Comprehensive Risk Assessment Model Research Articles

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. However, this surgical procedure comes with inherent risks and effectively managing these risks to minimize the likelihood of complications arising from organ transplantation (maximizing life years from transplant and quality-adjusted life years) is crucial. To facilitate this process, risk ranking is used to identify and promptly address potential risks. Over recent years, considerable efforts have been made, and various approaches have been proposed to enhance Failure Modes and Effects Analysis (FMEA). In this study, taking into account the uncertainty in linguistic variables (F-FMEA), we introduce an approach based on Fuzzy Multi Criteria Decision Making (F-MCDM) for effectively evaluating scenarios and initial failure hazards. Nevertheless, the results of ranking failure modes generated by different MCDM methods may vary. This study is a retrospective study that suggests a comprehensive unified risk assessment model, integrating multiple techniques to produce a more inclusive ranking of failure modes. Exploring a broad spectrum of risks associated with organ transplant operations, we identified 20 principal hazards with the assistance of literature and experts. We developed a questionnaire to examine the impact of various critical factors on the survival of transplanted organs, such as irregularities in immunosuppressive drug consumption, inappropriate dietary habits, psychological disorders, engaging in strenuous activities post-transplant, neglecting quarantine regulations, and other design-related factors. Subsequently, we analyzed the severity of their effects on the durability of transplanted organs. Utilizing the Mamdani algorithm as a fuzzy inference engine and the Center of Gravity algorithm for tooling, we expressed the probability and severity of each risk. Finally, the failure mode ranking obtained from the F-FMEA method, three fuzzy MCDM methods, and the proposed combined method were identified. Additionally, the results obtained from various methods were evaluated by an expert team, demonstrating that the highest consistency and effectiveness among different methods are attributed to the proposed method, as it achieved a 91.67% agreement with expert opinions.

Proposal of a comprehensive risk assessment model for cut slope considering long-term deterioration characteristics based on the concept of disaster immunity

In Japan, the stability of cut slopes alongside expressways has deteriorated over time due to factors such as weathering, rendering them susceptible to significant collapses, especially during heavy rainfall. This study presents an evaluation model to assess the stability of cut slopes, with the aim of capturing their long-term deterioration characteristics using the logistic regression method. Additionally, to explore variations in deterioration patterns among cut slopes in diverse regions with varying geological compositions, a hierarchical Bayesian model is utilized to estimate parameters in the logistic regression model. This methodology enables the creation of a novel stability probability evaluation model for cut slopes that is adaptable to regions with different geological structures. Moreover, to comprehensively evaluate changes in the disaster resistance capabilities of extensively used cut slopes, the post-disaster recovery capabilities in the slope region are assessed through a comprehensive weighted evaluation method (EWM-TOPSIS). Building on this assessment, a novel, simplified, and more generalized resilience evaluation method is proposed. By integrating stability probability and resilience, an evaluation metric named "disaster immunity" is introduced to measure alterations in the disaster risk resistance of extensively utilized cut slopes. A case study is presented to illustrate the application of the evaluation model on selected cut slopes from various regions. This evaluation framework can assist road managers in predicting the disaster risk status of extensively used cut slopes and identifying priority slopes for implementing preventive measures to mitigate potential losses in the event of slope disasters.

Comprehensive risk assessment of typhoon disasters in China's coastal areas based on multi-source geographic big data

Typhoons can bring substantial casualties and economic ramifications, and effective prevention strategies necessitate a comprehensive risk assessment. Nevertheless, existing studies on its comprehensive risk assessment are characterized by coarse spatial scales, limited incorporation of geographic big data, and rarely considering disaster mitigation capacity. To address these problems, this study combined multi-source geographic big data to develop the Comprehensive Risk Assessment Model (CRAM). The model integrated 17 indicators from 4 categories of factors, including exposure, vulnerability, hazard, and mitigation capacity. A subjective-objective combination weighting method was introduced to generate the indicator weights, and comprehensive risk index of typhoon disasters was calculated for 987 counties along China's coastal regions. Results revealed a pronounced spatial heterogeneity of the comprehensive typhoon risk, which exhibited an overall decreasing trend from the southeast coastal areas toward the northwest inland territories. 61.7 % of the counties exhibited a medium-to-high level of comprehensive risk, and counties with very-high risks are predominantly concentrated in the Shandong Peninsula, Yangtze River Delta, Hokkien Golden Triangle, Greater Bay Area, Leizhou Peninsula, and Hainan Province, mainly due to high exposure and hazard factors. The correlation coefficient between the risk assessment results and typhoon-induced direct economic losses reached 0.702, indicating the effectiveness and reliability of the CRAM. Meanwhile, indicators from intrinsic attributes of typhoons and geographic big data had pronounced importance, and regional mitigation capacity should be improved. Our proposed method can help to scientifically understand spatial patterns of comprehensive risk and mitigate the effects of typhoon disasters in China's coastal regions.

