Risk Prioritization Research Articles

Integrated Fuzzy Technique for Order Preference by Similarity to Ideal Solution and Emotional Artificial Neural Network Model for Comprehensive Risk Prioritization in Green Construction Projects

With the rapid growth of green construction projects (GCPs) in Saudi Arabia, managing the associated risks has become crucial to ensuring project success and sustainability. These projects face a range of challenges, including socio-economic, environmental, and technical risks that need to be carefully identified and prioritized. This study systematically identifies and prioritizes the risks in GCP using a hybrid model combining fuzzy TOPSIS and an Emotional Artificial Neural Network (EANN). The focus of this study is on the risk management of the green construction industry in Saudi Arabia. Based on expert evaluations, low-quality materials and equipment (Likert scale mean is 4.71) and stakeholder resistance to adopting green ideas (4.67) emerged as the most critical risks. The fuzzy TOPSIS analysis assigned the highest weight to risk probability (0.174), followed by outcome (0.137), and vulnerability (0.123). The EANN refined the risk rankings, confirming the importance of these risks. The findings suggest that risk management strategies should prioritize material quality and stakeholder engagement, while environmental risks, ranked lower, are less of a concern. This hybrid model provides a robust framework for effective risk management, with practical implications for enhancing the sustainability and success of GCP.

Bioaccumulation and tissue distribution of pharmaceuticals and their transformation products in fish along the pollution gradients of a wastewater-impacted river

A field study on the occurrence and distribution of forty-three pharmaceutically active compounds (PhACs) in water and fish samples from anthropogenically impacted section of the Sava River (Croatia) was performed to estimate the importance of bioaccumulation for the environmental risk assessment of PhACs. The study was performed using a highly specific LC-MS/MS method, tailored to include the most prominent PhACs from different therapeutic categories as well as their major metabolites and/or transformation products (TPs). The results revealed a widespread occurrence of PhAC residues both in water and fish samples with a large spatial variability reflecting the distance from the dominant wastewater discharges. The most prominent PhAC categories in less polluted upstream part of the river were common psychostimulants caffeine and cotinine, therapeutic opioids and cardiovascular drugs, while in the river section affected by the local municipal and industrial wastewater inputs, antibiotic drugs became clearly predominant, especially in fish tissue samples. The apparent bioconcentration factors (BCFs) of investigated PhACs varied over several orders of magnitude, from 0.02 ± 0.01 Lkg−1 for O-desmethyl tramadol in fish muscle to 784 ± 260 Lkg−1 for terbinafine in fish liver, indicating rather large differences in their bioconcentration potential and affinity to different tissues, with the tissue-specific BCFs increasing in the following order: muscle < gills < gonads < heart < liver < kidneys. The bioconcentration potential of most of the PhACs included in this study was only low to moderate however moderately high BCFs of certain PhACs (e.g. sertraline, terbinafine, loratadine, diazepam and azithromycin) in some tissues should be taken into consideration when assessing their potential environmental risks. Moreover, it was shown that BCFs could be strongly affected by biotransformation in fish. Risk prioritization based on risk quotient (RQ) and ToxPi index, revealed antibiotics, in particular azithromycin, and therapeutic psychoactive substances as the most hazardous pharmaceutical contaminants in the Sava River.

Review of national climate change risk assessment methods for impacts on health and health systems in WHO European Region

