Risk Assessment Model Research Articles

Chinese Family Farm Business Risk Assessment Using a Hierarchical Hesitant Fuzzy Linguistic Model

Chinese family farms are continuously expanding; they are also facing various business risks that lead to a shorter lifespan. This paper constructed a family farm business risk assessment model that combined a hesitant fuzzy linguistic term sets (HFLTS) model with a hesitant fuzzy weighted average (HFWA) operator. On the basis of the factor analysis, this study built a family farm indicator system that included the natural, technical, market, policy, society, and management risk. The HFLTS was used for the assessment of weights in pairwise comparison matrices, and the HFWA operator was used as an aggregation operator to calculate the business risk score of family farms. For our case study, a method comparison analysis was also performed to check the validity of the results obtained by our risk assessment model.

Multipathogen quantitative risk assessment in raw milk soft cheese

We propose a multipathogen Quantitative Microbiological Risk Assessment (QMRA) model to estimate the risk of foodborne illness from bacterial pathogens in raw milk soft cheese. Our work extends an existing QMRA model for pathogenic Shiga toxin-producing Escherichia coli (STEC) (Basak et al.,under review; Perrin et al., 2014) by incorporating the effects of Salmonella and Listeria monocytogenes. This multipathogen model integrates microbial contamination of raw milk at the farm level, as well as the growth and survival of these bacteria during cheese fabrication, ripening, and storage. The public health impact of multipathogen risk associated with raw milk cheese consumption is assessed using Disability-Adjusted Life Years (DALYs). The model evaluates intervention strategies at both pre- and post-harvest stages to estimate intervention costs. Furthermore, it tests various scenarios of these strategies and optimizes intervention parameters to minimize multipathogen risk and associated costs. This article discusses challenges in QMRA model validation, emphasizes model limitations, and explores future perspectives for improvement.

Comparison of Two Previously Validated Risk Assessment and Prediction Tool Models in Extended Length of Stay Patients Following total Hip Arthroplasty

IntroductionPrevious studies have attempted to validate the risk assessment and prediction tool (RAPT) in primary total hip arthroplasty (THA) patients. The purpose of this study was to: (1) identify patients who had an extended length of stay (LOS) following THA; and (2) compare the accuracy of 2 previously validated RAPT models. MethodsWe retrospectively reviewed all primary THA patients from 2014 to 2021 who had a completed RAPT score. Youden’s J computational analysis was used to determine the LOS where facility discharge was statistically more likely. Based on the cut-offs proposed by Oldmeadow and Dibra, patients were separated into high- (O: 1 to 5 versus D: 1 to 3), medium- (O: 6 to 9 versus D: 4 to 7), and low- (O: 10 to 12 versus D: 8 to 12) risk groups. ResultsWe determined that an LOS of greater than 2 days resulted in a higher chance of facility discharge. In these patients (n = 717), the overall predictive accuracy (PA) of the RAPT was 79.8%. The Dibra model had a higher PA in the high-risk group (D: 68.2 versus O: 61.2% facility discharge). The Oldmeadow model had a higher PA in the medium-risk (O: 78.7 versus D: 61.4% home discharge) and low-risk (O: 97.0 versus D. 92.5% home discharge) groups. ConclusionsAs institutions continue to optimize LOS, the RAPT may need to be defined in the context of a patient’s hospital stay. In patients requiring an LOS of greater than 2 days, the originally established RAPT cut-offs may be more accurate in predicting discharge disposition. Level of EvidenceIII Retrospective Cohort Study.

Situational NMAC risk assessment using conditional random field for UAV tiered aerial passage operations

Complex urban unmanned aerial vehicle (UAV) operations necessitate near mid-air collision (NMAC) risk assessment to ensure safety. In this work, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. Firstly, a concept of tiered aerial passages is designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity. For UAV tiered aerial passage operations, a situational NMAC risk assessment framework is formulated, enabling the host UAV to evaluate and respond to real-time risks, ensuring avoidance of potential collisions. Specifically, a probabilistic risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the NMAC risks in overtaking, approaching head-on, and converging UAV situational encounters are quantified considering advisory, caution, and warning risk alert levels. To resolve risks assessed, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed method is validated by testing encounter situations and comparative study. The results show that the proposed method outperforms UAV NMAC risk assessment, effectively tailoring different risk requirements for diverse UAV operations.

