to infections due to chlorine resistance (for Cryptosporidium) and chlorine depletion at the contamination point. In our example scenarios, chlorine-susceptible pathogens infected of the downstream population, while chlorine-resistant ones infected the entire downstream population. Enterovirus infection risk is higher, despite the concentrations in the contamination source being lower, due to the lower susceptibility to chlorine than Campylobacter. In non-chlorinated networks, the modeled wastewater contamination events led to infection risk in the total population, depending on the contamination location. Hydraulic uncertainty had a limited influence on infection risk. Furthermore, Campylobacter’s infection risk is more sensitive to the initial concentration in the contamination source whereas enterovirus infection risk to the inactivation rate. The model further indicates that the time window for effective mitigation of the magnitude of a waterborne outbreak is short (within hours).

Mitigating risks in hydrogen-powered transportation: A comprehensive risk assessment for hydrogen refuelling stations, vehicles, and garages

Hydrogen is increasingly seen as a viable alternative to fossil fuels in transportation, crucial to achieving net-zero energy goals. However, the rapid expansion of hydrogen-powered transportation is outpacing safety standards, posing significant risks due to limited operational experience, involvement of new actors and lack of targeted guidelines. This study addresses the urgent need for a tailored comprehensive risk assessment framework. Using Structured What-If (SWIFT) and bowtie barrier analysis, the research evaluates a hypothetical pilot project focusing on hydrogen refuelling stations, vehicles, and garages. The study identifies critical hazards and assesses the adequacy of current risk mitigation measures. Key findings reveal gaps in safety practices, leading to 41 actionable steps and 5 key activities to help new actors manage hydrogen risks effectively. By introducing novel safety guidelines, this research contributes to the development of safe hydrogen use and advances the understanding of hydrogen risks, ensuring its sustainable integration into transportation systems.

Block-level spatial integration of population density, social vulnerability, and heavy precipitation reveals intensified urban flooding risk

Under the context of global warming and rapid urbanization, cities worldwide confront the pressing problem of urban waterlogging, hindering progress towards Sustainable Development Goals. Effective planning and mitigation of urban flooding require a comprehensive understanding of the spatial and temporal patterns of rainfall and risk heterogeneity. However, evaluating urban water-logging risk is challenged by the need for city-scale hydrological simulation and generally lacks comprehensive metrics integrating fine-scale datasets. To address these gaps, we developed a simulation method for urban flood hazards by integrating hydrological models and Random Forest algorithms. We then took Shenzhen, a megacity in China, as a case study, and investigated the spatial patterns of urban flooding risk and its determinants at the block level based on the risk assessment framework represented by Hazards-Exposure-Vulnerability (H-E-V) dimensions. We found that socio-economic indicators exhibited spatial clustering, while hazard-related indicators displayed more dispersed patterns. High-risk areas exhibited a highly heterogeneous spatial pattern, predominantly influenced by vulnerability and exposure factors, as well as the spatial mismatch among the three dimensions. Our results emphasize the importance of integrating spatial heterogeneity of exposure and vulnerability into climate adaptation resource allocation, addressing both current and future demands for effective climate mitigation.

A non-contact quantitative risk assessment framework for translational highway landslides: Integration of InSAR, geophysical inversion, and numerical simulation

A non-contact quantitative risk assessment framework for translational highway landslides: Integration of InSAR, geophysical inversion, and numerical simulation

A spatial source-oriented and probability-based risk-assessment framework for heavy metal and PAH contamination of urban soils in Guangzhou, China

A spatial source-oriented and probability-based risk-assessment framework for heavy metal and PAH contamination of urban soils in Guangzhou, China

