Risk Warning System Research Articles

Early Warning of Sudden Water Pollution Accident Risks Based on Water Quality Models in the Three Gorges Dam Area

In recent years, sudden water pollution accidents have frequently occurred and seriously endangered people’s drinking water safety. Early warnings for water pollution accidents has become the core work for emergency response and sparked substantial research. However, risk assessments for different water receptors still needs to be solved for the early warning of water pollution accidents. This paper proposes a new early warning assessment method based on water quality models for different receptors, divided into the water source area (including drinking water source area and agricultural water use area) and the non-water source area. The constructed method was applied to the Three Gorges Dam area in China to simulate a phosphorus leakage accident caused by a traffic accident. Based on the EFDC model, the migration evolution process and the distribution change characteristics of phosphorus were simulated. Accordingly, the different risk levels of zones between the incident site and the downstream drinking water source area were obtained. The application results show that the risk warning system for sudden water pollution accidents based on the water quality model proposed in this paper can be applied to provide scientific support for the emergency response of sudden water pollution accidents.

Lightweight ViT with Multiscale Feature Fusion for Driving Risk Rating Warning System

AbstractAddressing the issue of inadequate dynamic object detection accuracy in current road driving warning systems, this study proposes the RepBF‐YOLOv8 detection algorithm aimed at efficient risk identification. The backbone network of YOLOv8n is replaced with the lightweight RepViT architecture, which is more suitable for visual tasks. This replacement simplifies the traditional structure, reduces the complexity of the backbone network, maximizes performance enhancement, and minimizes latency. Additionally, the FPN in the neck section is upgraded to Bi‐FPN, which reduces nodes and span connections and incorporates rapid normalization to achieve fast multi‐scale feature fusion. For risk grading, the algorithm infers distances and collision times, categorizing detected objects into high, medium, and low‐risk levels, and uses different colors to warn the driver. Comparative experimental results show that the optimized algorithm improves Precision by 1.7%, Recall by 2.3%, mAP@0.5 by 1.53%, and mAP@0.5:0.95 by 2.91%. In road tests, the risk warning system achieves a frame detection rate ranging from a minimum of 38.4 fps to a maximum of 59.0 fps. The detection confidence for various objects remains above 0.71, reaching as high as 0.98. Specifically, the “Car” confidence ranges from 0.81 to 0.98, demonstrating the accuracy and robustness of vehicle risk detection.

China’s GDP-at-Risk: Real-Time Monitoring, Risk Tracing, and Macroeconomic Policy Effects

Timely monitoring GDP-at-risk and tracing economic downside risk sources can help establish effective risk warning and prevention systems. This study constructs a probability distribution for China’s economic growth with skewness determined by a multidimensional predictor information set of macro fundamentals. Such a treatment allows us to identify changing drivers of economic downside risks during monitoring GDP-at-risk’s dynamic evolutionary path. We also employ a time-varying parameter vector autoregression model with random volatility to explore the heterogeneous impacts of different macroeconomic policy instruments on economic slowdowns. Our results provide empirical support for macroeconomic management and policy formulation in emerging markets. We reach three conclusions. First, the dynamics of GDP-at-risk exhibit significant event-driven characteristics, and economic downside risk increases significantly under the influence of extreme events. Moreover, the probability distribution of economic growth is asymmetric--as the downside risk of the economy increases, its upside potential increases disproportionately. Second, the time-varying risk trace of GDP-at-risk shows that the contribution of financial conditions and local government debt to economic downside risk declines. The importance of the risk-driving role of housing price growth gradually increases, suggesting that China’s property prices can provide more valuable early warning information about future growth risk, allowing time for precise preventive measures. Nevertheless, interest rates and inflation as risk divers have consistently minimal impacts. Third, the heterogeneity impulse response function of GDP-at-risk suggests that quantity-based monetary policy and fiscal policy can manage economic downside risks in the short run. In contrast, price-based monetary policy can curb economic overheating and reduce downside risks in the medium to long term. Therefore, the effect of price-based monetary policy is more sustainable in China.

Hybrid Prediction Model for International Crude Oil Prices Based on EEMD and SE Reconstruction Methods

Accurate prediction of crude oil prices can provide references for the energy economy and valuable guidance for investors and decision-makers in formulating strategies. However, due to the complexity and volatility of the crude oil market, achieving accurate and reliable crude oil price forecasts is challenging. Therefore, this paper proposes a hybrid forecasting model for crude oil prices that combines Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SE) reconstruction. Firstly, the daily closing price data of WTI crude oil futures from January 2, 2018, to March 21, 2024, is selected. The EEMD method is used to decompose the original data, and the decomposed sub-sequences are reconstructed and merged into high-frequency, mid-frequency, and low-frequency sequences using Sample Entropy (SE). Finally, these sequences are input into six machine learning models for prediction and comparison. The research results show that, firstly, by comparing the R2, RMSE, MAE, and MAPE values, it can be seen that the forecasting ability of the EEMD-SE hybrid forecasting model is significantly better than the EEMD hybrid forecasting model, indicating that the combination of SE methods can effectively improve the model s forecasting ability. Secondly, the EEMD-SE-LSTM model has higher prediction accuracy than other models. Finally, the comparison of models reveals that the BP neural network exhibits the greatest improvement after SE reconstruction. Therefore, it is recommended to improve the crude oil futures price forecasting and risk warning system.

