Risk Quantification Model Research Articles

Minimum Risk Quantification Method for Error Threshold of Wind Farm Equivalent Model Based on Bayes Discriminant Criterion

The error threshold is the cornerstone to balance the mathematical complexity and simulation speed of wind farm (WF) equivalent models, and can promote the standardization process of equivalent methodology. Due to differences in power system conditions and model evaluation standards in different countries, the form and indexes of error thresholds of WF equivalent models have not been unified yet. This paper proposes a theoretical method for quantifying the minimum risk of error threshold of WF equivalent models based on the Bayes discriminant criterion. Firstly, the Euclidean errors of WF equivalent models in different periods are quantified, and the probability density distributions of the errors are fitted by kernel density estimation. Secondly, the real-time weighted prior probability algorithm is used to obtain the prior probability of a valid WF equivalent model, and the different losses caused by the missed judgment and misjudgment of the model validity to power systems are taken into account. Thirdly, the minimum risk quantification model of error threshold is established based on the Bayes discriminant criterion, and the feasibility of the proposed method is verified by an actual WF with numerical examples. Compared with the existing error thresholds, the proposed error threshold can more quickly and accurately determine the validity of WF equivalent models.

Integrating Multiple Bacterial Phenotypes and Bayesian Network for Analyzing Health Risks of Pathogens in Plastisphere.

Plastic pollution represents a critical threat to soil ecosystems and even humans, as plastics can serve as a habitat for breeding and refuging pathogenic microorganisms against stresses. However, evaluating the health risk of plastispheres is difficult due to the lack of risk factors and quantification model. Here, DNA sequencing, single-cell Raman-D2O labeling, and transformation assay were used to quantify key risk factors of plastisphere, including pathogen abundance, phenotypic resistance to various stresses (antibiotic and pesticide), and ability to acquire antibiotic resistance genes. A Bayesian network model was newly introduced to integrate these three factors and infer their causal relationships. Using this model, the risk of pathogen in the plastisphere is found to be nearly 3 magnitudes higher than that in free-living state. Furthermore, this model exhibits robustness for risk prediction, even in the absence of one factor. Our framework offers a novel and practical approach to assessing the health risk of plastispheres, contributing to the management of plastic-related threats to human health.

Risk equations for prosthetic joint infections (PJIs) in UK: a retrospective study using the Clinical Practice Research Datalink (CPRD) AURUM and GOLD databases

BackgroundProsthetic joint infections (PJIs) are a serious negative outcome of arthroplasty with incidence of about 1%. Risk of PJI could depend on local treatment policies and guidelines; no UK-specific risk...

Designing user-centered decision support systems for climate disasters: What information do communities and rescue responders need during floods?

Flooding events are the most common natural hazard globally, resulting in vast destruction and loss of life. An effective flood emergency response is necessary to lessen the negative impacts of flood disasters. However, disaster management and response efforts face a complex scenario. Simultaneously, regular citizens attempt to navigate the various sources of information being distributed and determine their best course of action. One thing is evident across all disaster scenarios: having accurate information and clear communication between citizens and rescue personnel is critical. This research aims to identify the diverse needs of two groups, rescue operators and citizens, during flood disaster events by investigating the sources and types of information they rely on and information that would improve their responses in the future. This information can improve the design and implementation of existing and future spatial decision support systems (SDSSs) during flooding events. This research identifies information characteristics crucial for rescue operators and everyday citizens' response and possible evacuation to flooding events by qualitatively coding survey responses from rescue responders and the public. The results show that including local input in SDSS development is crucial for improving higher-resolution flood risk quantification models. Doing so democratizes data collection and analysis, creates transparency and trust between people and governments, and leads to transformative solutions for the broader scientific community.