Enhancing Environmental Sustainability: Risk Assessment and Management Strategies for Urban Light Pollution

Light pollution imposes significant and far-reaching adverse effects on human society, necessitating its stringent regulation. However, intervention policies could be customized to suit the unique characteristics of each region, taking into account local conditions. To address this challenge, we have developed a comprehensive light pollution risk assessment model using a combination of objective and subjective weighting methods, including analytic hierarchy process (AHP), independent weighting method (IWM), entropy weight method (EWM), coefficient of variation (CV), criteria importance through intercriteria correlation (CRITIC), and principal component analysis (PCA). This model facilitates a systematic evaluation of light pollution risk levels across diverse regions in China. Subsequently, we have proposed intervention policies targeting light pollution risk reduction and assessed their efficacy using the synthetic control method. Our findings reveal elevated light pollution risk levels in coastal and mountainous regions with heightened concentrations closer to urban centers. Strategies focused on enhancing lighting hardware, optimizing lighting schedules, and upgrading light sources demonstrated the impact on reducing light pollution risk levels (LPRL). This study not only lays a solid theoretical foundation for assessing urban light pollution risks but furnishes empirical evidence to aid relevant authorities in formulating effective light pollution control strategies.

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

Development and internal validation of the Cleveland Clinic Bleeding Model to predict major bleeding risk at admission in medical inpatients

BackgroundGuidelines recommend pharmacologic VTE prophylaxis for acutely ill medical patients at acceptable bleeding risk, but only the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model has been validated for bleeding risk assessment. ObjectivesWe developed and internally validated a risk assessment model (RAM) to predict major in-hospital bleeding using risk factors at admission and compared our model with IMPROVE. MethodsWe selected patients admitted to medical services at 10 hospitals in the Cleveland Clinic Health System from 2017 to 2020. We identified major bleeding according to the International Society on Thrombosis and Haemostasis criteria, using a combination of diagnostic codes and laboratory values, and confirmed events with chart review. We fit a least absolute shrinkage selection operator logistic regression model in the training set and compared the discrimination and calibration of our model with the IMPROVE model in the validation set. ResultsAmong 46 314 admissions, 268 (0.58%) had a major bleed. The final RAM included 16 risk factors, of which prior bleeding (odds ratio [OR] = 4.83), peptic ulcer (OR = 3.82), history of sepsis (OR = 3.26), and steroid use (OR = 2.59) were the strongest. The Cleveland Clinic Bleeding Model had better discrimination than IMPROVE (area under the receiver operating characteristics curve = 0.85 vs 0.70; P < .001) and, at equivalent sensitivity (52%), categorized fewer patients as high risk (7.2% vs 11.8%; P < .001). Calibration was adequate (Brier score = 0.0057). ConclusionUsing a large population of medical inpatients with verified major bleeding events, we developed and internally validated a RAM for major bleeding whose performance surpassed the IMPROVE model.

Environment Assessment of Modified Red Mud Utilized in Roadbed

Utilization of red mud in road projects is an effective way to consume large amounts of red mud on a large scale. In order to meet the requirements for road performance, a modified material, Heinchem, has been developed on the basis of extensive experiments, and the long-term environmental risks of red mud modified by this material have been investigated. By collecting and modifying original red mud samples, a series of continuous leaching tank experiments are carried out based on the exposure scenario analysis. According to the leaching content of pollution in the original and modified red mud, the characteristic pollutants are identified. The release mechanism of these characteristic pollutants in the modified red mud is revealed, and the long-term release amount is predicted. Furthermore, in light of the actual road use scenario of the modified red mud, a risk assessment model is established and used to simulate the release, migration, and transformation of characteristic pollutants during the use of modified red mud as roadbed material. The groundwater environmental risk is then assessed. Finally, an acute toxicity test of earthworms and a seed germination test are conducted to investigate the impact of the modified red mud on the farmlands. The results showed that the proposed red mud modified materials have obvious curing effects on V, As, Se, Mo, and F. When the leaching contents of V, Cr6+, Cr3+, As3+, Se4+, Se6+, Mo, and F in the modified red mud were lower than 0.15 mg/L, 0.1 mg/L, 0.2 mg/L, 0.012 mg/L, 0.012 mg/L, 0.012 mg/L, 0.075 mg/L, and 1.2 mg/L, respectively, the environmental risk of modified red mud during long-term road use is acceptable. This study provides a new way for the resource utilization of red mud.