An Integrated Risk Assessment Methodology of In-Service Hydrogen Storage Tanks Based on Connection Coefficient Algorithms and Quintuple Subtraction Set Pair Potential

At present, there have been a number of hydrogen storage tank explosions in hydrogen filling stations, causing casualties and property losses, and having a bad social impact. This has made people realize that the risk assessment and preventive maintenance of hydrogen storage tanks are crucial. Therefore, this paper innovatively proposes a comprehensive risk assessment model based on connection coefficient algorithms and quintuple subtractive set pair potential. First of all, the constructed index system contains five aspects of corrosion factors, material factors, environmental factors, institutional factors and human factors. Secondly, a combined weighting analysis method based on FAHP and CRITIC is proposed to determine the weight of each indicator. The basic indicators influencing hydrogen storage tanks are analyzed via the quintuple subtraction set pair potential and full partial connection coefficient. Finally, the risk level and development trend of hydrogen storage tanks in hydrogen filling stations are determined by a combination of the three-category connection coefficient algorithms and the risk level eigenvalue method. The results of our case analysis show that the proposed risk assessment model can identify the main weak indicators affecting the safety of hydrogen storage tanks, including installation quality, misoperation and material quality. At the same time, it is found that the risk of high-pressure hydrogen storage tanks is at the basic safety level, and the development trend of safety conditions holds a critical value. The evaluation results can help establish targeted countermeasures for the prevention and maintenance of hydrogen storage tanks.

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

Construction and Validation of a Nomogram to Predict the Postoperative Venous Thromboembolism Risk in Patients with HGSOC.

Venous thromboembolism (VTE) is a common complication in patients with high-grade serous ovarian cancer (HGSOC) after surgery. This study aims to establish a comprehensive risk assessment model to better identify the potential risk of postoperative VTE in HGSOC. Clinical data from 587 HGSOC patients who underwent surgical treatment were retrospectively collected. Univariate and multivariate logistic regression analyses were performed to identify independent factors influencing the occurrence of postoperative VTE in HGSOC. A nomogram model was constructed in the training set and further validated in the verification set. Logistic regression identified age (odds ratio [OR] = 1.063, P = .002), tumor size (OR = 3.815, P < .001), postoperative transfusion (OR = 5.646, P = .001), and postoperative D-dimer (OR = 1.246, P = .003) as independent risk factors for postoperative VTE in HGSOC patients. A nomogram was constructed using these factors. The receiver operating characteristic curve showed an area under the curve (AUC) of 0.840 (95% confidence interval [CI]: 0.782, 0.898) in the training set and 0.793 (95% CI: 0.704, 0.882) in the validation set. The calibration curve demonstrated a good consistency between model predictions and actual results. The decision curve analysis indicated the model benefits at a threshold probability of less than 70%. A nomogram predicting postoperative VTE in HGSOC was established and validated. This model will assist clinicians in the early identification of high-risk patients, enabling the implementation of appropriate preventive measures.

Integration of ESG Factors in China's Coffee Industry and Identification of Financial Risks

With the increasing concern for sustainable development in the world, ESG (environmental, social and corporate governance) factors are playing a more and more important role in enterprise management. The purpose of this study is to explore the influence of the integration of ESG factors in China's coffee industry on financial risk identification. By systematically integrating ESG factors such as environmental friendliness, social responsibility and corporate governance, we build a comprehensive financial risk assessment model to identify the potential risks faced by coffee enterprises more comprehensively and accurately. In the method, CatBoost algorithm is used to identify financial risks, and ADASYN algorithm is used to optimize the training set to improve the sensitivity of the model to default risks. The research results show that through the integration of ESG factors, our model has achieved remarkable results in financial risk identification. The Accuracy value reached 0.9671, 12 out of 15 default samples were accurately predicted, and the identification of non-default companies also had strong accuracy, only 2 of them were misjudged. This shows that ESG factors not only have an important impact on sustainable management in the coffee industry, but also can provide strong support for enterprises in identifying financial risks.