Research on finance Credit Risk Quantification Model Based on Machine Learning Algorithm

Machine learning is a branch of artificial intelligence (AI) technology that enables systems to learn and make predictions and decisions without the need for explicit programming. Machine learning algorithms learn patterns and relationships from data and are able to gradually improve their accuracy.This paper mainly introduces the application of deep learning in financial academia and the financial industry, with a particular focus on the application of machine learning and deep learning in financial quantitative trading. The paper mentions the complexity and challenges of financial markets and the difficulties machine learning faces in processing financial time series data, such as overfitting, non-stationarity, heteroscedasticity and autocorrelation. To overcome these challenges, the paper explores machine learning model design in financial quantitative trading and risk prediction, including data transformation and model construction. In the related work part, it introduces the history and development of quantitative investment, as well as the application of quantitative investment, such as high-frequency trading, arbitrage strategy and commodity trading advisory strategy. It then focuses on the application of machine learning in financial quantification, including the use of supervised and unsupervised learning in stock price forecasting and trading strategy generation. The paper also compares traditional quantitative strategies with machine learning strategies and discusses the advantages of machine learning in solving high-dimensional and non-linear problems. The methodology section introduces the application of the random forest model in financial quantification, including the model principle, feature importance calculation and experimental design. Through the pre-processing, feature selection and model construction of the credit risk prediction dataset, the construction and evaluation process of the random forest model is demonstrated. Finally, the performance of the random forest model is evaluated and compared with other models to demonstrate its advantages and applicability in financial quantification.

A model for quantification of environmental risk in mining ventures

The premature termination of industrial projects, often triggered by insufficient revenue generation to cover liabilities, poses a significant challenge in project evaluation. Although the conventional discounted cash flow (DCF) method remains widely used, its inability to account for project failure probabilities can lead to resource misallocation. This study aims to address this limitation by applying real options theory to assess the risk of premature abandonment within the context of a mineral extraction project. In this proposed system, fluctuations in environmental remediation costs are modelled by considering the cumulative probabilities of cost increases or decreases at each time step. The simulated project value reflects the cumulative activities required for mine closure, where each ton of extracted ore generates an environmental liability demanding remediation. Through the application of this system, the study demonstrates its effectiveness in identifying an escalating probability of project closure over time. Particularly, the findings reveal the existence of a critical threshold date. Beyond this date, any operational suspension exceeding one year renders project closure the economically optimal choice. This closure encompasses all necessary environmental restoration activities. By integrating real options theory into project evaluation, this study offers a more comprehensive approach to assessing the risk of premature project abandonment in mineral extraction projects. Through the consideration of fluctuating environmental remediation costs and the cumulative probabilities of project closure, it provides valuable insights for decision-making in project management and resource allocation. The trigger curve generated allowed for the identification of the viability threshold of the venture. Identifying the threshold to initiate the mining closure project.

Dynamic risk assessment of gas pipeline operation process by fusing visual and olfactory monitoring

With the rapid increase in urban gas consumption, the frequency of maintenance and repair of gas pipelines has escalated, leading to a rise in safety accidents during these processes. The traditional manual supervision model presents challenges such as inaccurate monitoring results, incomplete risk factor analysis, and a lack of quantitative risk assessment. This research focuses on developing a dynamic risk assessment technology for gas emergency repair operations by integrating the monitoring outcomes of artificial olfactory for gas leakage information and video object recognition for visual safety factor monitoring data. To quantitatively evaluate the risk of the operation process, a three-dimensional risk assessment model combining gas leakage with risk-correlated sensitivity was established as well as a separate three-dimensional risk assessment model integrating visual risk factors with predictable risk disposition. Furthermore, a visual risk quantification expression mode based on the risk matrix-radar map method was introduced. Additionally, a risk quantification model based on the fusion of visual and olfactory results was formulated. The verification results of simulation scenarios based on field data indicate that the visual-olfactory fusion risk assessment method can more accurately reflect the dynamic risk level of the operation process compared to simple visual safety factor monitoring. The outcomes of this research can contribute to the identification of safety status and early warning of risks related to personnel, equipment, and environmental factors in emergency repair operations. Moreover, these results can be extended to other operational scenarios, such as oil and gas production stations and long-distance pipeline operations.

Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity

To systematically study the influence of a propensity for a particular driving style on car-following risk, a safety potential field risk-quantification method that considers driving style is proposed. First, we classify driving behaviors and construct a field-based safety potential car-following model via analogy with intermolecular interactions; second, we establish a risk-quantification model by considering driving style, risk exposure, and risk severity and classify the consequent risk into four levels, high risk, medium risk, low risk, and safe, using the fuzzy C-means algorithm. Finally, we predict the car-following risk using the LightGBM algorithm in real time. The experimental results show that the LightGBM algorithm can recognize up to 86% of medium–high risk levels compared to the Decision Tree and Random Forest Algorithms. It can achieve effective prediction of car-following risk, which provides sufficient warning information to drivers and helps improve the overall safety of vehicle operation.

Dynamic Illumination Method for Rural Highway Intersections with Traffic Flow Changes

The conflicts between vehicles and vulnerable road users are frequent at rural highway intersections. Improving lighting conditions at intersections can significantly reduce the likelihood of nighttime accidents. This study aims to develop a dynamic illumination method for rural highway intersections with traffic flow changes. First, the safety design principles of a lighting scheme were formulated for minimum illuminance, vehicle speed, and traffic risk at intersections. In addition, a traffic risk quantification model for intersections was developed with illuminance and traffic flow parameters as inputs, incorporating gap acceptance theory and the braking safety distance model. Second, combining safety design principles and a traffic risk quantification model, a calculation method for optimal illuminance at intersections was designed with minimum illuminance as the optimization objective, and considering the risk at intersections not exceeding the safety threshold as the constraint. Finally, a case study was conducted in a simulated environment. Evaluation results demonstrate that limiting the maximum nighttime vehicle speed at intersections to 50 km/h can maximize the energy-saving rate of the lighting system while ensuring traffic safety and transport efficiency. The advantage of the method is that it can calculate the safe lighting scheme for highway intersections without any historic accident data and save lighting energy consumption. It increases the feasibility of promoting lighting facilities at rural highway intersections.

Risk quantification and prediction of non-driving-related tasks on drivers' critical intervention behavior in autonomous driving scenarios

For autonomous driving, drivers’ intervention may be required when vehicles fail or are in a dilemma to detect emergent and unprogrammed events. In such situations, non-driving related tasks may have a great impact on the safety of drivers’ critical intervention behavior thus leading to traffic accidents. Therefore, exploring the impacts of non-driving-related tasks on drivers’ critical intervention behavior, quantifying and predicting the corresponding risks have become important. In this paper, driving simulation experiments are carried out to obtain the vehicle driving state data and visual behavior information of drivers during the autonomous driving scenarios that require critical interventions. To construct the risk quantification model for drivers’ critical intervention behavior, the fuzzy comprehensive evaluation method and the criteria importance though intercriteria correlation (CRITIC) weighting method are employed. Then, for risk prediction, a model is constructed based on the visual behavior information before the occurrences of intervention. Multivariate logistic regression (MLR) and support vector machine are compared. The results show that non-driving tasks significantly postpone driver's critical intervention responses, increasing crash risks of the driving. For prediction, SVM performs better than the MLR in terms of metrics including the precision, the recall, and the overall accuracy. This paper examines the risks during situations requiring drivers’ critical intervention, associated with different non-driving tasks, which has remained much unexplored in the previous research. The methodology of this paper can be applied to smart vehicle systems in alerting vehicles for take-over reactions, with recognizing and predicting potential risks.

Influence of Risk Management on Financial Performance of “Uzbekinvest” Insurance Company

Risk if not well managed could lead to collapse for most organizations especially those whose core business deals with day-to-day handling of risk. Risk management should, therefore, be at the core of an organization’s operations by integrating risk management practices into processes, systems, and culture of the entire organization. This involves identifying and analyzing risks, developing, and implementing risk handling techniques and monitoring the progress of these to avoid andor reduce the impact of risk on the financial performance of the company. Furthermore, insurance company under concern should follow current international leading practice by adopting Enterprise Risk Management (ERM) which incorporates other insurance risk quantification models. This will ensure that the companies remain afloat during such times of strict regulatory regimes such as solvency 11 and Basel.