Comparison of machine learning models in credit risk assessment

Crdit risk plays an important rol in financ. In a sns, crdit risk rflcts th stability of financial institutions and th protction of invstors. Du to th impact of th pidmic on th world conomy, w nd to rassss th crdit risk. For a long tim, machin larning and dp larning in statistics hav bn vry ffctiv in prdicting crdit risk. But in machin larning and dp larning, whn prdicting crdit risk, w citd four modls, namly XGBoost, Dcision Tr, Random Forst and Convolutional Nural Ntwork (CNN) modls. Analyz th advantags and disadvantags of ths modls: XGBoost can optimiz th tr modl and prvnt ovrfitting through rgularization, but XGBoost has poor intrprtability. Dcision Tr has strong xplanatory powr, but it is prone to overfitting. Compared with th dcision tr, Random Forsts incras accuracy and rduc th probability of overfitting, but Random Forsts consum mor tim and computing rsourcs. Although Convolutional Nural Ntwork has a high accuracy rat, it abandons intrprtability. Thrfor, in our xprimnts, w found that ths modls ar not prfct and hav thir own dfcts. So, in futur rsarch, w will mak an intgratd modl to includ th advantags of th modl and discard som dfcts, so that th intgratd modl will hav bttr gnralization and accuracy.

Risk assessment models for development of carpal tunnel syndrome: clinical, anthropometric, and neuromuscular ultrasound predictors

BackgroundCarpal tunnel syndrome (CTS) is the most common entrapment neuropathy with multifactorial etiologies. We intended in this case–control study to identify, out of a comprehensive set of risk factors, the relatively important ones and develop a quantitative risk assessment model for this disorder. It was also legitimate to define the hazard of those factors in a dose-related manner and the acceptable safe limit especially for work-related stresses.ResultsAge and female predominance were comparable between the 60 patients (89 hands with electrophysiologically confirmed CTS) and 50 controls (100 hands). Occupation of the studied sample varied between housewives only, employed housewives, and manual workers with a distribution that differed significantly between patients and controls. Significantly higher body mass index (BMI) and mean wrist depth were found in patients than controls. Wrist ratio (clinically or sonographically) was significantly squarer in patients than controls. Overall workload and number of hours spent daily performing work with repeated hand movements or awkward hand position were significantly higher among patients than controls.ROC curves were constructed for wrist measurements and occupational stresses. Cut-off points of wrist ratio and internal carpal tunnel ratio (by ultrasound) to discriminate subjects with CTS were > 0.68 and ≤ 1.854, respectively. The best cut-off value for number of hours spent daily performing work with repeated hand movements was 3 h/ day. As for working with awkward hand position or cold exposure, cut-off value was 0.6 h/day for both.Two logistic regression models were conducted to investigate nonoccupational and occupational predictors of CTS. The independent predictors concluded from the first model were BMI, positive family history of CTS, wrist ratio, and decrease grip strength. As for the occupational model, predictors were tasks requiring awkward hand position and cold exposure.ConclusionOccupational risk assessment by clinical, anthropometric, and ultrasonographic measurement should be used in professions requiring repetitive or awkward hand movements, so that in overweight persons with square wrists appropriate workplace setup measures or assistive technology at work or home could be taken to prevent or decrease the impact of work hazards or help choose individuals with low risk for appropriate jobs.