Abstract Background Climate change poses significant risks to health and health systems. The European Union climate adaptation strategy sets out the pathway to become climate resilient by 2050 and supports the development of adaptation strategies across all levels of governance. Whilst many European countries have published climate change risk assessments (CCRA), it is unclear to what extent health outcomes have been quantified or prioritised. There is a need to improve methods for CCRA methods to support decision making on adaptation and better quantify future impacts on health. The aim of this study is to review the methods used in national CCRAs for impacts on health and health systems in the WHO European region. Methods National and European level CCRAs were identified from national government websites and European document databases e.g. Climate-ADAPT. The most recently published CCRA from each country was included. Data extracted from CCRAs included climate scenarios used, modelling data, outcomes, types of evidence evaluated, stakeholder involvement, health information, methods for prioritization of adaptation options and urgency scoring. Data extracted from each document was grouped by climate risk or outcome. Results Nearly all WHO EURO countries have conducted a formal assessment of climate risks and over half specifically include risks to health. All countries assessed the urgency to adapt to heat and flooding however, less than half consider the health related risks related to disrupted water supply and quality as well as impacts on food supply and security. The key reported health outcomes identified across countries were mortality, infectious disease, chronic diseases and mental health. Conclusions There is varying progress in the quantification and prioritization of health risks from climate change across Europe. To support better public health decision-making, CCRAs should integrate sub-national and local information for targeted adaptation action.

A multicriteria decision-making model for risk management in an integrated management system

PurposeThis paper proposes a multicriteria model for risk management to identify and assess risks associated with an integrated management system (IMS). The main benefit of the proposed model is its systemic and logical visualization, which may facilitate the understanding of this proposal’s practical application.Design/methodology/approachThe research design consists of four stages: (1) conduct a literature review to establish risk management models in IMS; (2) collect data concerning risk management models in IMS from a large multinational automotive company; (3) propose a multicriteria model to define and assess risks as well as prioritize mitigation actions and (4) apply the proposed multicriteria model to the data collected in case-based research to evaluate the practical viability of the model to contribute to methods traditionally used.FindingsThe results showed that the proposed risk management model contributes to more reliable decision-making in an IMS. The application of the proposed model identified 85 risks in the total processes of the IMS, 31 of which were classified as high risk; thus, priority actions to be taken were defined. The risk classification and prioritization facilitated the implementation of measures to mitigate or eliminate risks, as pointed out by the company managers.Research limitations/implicationsOne of the limitations is the fact that specific knowledge is required to maintain and update the multicriteria decision-making tool used in this study. Another one implies the approach to managing risks under the different ISO standards and sector-specific requirements, since this may require updates and customization of the proposed risk management model.Practical implicationsThe implementation of IMS in contemporary business environments can be supported by a robust risk management approach. In addition, it provides the leadership with a holistic view of multiple aspects related to a company and fosters continuous improvement.Social implicationsThe social implications of this study are assessed indirectly. This study contributes to the improvement of company management models.Originality/valueTraditionally, the methods used for risk management in IMS are usually applied independently of techniques such as failure mode and effect analysis. The model developed in this work enables to manage risks continuously to achieve a systemic view of organizational issues and greater transparency of the processes.

Development of a Flexible Information Security Risk Model Using Machine Learning Methods and Ontologies

This paper presents a significant advancement in information security risk assessment by introducing a flexible and comprehensive model. The research integrates established standards, expert knowledge, machine learning, and ontological modeling to create a multifaceted approach for understanding and managing information security risks. The combination of standards and expert insights forms a robust foundation, ensuring a holistic grasp of the intricate risk landscape. The use of cluster analysis, specifically applying k-means on information security standards, expands the data-driven approach, uncovering patterns not discernible through traditional methods. The integration of machine learning algorithms in the creation of information security risk dendrogram demonstrates effective computational techniques for enhanced risk discovery. The introduction of a heat map as a visualization tool adds innovation, facilitating an intuitive understanding of risk interconnections and prioritization for decision makers. Additionally, a thesaurus optimizes risk descriptions, ensuring comprehensiveness and relevance despite evolving terminologies in the dynamic field of information security. The development of an ontological model for structured risk classification is a significant stride forward, offering an effective means of categorizing information security risks based on ontological relationships. These collective innovations enhance understanding and management of information security risks, paving the way for more effective approaches in the ever-evolving technological landscape.