Elucidating the Influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma.

Clear cell renal carcinoma (ccRCC) stands as the prevailing subtype among kidney cancers, making it one of the most prevalent malignancies characterized by significant mortality rates. Notably,mitochondrial permeability transition drives necrosis (MPT-Driven Necrosis) emerges as a form of cell death triggered by alterations in the intracellular microenvironment. MPT-Driven Necrosis, recognized as a distinctive type of programmed cell death. Despite the association of MPT-Driven Necrosis programmed-cell-death-related lncRNAs (MPTDNLs) with ccRCC, their precise functions within the tumor microenvironment and prognostic implications remain poorly understood. Therefore, this study aimed to develop a novel prognostic model that enhances prognostic predictions for ccRCC. Employing both univariate Cox proportional hazards and Lasso regression methodologies, this investigation distinguished genes with differential expression that are intimately linked to prognosis.Furthermore, a comprehensive prognostic risk assessment model was established using multiple Cox proportional hazards regression. Additionally, a thorough evaluation was conducted to explore the associations between the characteristics of MPTDNLs and clinicopathological features, tumor microenvironment, and chemotherapy sensitivity, thereby providing insights into their interconnectedness.The model constructed based on the signatures of MPTDNLs was verified to exhibit excellent prediction performance by Cell Culture and Transient Transfection, Transwell and other experiments. By analyzing relevant studies, we identified risk scores derived from MPTDNLs as an independent prognostic determinant for ccRCC, and subsequently we developed a Nomogram prediction model that combines clinical features and associated risk assessment. Finally, the application of experimental techniques such as qRT-PCR helped to compare the expression of MPTDNLs in healthy tissues and tumor samples, as well as their role in the proliferation and migration of renal clear cell carcinoma cells. It was found that there was a significant correlation between CDK6-AS1 and ccRCC results, and CDK6-AS1 plays a key role in the proliferation and migration of ccRCC cells. Impressive predictive results were generated using marker constructs based on these MPTDNLs. In this research, we formulated a new prognostic framework for ccRCC, integrating mitochondrial permeability transition-induced necrosis. This model holds significant potential for enhancing prognostic predictions in ccRCC patients and establishing a foundation for optimizing therapeutic strategies.

A Bayesian Network Model for Risk Management during Hydraulic Fracturing Process

The escalating production of shale gas and oil, witnessed prominently in developed nations over the past decade, has sparked interest in prospective development, even in developing countries like Algeria. However, this growth is accompanied by significant opposition, particularly concerning the method of extraction: hydraulic fracturing, or ‘fracking’. Concerns regarding its environmental impact, water contamination, greenhouse gas emissions, and potential health effects have sparked widespread debate. This study thoroughly examines these concerns, employing an innovative approach to assess the risks associated with hydraulic fracturing operations in shale gas reservoirs. Through the integration of diverse data sources, including quantitative and qualitative data, observational records, expert judgments, and global sensitivity analysis using the Sobol method, a comprehensive risk assessment model, was developed. This model carefully considered multiple condition indicators and extreme working conditions, such as pressures exceeding 110 MPa and temperatures surpassing 180° F. The integration of these varied data streams enabled the development of a robust Bayesian belief network. This network served as a powerful tool for the accurate identification of process vulnerabilities and the formulation of optimal development strategies. Remarkably, this study’s results showed that this approach led to a notable 12% reduction in operational costs, demonstrating its practical efficacy. Moreover, this study subjected its model to rigorous uncertainty and sensitivity analyses, pinpointing the most severe risks and outlining optimal measures for their reduction. By empowering decision-makers to make informed choices, this methodology not only enhances environmental sustainability and safety standards but also ensures prolonged well longevity while maximizing productivity in hydraulic fracturing operations.