Study on risk assessment models for the aggregation of vehicles transporting hazardous chemicals

Aggregation of vehicles transporting hazardous chemicals is part of a complex system of hazardous chemical transportation with frequent accidents that threaten public safety. In order to avoid catastrophic consequences in the vehicle aggregation areas, dynamic risk assessment models have been studied in this paper. The macro quantitative risk assessment (M-QRA) model determines the accident probability through the probability of leakage and the probability of induced secondary accidents and takes into account the domino effect to determine the risk in conjunction with the consequence of pool fire, but some data are not readily available. Therefore, in order to accurately assess the risk in case of incomplete data, a rapid quantitative risk assessment (R-QRA) model considering the number of vehicles and the critical mass of hazardous chemicals was developed. In this paper, the M-QRA and R-QRA model were used to calculate the real-time risk of aggregation of vehicles transporting hazardous chemicals respectively, and the two models were compared in terms of calculation accuracy and assessment efficiency. The results show that the risks obtained from the two models are basically consistent, but the R-QRA model is faster in calculation, which is of great practical significance for actual engineering applications and helps safety management.

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.

Hydrochemical characterization and health risk assessment of different types of water bodies in Fenghuang Mountain Area, Northeast China.

Groundwater, as an essential resource, holds significant importance for human production and livelihoods. With the deterioration of the water environment, the issue of groundwater quality has become an urgent international concern. This study focused on the Fenghuang Mountain Area (FMA) and collected a total of 41 sets of samples including pore groundwater (PGW), fissure groundwater (FGW), karst groundwater(KGW), and river water(RW). Hydrochemical analysis methods were employed to identify the hydrochemical characteristics and controlling factors. The entropy-weighted water quality index (EWQI) and health risk assessment model were utilized to assess the groundwater quality and nitrate health risk, respectively. The results indicated that the dominant anion and cation in both groundwater and surface water in the FMA were HCO3- and Ca2+, respectively, with the main hydrochemical type being HCO3-Ca. Groundwater and surface water in the FMA were primarily controlled by rock weathering process, with ion concentrations influenced mainly by the dissolution of halite, sylvite, carbonates (calcite and dolomite), silicates, and gypsum, as well as by reverse anion exchange process. PGW was significantly affected by agricultural activities, with NO3- concentration closely related to human activities. The water quality of FGW was relatively good, with Class I and Class II water accounting for the highest proportion, reaching 84.62%. The high-value area of EWQI in PGW was influenced by human activities. The impact of nitrate health risk on children was significantly greater than on adults, with FGW having the lowest health risk and PGW having the highest health risk. The research results can provide important guarantees for the rational development and utilization of water resources in the FMA and the sustainable development of the economy in Northeast China.

Quantitative Risk Assessment of Wind-Supported Transmission of Highly Pathogenic Avian Influenza Virus to Dutch Poultry Farms via Fecal Particles from Infected Wild Birds in the Environment.

A quantitative microbial risk assessment model was developed to estimate the probability that the aerosolization of fecal droppings from wild birds in the vicinity of poultry farms would result in the infection of indoor-housed poultry with highly pathogenic avian influenza virus (HPAIv) in the Netherlands. Model input parameters were sourced from the scientific literature and experimental data. The availability of data was diverse across input parameters, and especially parameters on the aerosolization of fecal droppings, survival of HPAIv and dispersal of aerosols were uncertain. Model results indicated that the daily probability of infection of a single poultry farm is very low, with a median value of 7.5 × 10-9. Accounting for the total number of poultry farms and the length of the bird-flu season, the median overall probability of at least one HPAIv-infected poultry farm during the bird-flu season is 2.2 × 10-3 (approximately once every 455 years). This is an overall estimate, averaged over different farm types, virus strains and wild bird species, and results indicate that uncertainty is relatively high. Based on these model results, we conclude that it is unlikely that this introduction route plays an important role in the occurrence of HPAIv outbreaks in indoor-housed poultry.

Hazard Identification and Risk Assessment for Sustainable Shipyard Floating Dock Operations: An Integrated Spherical Fuzzy Analytical Hierarchy Process and Fuzzy CoCoSo Approach