“I’ve got ninety-nine problems…”: a starting point for investigating the risk prioritisation paradox

It has been shown in several previous studies that the implementation of risk-reducing interventions or adoption of safer behaviours is considerably less amongst individuals with low sociodemographic status. In turn, this has led to an increasing inequality of mortality risk and a considerable challenge for societal actors. Decreasing economic hurdles, that is, making safety interventions cheaper, and increasing knowledge has often been the chosen strategies to reduce inequality and although some success has been shown in terms of specific risks, the overall trend is towards increased inequality. An assumption underlying the sociodemographic differences in prevention has been that individuals with low sociodemographic status have an incorrect understanding of their risk. However, several recent studies have shown that individuals with objectively high risk also perceive their risk as higher therefore raising the question of why—despite having a sound knowledge of risks—they more often refrain from risk-reducing interventions. One hypothesis is that individuals with low sociodemographic status (correctly) experience that they have a higher risk in relation to many different hazards. Given the greater number of risks needing to be handled, these individuals need to prioritise which risks to focus upon, thereby not necessarily prioritising the risks that society would like them to prioritise. Using a large dataset on the self-reported perception of a large number of different risks, the differences between sociodemographic groups are analysed to investigate whether an underlying issue in the inequality of risk is that lower sociodemographic status groups perceive more risks as greater than those of higher sociodemographic status.

Unveiling Priority Emerging PFAS in Taihu Lake Using Integrated Nontarget Screening, Target Analysis, and Risk Characterization.

Amidst tightening regulations, the proliferation of next-generation per- and polyfluoroalkyl substances (PFAS) necessitates a deeper understanding of their environmental fate and potential risks. Here, we conducted a comprehensive assessment of PFAS in the water and sediment of Taihu Lake, incorporating both nontarget and target screening, seasonal and geographical variation analysis, and risk prioritization. A total of 58 PFAS from 13 classes were identified, revealing complex PFAS contamination. In addition to short-chain perfluoroalkyl carboxylates (PFCAs) and sulfonates (PFSAs), bis(trifluoromethanesulfonyl)imide (Ntf2) and perfluoro-2,5-dimethyl-3,6-dioxo-heptanoic acid (C7 HFPO-TA) exhibited relatively high concentrations in water, with median values of 21.7 and 5.72 ng/L, respectively. Seasonal and geographical variation analysis revealed elevated levels of C7 HFPO-TA, Ntf2, and perfluorohexanoic acid (PFHxA) in the northeastern areas, suggesting transport via water diversion project. Multicriteria risk prioritization identified four high priority PFAS (Ntf2, C7 HFPO-TA, PFHxA, and perfluorooctanoic acid (PFOA)) in water and two high priority PFAS (hexafluoropropylene oxide dimer acid (HFPO-DA) and PFHxA) in sediment. Overall, this study revealed Ntf2 and C7 HFPO-TA as priority PFAS in Taihu Lake, underscoring the urgent necessity of evaluating risks associated with these emerging PFAS.

Qualitative prioritization of accident risks in the mining industry

Qualitative prioritization of accident risks in the mining industry

Risk assessment and prioritization using fuzzy FMECA: a case study of dumper breakdowns

Risk assessment and prioritization using fuzzy FMECA: a case study of dumper breakdowns

Situational analysis on porcine diseases: general risk assessment and prioritization of epizootic threats to biosecurity systems of pig establishments in the Russian Federation