A Comprehensive Model for Predicting 1-Year Survival after Allogeneic Hematopoietic Cell Transplantation in Older Patients

Background: As the number of elderly patients referred to and undergoing allogeneic hematopoietic cell transplantation (alloHCT) continues to rise, there is a growing need for a comprehensive risk assessment model to accurately predict transplantation outcomes in this specific patient population. The widely used Hematopoietic Cell Transplantation-specific Comorbidity Index (HCT-CI) does not fully capture factors influencing transplant outcomes in elderly patients. This study aims to enhance risk stratification for elderly patients undergoing alloHCT by integrating additional functional, nutritional, and clinical indicators. Methods: This is a retrospective study utilizing the data registry of HCT patients who received their treatment at UF Health Cancer Center. The inclusion criteria included all patients age &amp;gt; 65 years, who underwent their first alloHSCT between January 2012 and December 2022, regardless of graft type, conditioning regimen, donor type, or graft source. Patient data were extracted through chart review. The primary endpoint was 1-year Overall Survival (OS). Prognostic significance of each variable was assessed using Cox proportional hazard models. We subsequently used all variables to build a comprehensive prediction model to assess 1-year survival outcomes in elderly patients undergoing alloHCT. The predictive model's performance was measured by the Concordance-statistic (c-statistic). Results: A total of 69 patients satisfied the inclusion criteria and were included in the analysis. Median age was 69 years (range 66-81) and 65.2% were male. Unrelated donors were used in 66.6% of patients, while the remainder received grafts from related donors. Most patients received peripheral blood stem cell grafts (91%). Myeloid malignancies comprised the most common indication for alloHCT in this group of patients (89.8%). 59.4% had abnormal cytogenetics. 35 variables were considered in the analysis. In the univariate analysis, variables with impact on 1-year OS post-transplant included creatinine (HR 3.35, CI 1.62-6.91, p=0.001), blood urea nitrogen (HR 1.03, CI 1.01-1.06, p=0.007), estimated glomerular filtration rate (HR 0.93, CI 0.89-0.93, p=0.001), hemoglobin (HR 0.8, CI 0.66-0.97, p=0.02), abnormal cytogenetics (HR 2.27, CI 1.20-4.31, p=0.012) and high and very high disease risk index (HR 0.51, CI 0.27-0.98, p=0.04). Notably, both hemoglobin and cytogenetics demonstrated a c-statistic greater than 0.6 in the univariate analysis. In the multivariate analysis, variables impacting 1-year OS included hemoglobin (HR 0.69, CI 0.48-0.99, p=0.04), remission status at the time of transplant i.e., not reached (NR) vs complete remission 1 (CR1) (HR 0.05, CI 0.00-0.71, p=0.027), and abnormal cytogenetics (HR 7.55, CI 2.32-24.58, p=0.001). Interestingly, the HCT-CI did not significantly impact 1-year OS in the univariate analysis (c-statistic 0.51, HR 0.99, CI 0.85-1.15, p=0.896). Our prediction model showed good performance with a mean area under the receiver operating characteristic curve of 0.82. Conclusion: We developed a comprehensive prediction model to assess 1-year OS in elderly patients undergoing alloHCT to overcome some of the limitations of the HCT-CI in this unique age group. This model holds potential to enhance decision-making regarding candidacy for elderly patients undergoing alloHCT.

Exploring the Mutual Influence Relationships of International Airport Resilience Factors from the Perspective of Aviation Safety: Using Fermatean Fuzzy DEMATEL Approach

International airports are responding to the threat of climate change and various man-made hazards by proposing impact protection measures. Airport managers and risk controllers should develop a comprehensive risk assessment model to measure the mutual influence relationships of resilience factors. In this paper, the problem of treating resilience factors as independent ones in previous studies is overcome. In this study, we not only develop a framework for assessing resilience factors in international airports based on an aviation safety perspective, but also develop the Fermatean fuzzy decision-making trial and evaluation laboratory (FF-DEMATEL) to identify the mutual influence relationships of resilience factors. Fermatean fuzzy sets are incorporated in DEMATEL to reflect information incompleteness and uncertainty. The critical resilience factors of international airports were identified through real-case analysis. In terms of importance, the results show that rescue capability is a core capability that is important for airport resilience. In addition, “security management system (SeMS) integrity”, “education and training of ground staff on airport safety awareness”, “first aid mechanism for the injured”, and “adequate maintenance equipment for rapid restoration tasks” are identified as key factors that are given more weights. On the other hand, in terms of influence strength, the detection capability has the highest total influence and significantly influenced the other resilience capabilities. Finally, the influential network relation map (INRM) is utilized to assist decisionmakers in swiftly comprehending the impact of factors and formulating viable strategies to enhance airport resilience. This enables airport managers and risk controllers to make informed decisions and allocate resources efficiently.