ENVIRONMENTAL RISK ASSESSMENT OF TRANSPORTATION INFRASTRUCTURE DEVELOPMENT USING GIS IN LAGOS STATE

Transportation infrastructure development poses significant environmental risks, including habitat disruption, air and water pollution, and climate change. This study develops a GIS-based framework for ecological risk assessment of transportation infrastructure development. This research identifies high-risk areas and prioritizes mitigation measures by integrating spatial analysis, multi-criteria decision analysis, and expert consultations. The GIS-based approach evaluates environmental sensitivity, proximity to ecosystems, and potential hazards. Results provide valuable insights for sustainable transportation planning, policy-making, and ecological management. This study explored its application in solving Environmental Risk Assessment of Transportation Infrastructure Development GIS application in environmental Risk Assessment monitoring has mainly been divided into three aspects: water, soil, and atmosphere. Based on ArcGIS 10.1 software and ArcGIS 9.3.1 version, GIS has been applied in Transportation system monitoring and soil heavy metal concentration monitoring, respectively. A major function of planners is to promote the best use of a community's land and resources for different construction projects; especially critical are infrastructure projects on which economic development relies. Transportation projects typically involve both environmental and economic issues facing a community as it grows and changes. Considerations of sustainability, as well as the widespread use of collaborative planning, design, and construction, require tools that facilitate long-term impact analysis and easy communication among built-environment professionals. Environmental impact assessment (EIA) is instrumental in studying transportation project impacts, but it is a time-consuming process because of the large number of dependent and independent variables involved. A vulnerability grade map and road distribution map were produced, providing an overall vulnerability score for each of the three planning alternatives considered. A road alignment was indicated as the preferred corridor, which was further recommended for a project development and environmental (PD&E) study.

Risk assessment of urban infrastructure vulnerability to meteorological disasters: A case study of Dongguan, China

Effective forecasting and response to meteorological hazards are crucial for safeguarding life, property, and supporting sustainable socioeconomic development. With the rising frequency and severity of meteorological hazards worldwide, this study proposes an enhanced risk assessment framework for urban infrastructure exposed to extreme weather events, with a focus on cascading impacts to critical services such as electricity, communication, and transportation networks (roads and subways). A disaster-loss model is developed to quantify infrastructure vulnerability at various spatial and temporal scales under heavy rainfall conditions, accounting for secondary effects. The model's performance is validated through empirical analysis of a 15-year rainfall event in Dongguan City, China, occurring from September 7–8, 2023. Results indicate the model's ability to predict real-event outcomes with approximately 70% accuracy. This model offers valuable insights for disaster prevention and mitigation strategies, aiding decision-makers in optimizing emergency resource allocation, enhancing disaster response efficiency, and issuing timely public risk warnings to minimize losses.

Riskscapes: A framework for risk assessment in colonial contexts

Risk assessment is a critical aspect of coping with environmental changes. The identification of values at risk — entities, attributes, and ideas that are important to a community — is a key component of a population's ability to resist or adapt to hazards. In colonial contexts, risk assessment must take into account the distinct relationality to the land of Indigenous and non-Indigenous groups and the historicized power relations. Most risk assessment frameworks ignore or oversimplify the cultural heterogeneity of human–environment relationships by using generalized value concepts. The few context-dependent frameworks that have been proposed do not account for different sociocultural groups living on the same land. We propose a spatial-based risk assessment approach inspired by the concept of riskscape, acknowledging diverse perceptions of risk and landscape among different sociocultural groups. We present a risk assessment method eliciting values for different sociocultural groups in their specific contexts using separate valuation methods, and then aggregating them into a joint geospatial interface to highlight convergent and competing interests. Illustrated with the boreal region of northwestern Quebec (Canada), we discuss how the riskscape framework balances Indigenous and non-Indigenous perspectives in a non-hierarchical assessment of values at risk.

Methodological framework for early detection of dengue outbreaks in Tanzania

Abstract Background Dengue fever is a major public health problem because it is a widespread and rapidly transmitted mosquito-borne viral disease. We developed a framework for early detection of dengue outbreaks in Tanzania, starting with detection of suspected and/or confirmed cases, followed by site-specific risk characterization, both useful for planning and prioritizing outbreak interventions or epidemic preparedness. Methods Our initial focus was on the identification and categorization of indicators that are specifically tailored to the early detection and classification of outbreak events. The framework was then evaluated using (i) available cases with syndromic and laboratory-confirmed disease information from ProMED emails using decision tree analysis, and (ii) available historical dengue epidemic data at the local level from 2019 with 6,795 confirmed cases using negative binomial regression analysis adjusted for month and area. Results The laboratory-confirmed diagnosis (dengue yes or no) was consistent with the results of the suspected case classification algorithm for clinically defined syndromic cases. There was strong evidence of an increase in dengue cases with higher site-specific risk (rate ratio = 2.51 (95% CI = [1.76, 3.58])) when regressing confirmed dengue cases in 2019 as the dependent variable and site-specific risk as the independent variable. Conclusions The rapid dengue outbreak risk assessment developed may be useful in controlling the epidemic in Tanzania. The suspected case classification algorithm can be a very useful tool to better assess whether a potential dengue case is present and thus requires laboratory confirmation. The framework can be used to rapidly predict the risk of dengue outbreaks, which is useful for planning and prioritizing interventions or for epidemic preparedness. Key messages • Framework for a novel rapid risk assessment for early detection of dengue outbreaks in Tanzania. • Framework relevant to epidemic preparedness.