Background: This study investigated the process of selecting sustainable safety protocols for floating dock operations in shipyards by identifying potential workplace risks in emergency situations. Thirteen occupational hazards for shipyard floating dock operations were identified through a literature review and expert discussions. Methods: We incorporated four risk elements (consequence: C, frequency: F, probability: P, and number of people at risk: NP) from the Fine–Kinney and Hazard Rating Number System (HRNS) approaches as the risk assessment criteria. We obtained the importance weights of the risk assessment criteria via the Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) and extended the Combined Compromise Solution (CoCoSo) method within the fuzzy framework to prioritize occupational hazards. This study demonstrated the practicality and efficiency of the proposed emergency risk assessment model for shipyard floating dock operations through a case example of occupational risk assessment. Results: The analysis results show that H4 is the occupational hazard with the highest priority, with a score of 3.553. H4 represents the hazard associated with insufficient access to the entire pool area. The second and third most important hazards are the inability of cranes to move freely in and out of the berthing dock and the inability to dispatch emergency teams. These hazards, denoted H1 and H12, follow closely behind with scores of 3.391 and 3.344, respectively. H10 is deemed the least concerning hazard, with a score of 1.802. Conclusions: Professionals can handle complex and uncertain risk assessment data more flexibly using the proposed system, which excels in accurately organizing occupational hazards.

Risk Assessment of TBM Construction Based on a Matter-Element Extension Model with Optimized Weight Distribution

In order to effectively address the potential hazards associated with the construction of Phase II of the YE Water Supply Project’s KS tunnel in Xinjiang, this study employs the WBS-RBS (Work Breakdown Structure and Risk Breakdown Structure) method for risk identification. This approach aims to identify various risks that may arise during TBM (Tunnel Boring Machine) construction. To prevent incomplete risk factor identification resulting from subjective judgment, a risk index system is established based on the identification results. Subsequently, a matter-element extension model is utilized to quantify the risk factors within this index system, and comprehensive weights are determined using variable weight theory to assess construction risk levels. Importance analysis of each index is then conducted to identify those with significant impact on risk evaluation outcomes. Finally, by comparing actual engineering cases with other risk evaluation models, this paper verifies the reliability of its constructed risk assessment model and proposes measures for controlling potential risks based on these evaluations. The paper provides a clear definition of safety risks encountered during TBM construction and conducts comprehensive risk assessments as a valuable reference for research related to the tunnel boring machine construction period in tunnel engineering.

An In-Depth Analysis of 1370 Consecutive Abdominoplasties: Fascial Plication Increases Risk of Venous Thromboembolism, and Scoring for Plication Improves the Caprini Risk Assessment Model.

Venous thromboembolism (VTE) is the most dangerous complication of abdominoplasty. One relatively undefined risk factor is plication, which in theory increases VTE risk. To assess the thromboembolic risk of plication. A retrospective review of 1370 consecutive abdominoplasties by one surgeon was undertaken. Two groups were considered, plicated (n= 1089) and non-plicated (n = 281), and VTE rates were compared between these groups. There were 25/1089 cases of VTE (2.3%) in plicated patients and 1/281 case of VTE (0.36%) in non-plicated patients, which was statistically significant (Fisher's exact test, p=0.028) despite the non-plicated group being a higher risk population. Case-Control matching yielded 225 pairs which differed statistically only by the presence or absence of plication; there were 12/225(5.3%) VTE events in the plicated group and 0/225 in the non-plicated group (McNemar's test, p=0.0015). Logistic regression demonstrated increased VTE risk with increasing age (OR 1.08, p<0.001), BMI (OR 1.34, p=0.002), Caprini score (OR 2.17, p<0.001), and especially plication (OR 16.76, p=0.008). Adding two points for plication to Caprini scores offered better risk stratification at a level of 7, with an improved combination of sensitivity/specificity (0.31/0.98 vs 0.69/0.96) and a 27% improvement over the 2005 Caprini RAM in the area under a Receiver Operating Characteristic (ROC) curve (0.826 vs 0.651, Z-value -3.596, p = 0.003). Plication was shown to be a powerful risk factor for the development of VTE in abdominoplasty. Abdominal wall plication should be considered in risk assessment, and scoring for plication may improve the performance of the Caprini RAM.