Exploring two-decadal risk variability of drought-flood abrupt alternation in a high-plateau basin

The escalating intensity and frequency of drought-flood abrupt alternation (DFAA) events, intensified by climate change, pose a significant threat to both the eco-environment and human life. Despite their evident impacts, the risk associated with DFAA events has received limited attention. In this study, we present a comprehensive risk assessment model incorporating multifold natural and anthropogenic indicators to analyze the spatiotemporal variations of DFAA risk. Utilizing a DFAA index (DFAAI) based on river flow data from seven monitoring stations spanning 1999 to 2019, we investigate DFAA events in the Dianchi Basin, the largest lake in the Yunnan-Guizhou Plateau, China. Our analysis reveals that 56% of total DFAA events occurred between June and August, with drought-to-flood (DTF) events accounting for 62.5% of DFAA occurrences in the Dianchi Basin. Notably, the intensity and frequency of DFAA events exhibit significant spatial variations across the basin. The hazard, vulnerability, exposure, and disaster defense indices further exhibit substantial spatial discrepancies, with hazard indicators being the most influential, notably the DFAA rate accounting for 72.4% of the overall impact. Spatial correlations in DFAA risk among monitoring stations were found to be low, primarily influenced by variations in meteorological conditions, underlying surface characteristics, and human activities. The findings of this study can offer valuable insights for effective water resource management in an ever-changing environment.

Research on a Model for an Empirical Analysis of Inherent Defect Insurance Based on Ruin Theory

The system of inherent defect insurance is an important measure to serve the real economy through financial means and improve the quality of construction projects, which is the future development direction of China’s construction industry. However, the related research is not perfect due to the short implementation of the insurance. This could bring risks to the promotion of insurance for companies. Insurance ruin theory is an important method in risk management theory, so adopting it to manage the risk inherent defect insurance from the perspective of insurance companies is vital. The research starts with the classic insurance ruin theory and determines the coefficient of premium collection from the perspective of claim settlement distribution expectations. Furthermore, the approximate distribution of claim settlement is deduced, and a comprehensive risk assessment model is constructed. Finally, based on the data of insurance actuarial practice in Shanghai, both the ruin probability of inherent defect insurance in each insuring term and its average required initial reserve are calculated, which provides the analyses on the risks and main subitems of inherent defect insurance as well as relevant suggestions. Finally, the sensitivity analysis is used to further analyse the risk of different insurance stages of IDI, and the relevant measures are proposed. The research can provide theoretical assistances for insurance companies to carry out effective risk management and provide model tools to make scientific decisions.

Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data

The intricate topography and numerous hazards of highland roads contribute to a significantly higher incidence of traffic accidents on these roads compared to those on the plains. Although precise road data can enhance the safety evaluation and management of these road segments, the cost of data acquisition in highland areas is prohibitively high. To tackle this issue, our paper proposes a system of assessment indices and extraction methods specifically designed for plateau regions, supplementing existing road safety audit techniques. We are pioneers in integrating a high-precision 3D point cloud model into the safety risk assessment of low-traffic plateau roads, utilizing unmanned aerial vehicle (UAV) LiDAR technology. This innovative approach enhances both the efficiency and accuracy of road mapping. Building on this, we amalgamated three categories of indices—road 3D alignment, geographical environment, and natural disasters—to formulate a comprehensive safety risk assessment model. Applying this model to seventeen representative road segments on the Tibetan Plateau, we found that road alignment significantly influences road safety risk. The segments with the highest risk ratings are predominantly those located in the southwestern part of the Tibetan region, such as Zanda and Gar. Road safety management should prioritize road alignment, particularly the role of the curve radius, without overlooking the impact of environmental factors and natural disasters.