Crash Risks Evaluation of Urban Expressways: A Case Study in Shanghai

Proactive traffic safety management systems can reduce crashes by identifying crash precursors, evaluating real-time crash risks, and implementing suitable interventions. The basic prerequisite for developing such a system is to propose a reliable crash risk evaluation model that takes real-time traffic flow data as input. Previous studies have primarily focused on real-time crash prediction using some statistical or machine-learning methods. However, further quantitative evaluation and classification of crash risks have been ignored. In this study, we conduct a systematic crash risk evaluation workflow, including crash risk prediction, crash risk quantification, and crash risk classification. Specifically, the crash risk prediction using an extended logit model is proposed, from which CAS, CSD, UAS, DAS, DTV are identified to be contributing factors of crash risks. Then a crash risk quantification model based on the parameter evaluation of the extended logit model is developed. The crash risks of urban expressways and their spatial-temporal evolution trends are quantified. Finally, the crash risks are classified into high crash risk level, moderate crash risk level, and low crash risk level by the k-means cluster algorithm. Then the threshold boundaries of different crash risk levels are determined. The research results provide a proactive guidance for traffic safety management of urban expressways.

Application of Bayesian Algorithm in Risk Quantification for Network Security.

Network security risk quantification involves both technical and management aspects. Risk quantification has great uncertainty and cannot be fully quantified. Therefore, the fully objective realization of network information security risk quantification is not yet mature. This paper analyzes and quantifies the network security risks caused by various threat sources through a network security risk quantification model based on the Bayesian algorithm. By combining expert knowledge, the conditional probability matrix under the inference rule of the Bayesian algorithm is clarified, and the subjective judgment information of experts on the damage degree of the target information system is synthesized into the prior information system of network security threat. The Bayesian algorithm is used to realize the observation node of objective assessment information and combining subjective security threat levels to achieve continuity and accumulation of security assessments. The error is about 3%, which has a very good effect on the quantification of network security risk.

SARS-CoV-2 Risk Quantification Model and Validation Based on Large-Scale Dutch Test Events.

In response to the outbreak of SARS-CoV-2, many governments decided in 2020 to impose lockdowns on societies. Although the package of measures that constitute such lockdowns differs between countries, it is a general rule that contact between people, especially in large groups of people, is avoided or prohibited. The main reasoning behind these measures is to prevent healthcare systems from becoming overloaded. As of 2021 vaccines against SARS-CoV-2 are available, but these do not guarantee 100% risk reduction and it will take a while for the world to reach a sufficient immune status. This raises the question of whether and under which conditions events like theater shows, conferences, professional sports events, concerts, and festivals can be organized. The current paper presents a COVID-19 risk quantification method for (large-scale) events. This method can be applied to events to define an alternative package of measures replacing generic social distancing.

Research of Quantative Trading Strategy Based on LSTM

Making strategies to maximize returns has always been the biggest problem almost every investor faces in the process of investment. In this paper, we will establish a model to analyze the internal characteristics of gold and bitcoin price data for further price prediction, which is of great help in formulation of trading strategy for the future. In Section 2 we hope to analyze the price trend of gold and bitcoin. At the beginning we establish a model based on ARIMA and get a relatively good prediction result. However, considering the errors may occur when the backward prediction unit gets long in this model, we try to use LSTM algorithm to better extract the nonlinear characteristics of the two time series of price. Through LSTM algorithm we get a much better result, and thus we choose LSTM as our final prediction model after comprehensive comparison. In Section 3, we use the predicted results to make trading strategies. We first divide investors into reckless and prudent types and establish models under different assumptions respectively. Then by applying greedy algorithm and establishing risk quantification model we approach the global optimal solution respectively. In the end, after testing the model and confirming its excellent performance, we summarize the advantages and shortcomings about this model comprehensively. Moreover, much more conclusions are drawn to analyze the possible model update in future.Price Prediction