Comprehensive Risk Assessment of Farmer Households in Flood and Earthquake Prone Areas Based on Multidimensional Information Diffusion Model

In recent years, the global practice of regional natural disaster reduction and comprehensive risk prevention has promoted the integration of multiple disciplines, and the theory and technology of comprehensive risk assessment for multiple natural disaster chains urgently need innovation. This article is based on multiple natural disaster chain scenarios and sets secondary indicators for multiple influencing factors based on the first level indicators composed of basic risk factors such as hazard factors, vulnerability of disaster bearing bodies, and disaster prevention and reduction capabilities. By using multidimensional information diffusion technology, multiple hazard factor hazard combination factors, vulnerability combination factors of disaster bearing bodies, and comprehensive disaster prevention and reduction capability combination factors reflecting various natural disaster scenarios are constructed, A household comprehensive risk assessment index system for multi-dimensional information diffusion assessment of natural disaster comprehensive risk was designed by quantifying risk factors such as the intensity of comprehensive disaster causing factors, vulnerability of disaster bearing bodies, and comprehensive disaster prevention and reduction capabilities in disaster scenarios. This paper expands the traditional information diffusion comprehensive risk assessment model, makes up for the lack of information on geographical units, establishes a multi impact factor multi dimension natural disaster risk comprehensive assessment model for multi risk factors of complex disaster scenarios with small samples, obtains the theoretical value of multi-dimensional information Diffusion model comprehensive risk assessment for complex disaster scenarios, breaks through the sample size limit of natural disaster risks, and makes up for the lack of information on small samples of small probability events, Improved the reliability and accuracy of comprehensive risk assessment for multiple natural disaster chains. Then, take the disaster scenario formed by the comprehensive risk of earthquake and flood in a place in the upper reaches of the Fujiang River basin as the analysis sample, calculate the comprehensive risk assessment value of flood and earthquake damage in the study area's micro samples (rural households in villages and towns). By comparison, it is found that the comprehensive risk value assessed by the multi-dimensional information Diffusion model has good reliability and high accuracy, which is significantly better than the traditional Kriging interpolation method and geographical weighted regression method. This article provides a comprehensive risk assessment of natural disasters at the micro household level, which can better reflect the diversity and regionality of risks, help clarify the hidden risks of rural natural disasters, and provide scientific basis for government emergency management decision-making.

Health Risk Assessment of Heavy Metal(loid)s in the Overlying Water of Small Wetlands Based on Monte Carlo Simulation.

Heavy metal(loid) (HM) contamination is a significant threat to wetland ecosystem. However, contamination risks of HMs in overlying water of small wetlands, which are easily ignored because of their minor occupancy in an overall area, are nearly unknown. A total of 36 water samples containing six HMs were collected from the urban and rural small wetlands of Urumqi in China, and the contamination levels and probabilistic health risks caused by HMs were assessed using the Nemerow pollution index (NPI) and the health risk assessment model introduced by the US EPA. The results revealed that the average concentration of Hg in the urban and rural small wetlands surpassed the Class II thresholds of the Environmental Quality Standards for Surface Water (GB 3838-2002) by factors of 3.2 and 5.0 times, respectively. The overall contamination levels of HMs in the small wetlands fall into the high contamination level. Results of a health risk assessment indicated that non-carcinogenic health risk of the investigated HMs are found to be lower than the acceptable range for adults, but higher than the acceptable range for children. Meanwhile, As falls into the low carcinogenic risk level, whereas Cd falls into the very low carcinogenic risk level. Overall, HMs in rural small wetlands showed relatively higher contamination levels and probabilistic health risks than that of urban small wetlands. In addition, As was identified as the dominant health risk factor in the overlying water of small wetlands in the study area. Findings of this study provide scientific support needed for the prevention of HM contamination of small wetlands in arid zones.

Quantitative assessment of population risk to tropical cyclones using hybrid modeling combining GAM and XGBoost: A case study of Hainan Province

Global climate change is expected to increase the proportion of intense tropical cyclones (TCs) in the Northwest Pacific. This study focused on how factors, especially extreme events, may affect disaster losses. To address this issue, an event-based multivariate TC risk assessment model, which employs copulas, a generalized additive model, and undersampling extreme gradient boosting decision tree techniques, is developed to enhance the accuracy of disaster loss prediction. The results suggest that on Hainan Island, the rate of the affected population is positively correlated with the maximum wind speed and maximum daily rainfall while negatively correlated with the gross domestic product and elevation. The study also shows that the TC risk in the cities of Hainan Island increases as the return period expands, and each return period scenario shows a unique geospatial distribution of TC risk on Hainan Island, with higher risks in the coastal and eastern regions. These results highlight the importance of implementing effective disaster management strategies to mitigate the impact of severe TCs in the region.