Urban flood risk assessment using AHP and geospatial techniques in swat Pakistan

The rapid urbanization and changing climate patterns in Swat, Pakistan have increased the vulnerability of urban areas to flood events. Accurate assessment of flood risk is crucial for effective urban planning and disaster management. In current research study flood hazard index was developed using analytic hierarchy process (AHP) technique in combination with the geographical information system (GIS) environment in Swat, Pakistan. The study integrates various data sources, including topographic maps, land use/land cover information, rainfall data, and infrastructure data, to develop a comprehensive flood risk assessment model. The weights obtained from the AHP analysis are combined with geospatial data using a geographic information system (GIS) to generate flood risk maps. The flood hazard levels were categorized into five distinct classes: very low, low, moderate, high, and very high. Using the GIS-AHP approach, higher weights were assigned to rainfall, distance to river, elevation, and slope in comparison to NDVI, TWI, LULC, curvature, and soil type. The flood hazard map was then reclassified for each parameter. By overlaying these maps, it was determined that 5.6% of the total area is classified as very high flood risk, 52% as high risk, 39.3% as moderate risk, and 3.1% as low risk. The developed comprehensive flood risk assessment model in current study can identify high-risk areas, prioritize mitigation measures, and aid in effective urban planning and disaster management.

Spatial layout siting method for fire stations based on comprehensive forest fire risk distribution

In order to shorten the response time of forest fire (FF) rescue and improve the efficiency of forest fire prevention and control, this paper proposes a fire station spatial layout location method based on the spatial distribution of FF comprehensive risk. A comprehensive forest fire risk assessment model has been developed by three perspectives, namely the risk factors of forest fires, exposure degree of the disaster-bearing body, and vulnerability of the disaster-bearing body. The location-assignment model was introduced to scientifically plan the spatial layout of FF stations from different perspectives. Zhuhai in Guangdong Province, China, a typical “city surrounded by mountains,” was chosen as the research site for case analysis. The results show that the high-risk areas of Zhuhai are mainly concentrated in the forests of Xiangzhou District and Gaoxin District; the medium-risk areas mainly lie in the forests in the north of Gaoxin District, the forests of Hengqin Zone and some forests of Doumen District. Through a combination of the minimized facility point model and the maximized coverage model, it is calculated that 9 FF stations are the optimal choice, which can meet Zhuhai's demand for FF prevention and control with an acceptable construction cost and good coverage.

Agricultural Drought Risk Assessment Based on a Comprehensive Model Using Geospatial Techniques in Songnen Plain, China

Drought is a damaging and costly natural disaster that will become more serious in the context of global climate change in the future. Constructing a reliable drought risk assessment model and presenting its spatial pattern could be significant for agricultural production. However, agricultural drought risk mapping scientifically still needs more effort. Considering the whole process of drought occurrence, this study developed a comprehensive agricultural drought risk assessment model that involved all risk components (exposure, hazard, vulnerability and mitigation capacity) and their associated criteria using geospatial techniques and fuzzy logic. The comprehensive model was applied in Songnen Plain to justify its applicability. ROC and AUC techniques were applied to evaluate its efficiency, and the prediction rate was 88.6%. The similar spatial distribution of water resources further verified the model’s reliability. The southwestern Songnen Plain is a very-high-risk (14.44%) region, determined by a high vulnerability, very high hazardousness and very low mitigation capacity, and is the region that should be paid the most attention to; the central part is a cross-risk region of high risk (24.68%) and moderate risk (27.28%) with a serious disturbance of human agricultural activities; the northeastern part is a dry grain production base with a relatively optimal agricultural production condition of very low risk (22.12%) and low risk (11.48%). Different drought mitigation strategies should be adopted in different regions due to different drought causes. The findings suggest that the proposed model is highly effective in mapping comprehensive drought risk for formulating strong drought mitigation strategies and could be used in other drought-prone areas.