The results of the situational analysis on porcine diseases in the Russian Federation and the expert assessment prioritizing the list of porcine pathogens significant for the pig industry of the country are presented. The method applied to analyse the expert estimates in the situational analysis allows for rapid assessment and interpretation of the situation with identification of priority diseases to be further addressed. The calculations demonstrated the sufficient degree of agreement among the experts (coefficient of concordance W = 0.61), and Pearson’s chi-squared test statistic χ2 = 51.33 (≥ 21.02607) indicated that the concordance is not random and the results can be used in subsequent studies. The specific features of epizootiology of the agents of African swine fever, classical swine fever, porcine reproductive and respiratory syndrome that can impact the effectiveness of biosecurity systems of pig establishments, as well as further ways for improving biosecurity management measures are discussed. The overall risk for the pig industry in the Russian Federation that is associated with external sources is currently characterized as permanently high, requiring maintaining risk management measures at the pig establishments by both the managerial staff of the establishments and the State Veterinary Service. It is recommended that biosecurity measures against external threats should focus on diseases such as African swine fever (weight λ = 0.52), porcine reproductive and respiratory syndrome (λ = 0.071), classical swine fever (λ = 0.068) and infections considered emerging for the Russian Federation (λ = 0.05) according to the weights based on the expert estimation results. The biosecurity systems of the establishments should equally address other threats significant for the pig industry of the country: swine enzootic pneumonia, porcine pleuropneumonia (Actinobacillus pleuropneumoniae), Aujeszky’s disease, streptococcosis (Streptococcus suis), porcine circovirus infection, foot-and-mouth disease, leptospirosis, transmissible gastroenteritis, cysticercosis (λ = 0.02…0.05). The improvement of the governmental policy for eradication of African swine fever, porcine reproductive and respiratory syndrome, classical swine fever (including the substantial modification of the existing official pig turnover control, zoning, diagnosis and prevention quality, as well as the implementation of biosecurity standards) is the most significant factor, without which the disease eradication perspective is questionable.

Enterprise risk management in supply chain operation: a fuzzy risk prioritization approach

PurposeTraditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.Design/methodology/approachWe propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.FindingsThe proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.Practical implicationsThe adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.Originality/valueThis study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

Risk Identification, Prioritization, and Mapping for Early Construction Cost Contingency in Transportation Infrastructure Projects

Risk Identification, Prioritization, and Mapping for Early Construction Cost Contingency in Transportation Infrastructure Projects

Cybersecurity for tactical 6G networks: Threats, architecture, and intelligence

Edge intelligence, network autonomy, broadband satellite connectivity, and other concepts for private 6G networks are enabling new applications for public safety authorities, e.g., for police and rescue personnel. Enriched situational awareness, group communications with high-quality video, large scale IoT, and remote control of vehicles and robots will become available in any location and situation. We analyze cybersecurity in intelligent tactical bubbles, i.e., in autonomous rapidly deployable mobile networks for public safety operations. Machine learning plays major roles in enabling these networks to be rapidly orchestrated for different operations and in securing these networks from emerging threats, but also in enlarging the threat landscape. We explore applicability of different threat and risk analysis methods for mission-critical networked applications. We present the results of a joint risk prioritization study. We survey security solutions and propose a security architecture, which is founded on the current standardization activities for terrestrial and non-terrestrial 6G and leverages the concepts of machine learning-based security to protect mission-critical assets at the edge of the network.

Effect driven prioritization of contaminants in wastewater treatment plants across China: A data mining-based toxicity screening approach

A diversity of contaminants of emerging concern (CECs) are present in wastewater effluent, posing potential threats to receiving waters. It is urgent for a holistic assessment of the occurrence and risk of CECs related to wastewater treatment plants (WWTP) on national and regional scales. A data mining-based risk prioritization method was developed to collect the reported contaminants and their respective concentrations in municipal and industrial WWTPs and their receiving waters across China over the past 20 years. A total of 10,781 chemicals were reported in 8336 publications, of which 1037 contaminants were reported with environmental concentrations. While contaminant categories varied across WWTP types (municipal vs. industrial) and regions, pharmaceuticals and cyclic hydrocarbons were the most studied CECs. Contaminant composition in receiving water was closer to that in municipal than industrial WWTPs. Publications on legacy pesticides and polycyclic aromatic hydrocarbons in WWTP decreased recently compared to the past, while pharmaceuticals and perfluorochemicals have received increasing attention, showing a changing concern over time. Detection frequency, concentration, removal efficiency, and toxicity data were integrated for assessing potential risks and prioritizing CECs on national and regional scales using an environmental health prioritization index (EHPi) approach. Among 666 contaminants in municipal WWTP effluent, trichlorfon and perfluorooctanesulfonic acid were with the highest EHPi scores, while 17ɑ-ethinylestradiol and bisphenol A had the highest EHPi scores among 304 contaminants in industrial WWTPs. The prioritized contaminants varied across regions, suggesting a need for tailoring regional measures of wastewater treatment and control.