Privacy risk quantification in education data using Markov model

AbstractWith Big Data revolution, the education sector is being reshaped. The current data‐driven education system provides many opportunities to utilize the enormous amount of collected data about students' activities and performance for personalized education, adapting teaching methods, and decision making. On the other hand, such benefits come at a cost to privacy. For example, the identification of a student's poor performance across multiple courses. While several works have been conducted on quantifying the re‐identification risks of individuals in released datasets, they assume an adversary's prior knowledge about target individuals. Most of them do not utilize all the available information in the datasets. For example, event‐level information that associates multiple records to the same individual and correlation between attributes. In this work, we propose a method using a Markov Model (MM) to quantify re‐identification risks using all available information in the data under a more realistic threat model that assumes different levels of an adversary's knowledge about the target individual, ranging from any one of the attributes to all given attributes. Moreover, we propose a workflow for efficiently calculating MM risk which is highly scalable to large number of attributes. Experimental results from real education datasets show the efficacy of our model for re‐identification risk. Practitioner notesWhat is already known about this topic? There has been a number of works/research conducted on privacy risk quantification in datasets and in the Web. Majority of them have strong assumption about adversary's prior knowledge of target individual(s). Most of them do not utilize all the available information in the datasets, eg, event‐level or duplicate records and correlation between attributes. What this paper adds? This paper proposes a new re‐identification risk quantification model using Markov models. Our model addresses the shortcomings of existing works, eg, strong assumption about adversary's knowledge, unexplainable model, and utilizing available information in the datasets. Specifically, our proposed model not only focuses on the uniqueness of data points in the datasets (as most of the other existing methods), but also takes into account uniformity and correlation characteristics of these data points. Re‐identification risk quantification is computationally expensive and is not scalable to large datasets with increasing number of attributes. This paper introduces a workflow for data custodians to use to efficiently evaluate the worst‐case re‐identification risk in their datasets before releasing. It presents extensive experimental evaluation results of the proposed model for quantifying re‐identification risks on several real education datasets. Implications for practice and/or policy? Empirical results on real education datasets validate the significance and efficacy of the proposed model for re‐identification risk quantification compared to existing approaches. Our model can be used by the data custodians as a tool to evaluate the worst‐case risk of a dataset. It empowers data custodians to make informed decisions on appropriate actions to mitigate these risks (eg, data perturbation) before sharing or releasing their datasets to third parties. A typical use case would be one where the data custodians is an online course/program provider, which collects data about students' engagement with their courses and would like to share it with third parties for them to run learning analytics that would provide value‐added benefits back to the data custodian. We specifically study the privacy risk quantification for education data; however, our model is applicable to any tabular data release.

Quantification of model risk with an application to probability of default estimation and stress testing for a large corporate portfolio

This paper addresses the building of obligor-level hazard rate corporate probability of default models for stress testing, departing from the predominant practice in wholesale credit modeling of constructing segment-level models for this purpose. We build models based upon varied financial, credit rating, equity market and macroeconomic factors, using an extensive history of large corporate firms sourced from Moody’s.We develop distance-to-default (DTD) risk factors and design hybrid structural/ Merton reduced-form models as challengers to versions of the models containing other individual variables. We measure the model risk attributed to various modeling assumptions according to the principle of relative entropy and observe that the omitted-variable bias with respect to the DTD risk factor, neglect of interaction effects and incorrect link function specification has the greatest, intermediate and least impact, respectively. Given the sensitive regulatory uses of these models and the concerns raised in the industry about the effect of model misspecification on capital and reserves, our conclusion is that validation methods chosen in the stress testing context should be capable of testing model assumptions. Our research adds to the literature in that it offers state-of-the-art techniques as viable options in the arsenal of model validators, developers and supervisors seeking to manage model risk.