Groundwater Risk Assessment Based on DRASTIC and Special Vulnerability of Solidified/Stabilized Heavy-Metal-Contaminated Sites

Solidification/stabilization technology is commonly used in the remediation of heavy-metal-contaminated sites, which reduces the leaching capacity of heavy metals, but the total amount of heavy metals in the soil is not reduced, there is still a risk of heavy metal re-release and contamination of groundwater, and the risk of groundwater contamination of solidified/stabilized heavy-metal-contaminated sites needs to be assessed. Through the analysis of the system structure of solidified/stabilized heavy-metal-contaminated sites, combined with the integration method of pollution sources—the vadose zone-aquifer, based on the DRASTIC model and the special vulnerability of the solidification/stabilization site, a groundwater pollution risk assessment index system including 4 influencing factors such as site hazard, pollutant hazard, aquifer vulnerability, and natural conditions and a total of 18 evaluation indexes was constructed. Each evaluation index was graded and assigned a scoring value combined with the Analytic Hierarchy Process (AHP) to calculate index weights. The comprehensive weights of site hazard, contaminant stability, aquifer vulnerability, and natural conditions were 0.1894, 0.3508, 0.3508, and 0.1090, respectively. The isometric method was used to classify the pollution risk into five risk levels (very low risk [0, 2), low risk [2, 4), medium risk [4, 6), high risk [6, 8), and very high risk [8, 10]), and a groundwater comprehensive index pollution risk assessment model was established. The model was applied to the actual site. The results showed that under the scenario of direct landfill of remediated soil, the comprehensive indexes of groundwater pollution risk for As and Cd were 4.55 and 4.58, respectively, both of which were medium risk. When the surrounding protective measures were supplemented, the comprehensive indexes of groundwater pollution risk for As and Cd were 3.98 and 4.02, respectively. Cd remained as medium risk and As as low risk. In both scenarios, the combined groundwater contamination risk index of Cd was greater than that of As because the contaminant stability of As was higher than that of Cd. The average percentage of aquifer vulnerability score reached 45.50%, which was higher than the weight of site inherent vulnerability of 35.08%, indicating that the original site hydrogeological conditions are fragile, groundwater is vulnerable to contamination, and the in situ landfill solidification/stabilization of soil is at risk. In order to further reduce the risk, the topographic slope was increased, thereby increasing the surface drainage capacity, which reduced the combined groundwater contamination risk index for As and Cd to 3.94 and 3.90, both of which were low risk. This study provides a new method for assessing the risk of groundwater contamination at solidified/stabilized heavy-metal-contaminated sites. It also has reference significance for selecting solidification/stabilization remediation parameters

Meteorological hazard risk assessment of offshore wind power grids based on CVAR method

Wind energy is a clean, renewable and green energy. Compared to land-based wind power, offshore wind power does not occupy land resources. Meanwhile offshore wind resources is much richer and its wind direction is more stable. Meteorological hazards have a major impact on offshore wind facilities. Typhoons, lightning, rainstorms and other meteorological hazards that occur during operation can not only cause fatal damage to offshore wind facilities, but often also trigger a chain reaction of marine hazards, making them more difficult to maintain. In this paper, we analyze the impact of various types of meteorological hazards on offshore wind facilities with examples and select the most important ones. A comprehensive risk assessment model CVAR for offshore wind projects is developed in combination with other types of risk elements to predict the damaging of offshore wind facilities so that managers can respond in a timely manner. In addition, A nonlinear stepwise regression (NLSR) approach is then used to forecast the meteorological elements in offshore grid facilities.

Risk Assessment of China Rapeseed Supply Chain and Policy Suggestions.

Rapeseed, as the most important oil crop in the world, not only affects national food security but also affects energy security and environmental security. It is very important to conduct a risk assessment of China's rapeseed supply chain and put forward suggestions to construct a safe, effective, and accessible supply chain. In order to accurately evaluate the safety of the rapeseed supply chain from 2010 to 2020, we applied fuzzy multiconnection theory and analytic hierarchy process model (AHP). A comprehensive risk assessment model for the rapeseed supply chain with two primary indicators and 10 secondary indicators was constructed. By establishing the rapeseed risk evaluation model, we quantitatively analyzed the risk of China's rapeseed supply chain. The domestic risk of production is still high, and the international risk under the high import dependence is alarming. We put forward risk prevention and countermeasures for China's rapeseed supply chain. The results show that China has a large demand for rapeseed products, but the increase in China production is limited and the import from other countries is unstable. The proposed suggestions are designed to optimize and enhance the stability of the rapeseed product's supply chain. It is recommended to continue to consolidate and deepen the cooperation with traditional trading partners such as Germany, Spain, the United States, and Brazil; expand other import sources to build a more diversified and efficient rapeseed product import network and extend the supply chain of rapeseed products. This research can be a basis for making decisions for promoting the sustainable and efficient development of the rapeseed supply chain.