Prioritisation of emerging and epidemic-prone diseases and risk factors in India’s three western states using the One Health Risk and Disease (OHRAD) prioritisation tool

BackgroundThere have been numerous outbreaks of infectious diseases to date, and out of them, six events have been declared as Public Health Emergencies of International Concern by the World Health Organization (WHO). The outbreaks have been unprecedented, and the sudden anxiety associated with these makes the situation more complicated. Prioritisation of diseases has been a common process in public health, and it is important to identify infectious diseases that are important for human health. In addition, a One Health approach requires taking also animal origin and related risk factors for infectious diseases into account.MethodsState-level co-creation workshops were conducted in Gujarat, Rajasthan, and Maharashtra, the three western states of India. One Health Risk and Disease (OHRAD) prioritisation tool was used to prioritise emerging and epidemic-prone diseases and their risk factors.ResultsThe risk factors and diseases were scored using the OHRAD tool, and the ranks were obtained. Avian flu by A(H5N1), A(H7N9), and A(H9N2), swine flu by A(H1N1), A(H1N2) and A(H3N2), dengue, rabies, leptospirosis, bovine tuberculosis, salmonellosis, chikungunya, soil-transmitted helminths, and brucellosis were some of the 10 diseases prioritised in all the three western states of the country. Similarly, the presence of mosquito breeding sites near houses or workplaces, open defecation, poor hand hygiene, poor personal hygiene, reluctance to follow disease prevention measures and improper carcass disposal were prioritised as the risk factors for diseases in all three states.ConclusionThe emerging and epidemic-prone diseases and risk factors that are prioritised reflect state-specific scenarios and the gravity of the situation concerning risk factors for disease outbreaks. This can help in allocating resources and increasing efforts in the prevention and control of these diseases.

Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes.

Environmental chemicals may contribute to the global burden of cardiovascular disease, but experimental data are lacking to determine which substances pose the greatest risk. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a high-throughput cardiotoxicity model that is widely used to test drugs and chemicals; however, most studies focus on exploring electro-physiological readouts. Gene expression data may provide additional molecular insights to be used for both mechanistic interpretation and dose-response analyses. Therefore, we hypothesized that both transcriptomic and functional data in human iPSC-derived cardiomyocytes may be used as a comprehensive screening tool to identify potential cardiotoxicity hazards and risks of the chemicals. To test this hypothesis, we performed concentration-response analysis of 464 chemicals from 12 classes, including both pharmaceuticals and nonpharmaceutical substances. Functional effects (beat frequency, QT prolongation, and asystole), cytotoxicity, and whole transcriptome response were evaluated. Points of departure were derived from phenotypic and transcriptomic data, and risk characterization was performed. Overall, 244 (53%) substances were active in at least one phenotype; as expected, pharmaceuticals with known cardiac liabilities were the most active. Positive chronotropy was the functional phenotype activated by the largest number of tested chemicals. No chemical class was particularly prone to pose a potential hazard to cardiomyocytes; a varying proportion (10-44%) of substances in each class had effects on cardiomyocytes. Transcriptomic data showed that 69 (15%) substances elicited significant gene expression changes; most perturbed pathways were highly relevant to known key characteristics of human cardiotoxicants. The bioactivity-to-exposure ratios showed that phenotypic- and transcriptomic-based POD led to similar results for risk characterization. Overall, our findings demonstrate how the integrative use of in vitro transcriptomic and phenotypic data from iPSC-derived cardiomyocytes not only offers a complementary approach for hazard and risk prioritization, but also enables mechanistic interpretation of the in vitro test results to increase confidence in decision-making.