Validation of corporate probability of default models considering alternative use cases and the quantification of model risk

<abstract> <p>In this study we consider the construction of <italic>through-the-cycle</italic> ("TTC") <italic>probability-of-default</italic> ("PD") models designed for credit underwriting uses and <italic>point-in-time</italic> ("PIT") PD models suitable for early warning uses, considering which validation elements should be emphasized in each case. We build PD models using a long history of large corporate firms sourced from Moody's, with a large number of financial, equity market and macroeconomic candidate explanatory variables. We construct a Merton model-style <italic>distance-to-default</italic> ("DTD") measure and build hybrid structural and reduced-form models to compare with the financial ratio and macroeconomic variable-only models. In the hybrid models, the financial and macroeconomic explanatory variables still enter significantly and improve the predictive accuracy of the TTC models, which generally lag behind the PIT models in that performance measure. While all classes of models have high discriminatory power by most measures, on an out-of-sample basis the TTC models perform better than the PIT models. We measure the model risk attributable to various model assumptions according to the principle of <italic>relative entropy</italic> and observe that omitted variable bias with respect to the DTD risk factor, neglect of interaction effects and incorrect link function specification has the greatest, intermediate and least impacts, respectively. We conclude that care must be taken to judiciously choose how we validate TTC vs. PIT models, as criteria may be rather different and apart from standards such as discriminatory power. This study contributes to the literature by providing expert guidance to credit risk modeling, model validation and supervisory practitioners in managing model risk.</p> </abstract>

An analytic network process model for risk quantification of mega construction projects

Risk, complexity, and uncertainty are inherent components of megaprojects due to their unique features. However, existing project management practices lack a structured synthesis of these concepts, which leads to unrealistic risk assessments, ineffective management strategies, and poor project performance. In order to fill this gap, this research aims to develop a holistic risk quantification approach incorporating risk-related concepts. For this purpose, a conceptual risk assessment process developed for mega construction projects was operationalized with an Analytic Network Process (ANP) model. The weights of the risk sources in the ANP model were determined by the domain experts through a two-round Delphi study. With the purpose of improving the efficiency and reliability of the knowledge elicitation process, the Delphi study was supported by an interactive data collection tool capable of ANP calculations. The resulting model helped to prioritize the risk sources in mega construction projects. The validity of the findings was tested through the data of 11 mega construction projects. Validation studies revealed the potential of the ANP-based model in quantifying the project risks. Hence, the novel approach proposed in this study is expected to contribute both to the literature by unveiling the interactions between risk-related concepts and to the practitioners by assisting them in assessing the project risks more realistically. Although the risk quantification model has been developed for mega construction projects, it can also be implemented in other project-based industries with minor modifications.

Methodology on Establishing Multivariate Failure Thresholds for Improved Remaining Useful Life Prediction in PHM

Prognostics and Health Management (PHM) methodologies and techniques have been much widely studied in the academia and practiced by the industry in recent years. Prognostic approaches commonly try to establish the relationship between Remaining Useful Life (RUL) and a single variable or health indicator (HI) which can be obtained from multi-sensor fusion or data-driven models. However, simply relying on a single variable could reduce RUL prediction robustness when it is less representative of the system health conditions. Taking multiple variables into consideration for RUL prediction, quantifying operating risks and determining multivariate failure threshold is essential yet rarely studied. Generally, there are three major challenges that limit the practicality of this topic. 1) How to determine the multivariate failure threshold? 2) How to quantify operation risks based on multiple variables? 3) How to make reliable extrapolations of future conditions? To address these questions, this paper proposes 1) a novel copula model to determine multivariate failure threshold, and 2) a Maximum Likelihood Estimation enhanced similarity-based Particle Filter (MLE-SMPF) to predict future system conditions. In the proposed methodology, the health assessment is firstly performed to obtain HI trajectory. The copula risk quantification model is then trained by two variables HI and life. The proposed copula model can easily include multiple variables compared with our previously published approach using bivariate Weibull Distribution[1]. Afterward, MLE-SMPF is used to extrapolate future HI for testing data. The prediction capability is further improved compared with [2] by introducing MLE for Particle Filter transition function parameter initialization. Finally, the system RUL is determined from the failure threshold which is obtained according to the quantified operation risk. The proposed methodology is validated on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The result outperforms most of the benchmarks from recent publications. The proposed methodology is easy to transfer to other potential machine prognostic applications.