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients.

Land use and spatial contiguity are key driven factors of antibiotic multimedia patterns in the megacity river network

The widespread spread of antibiotics in the environment poses a growing threat to human health. This study investigated the distribution and fate of antibiotics concerning land use characteristics, hydrological conditions, and spatial contiguity within a megacity river network. Temporally, the average concentrations of twenty antibiotics in water (354 ng/L), suspended particulate matter (SPM) (46 ng/L), and sediment (151 ng/g) during dry season were notably higher than that in the corresponding environment media (water: 127 ng/L, SPM: 2 ng/L, and sediment: 49 ng/g) during the wet season. Moreover, the inter-annual variation of antibiotics in water showed a decreasing trend. Spatially, substantial antibiotic contamination was observed in a human-intensive watershed, particularly in the upstream and central city sections. The macrolides in water were most affected by land use types and hydrological processes. Antibiotic contamination in water exhibited a stronger spatial autocorrelation compared to other media. Nevertheless, the interconnectedness of antibiotic contamination in sediments during the wet season warrants attention, and relevant authorities should enhance environmental monitoring in watersheds with pollution hotspots. Certain antibiotics, such as sulfamethoxazole, enrofloxacin, and florfenicol, were transported via urban rivers to the ocean, potentially posing environmental risks to coastal water quality. Local sources accounted for the predominant portion (>50 %) of most antibiotics in various media. The correlation distances of antibiotics in waters during the wet season could screen ecological risk prioritization in aquatic environments.

A Fuzzy Decision Support System for Risk Prioritization in Fine Kinney-based Occupational Risk Analysis

Analyzing Occupational Health and Safety (OHS) risks requires prioritizing risk, a vital step in hazard and effect analysis (HEA). Currently, there is no existing approach to determine the priority of risk in HEA-based occupational risk analysis that incorporates interactive risk factors and spherical fuzzy risk information. This paper presents a novel approach to address the constraints by introducing a fresh framework for evaluating job-related hazards using HEA by combining the spherical fuzzy set (SFS), the multi-attributive border approximation area comparison (MABAC) technique, and the Ordered Weighted Averaging (OWA) operator, a more sophisticated approach is achieved within this framework. The SFS is utilized in this framework to depict the more ambiguous and unsure data given by specialists, offering a more efficient approach to handling the fuzzy risk information, encompassing the non-membership degree and hesitation. In addition, an advanced MABAC method is used to prioritize occupational risk. Afterward, we provide a practical instance of utilizing the MABAC technical-oriented risk prioritization approach for evaluating potential occupational dangers in risk analysis.

A novel sequential risk assessment model for analyzing commercial aviation accidents: Soft computing perspective.

Due to the importance of the commercial aviation system and, also, the existence of countless accidents and unfortunate occurrences in this industry, there has been a need for a structured approach to deal with them in recent years. Therefore, this study presents a comprehensive and sequential model for analyzing commercial aviation accidents based on historical data and reports. The model first uses the failure mode and effects analysis (FMEA) technique to determine and score existing risks; then, the risks are prioritized using two multi-attribute decision making (MADM) methods and two novel and innovative techniques, including ranking based on intuitionistic fuzzy risk priority number and ranking based on the vague sets. These techniques are based in an intuitionistic fuzzy environment to handle uncertainties and the FMEA features. A fuzzy cognitive map is utilized to evaluate existing interactions among the risk factors, and additionally, various scenarios are implemented to analyze the role of each risk, group of risks, and behavior of the system in different conditions. Finally, the model is performed for a real case study to clarify its applicability and the two novel risk prioritization techniques. Although this model can be used for other similar complex transportation systems with adequate data, it is mainly employed to illustrate the most critical risks and for analyzing existing relationships among the concepts of the system.