Optimizing Financial Risk Models in Digital Transformation-Deep Learning for Enterprise Management Decision Systems

The digital transformation of enterprises has amplified the complexity of financial risks, underscoring the significance of optimizing financial risk warning models to ensure sustainable development. This study integrates several deep learning techniques, including Back Propagation Neural Network (BPNN), Bi-Long Short-Term Memory (Bi-LSTM), and transfer learning, to enhance the risk warning system and improve the accuracy and efficiency of financial risk prediction models. The results demonstrate that the proposed algorithm surpasses the baseline models in various metrics. For instance, on the Altman's Z-Score dataset, there is an improvement of 1.4% in accuracy, a reduction of over 48.8% in FLOP, and an enhancement of 43.5% in MAPE. These outcomes underscore the significant advancements in risk identification, decision support, and proactive risk management facilitated by the proposed model. As a result, enterprises can derive benefits from more precise and reliable financial risk warning tools, and effectively address the challenges brought about by digital transformation.

Nonlinear behavior of tail risk resonance and early warning: Insight from global energy stock markets

This article calculates the tail risk of the global energy market and explores the nonlinear behavior of tail risk resonance at the global, regional and national levels from a dynamic perspective. Moreover, we apply various machine learning models to perform the experimental training on the drivers that affect tail risk resonance to establish a risk warning system. By simulation training and evaluation, we screen the optimal risk warning model and identify the important drivers of risk resonance in the energy market. The findings indicate that the energy system has a strong tail risk resonance phenomenon, and the occurrence of extreme events as well as a collapse in oil prices can significantly increase the system's risk resonance. The European region is the dominant risk emitter and the American region is the main risk receiver. The Russian-Ukrainian war significantly increased the risk resonance across regions. The Argentine energy market is the main receiver of tail risk and the US energy market is the primary sender of tail risk. By incorporating the risk factors into several machine learning models, the Random Forest model is found to provide effective early warning of tail risks. Additionally, importance analysis reveals that inflation levels, stock market volatility and trade policy changes are important factors affecting risk resonance in the energy market and have nonlinear effects on the risk resonance of the energy system.

МЕХАНИЗМ ТРАНСФОРМАЦИИ БИЗНЕС-ПРОЦЕССОВ АПК В УСЛОВИЯХ СУВЕРЕНИЗАЦИИ ПРОДОВОЛЬСТВЕННОГО ПОТЕНЦИАЛА

In the current difficult business conditions in the Russian Federation, due to the restrictive measures of unfriendly countries towards the Russian economy, there is a growing need for mechanisms to effectively increase the resilience of agricultural systems to sanctions risks, trade barriers and other external threats, reinforced by the low sovereignty of the country’s food potential. In this context, significant adaptive potential is concentrated in the transformation of business processes of economic entities that are producers of agricultural products and food, taking into account constantly changing business conditions. In this regard, the issues related to the development of a mechanism for the transformation of business processes seem relevant. The purpose of the study is to develop recommendations on the formation of the structure of the mechanism for the transformation of business processes in the agro-industrial complex in the context of the sovereignization of Russia’s food potential. The following tasks are to be solved: to assess current trends in the changing environment of food producers in Russia, in particular as a result of the establishment of barriers to Russian business; to identify key areas for ensuring the sustainable functioning of economic entities in conditions of turbulence and exogenous risks; to develop a generalized structure of the mechanism of transformation of business processes of manufacturers of agricultural products. The authors propose the structure of a distributed mechanism, represented by a risk warning system on the external contour of an economic entity that poses a threat to business process disruption; a system for responding to threats of technological process disruption, providing rapid restructuring of the entire chain of business processes; a reflection system responsible for a posteriori analysis of the operation of warning and response systems. The proposed structure of the mechanism for transforming the business processes of the agro-industrial complex can form the basis for further developments on the formation of such a mechanism and its practical use in order to adapt food producers to sanctions pressure, ensure the sustainability of production, and increase the country’s food security.

Stock Risk Warning and Financial Market Stability Based on the Perspective of Stock Price Crash Risk of Peer Enterprises

This study empirically tests the effect of stock risk warning of listed companies on the risk of stock price crash of non-warning companies in the same industry and its mechanism in the Chinese A-share listed companies from 2008 to 2021 as samples. The research results show that the risk warning information has a capital market governance effect on peer companies, that is, the new risk warning information in an industry will alleviate the risk of stock price crash of peer companies. This conclusion remains robust after a series of robustness and endogeneity tests. The mediating mechanism test shows that the idiosyncratic information content of the stock price mediates the process of risk warning information affecting the risk of stock price crash of peer companies. The heterogeneity test shows that the governance effect of risk warning information on the risk of stock price crash of peer companies is more significant when the problem of company agency is more serious, the competitive market position is lower, or the differences in opinion among investors are smaller. This study provides empirical evidence for the necessity of the existence of the risk warning system. Additionally, it provides a reference for the capital market risk supervision of similar transition economies such as China.

A Multi-channel System Architecture Design for Myocardial Infarction Prediction

Cardiac and cerebral infarctions represent the most perilous pathologies within the spectrum of cardiovascular and cerebrovascular diseases, with grave consequences following their onset. However, prompt diagnosis and treatment can substantially enhance the chances of successful rescue and avert severe outcomes. Therefore, the development of a non-invasive, low-cost, and easy-to-use cardiac and cerebral infarction prediction system for home and personal use bears considerable social and economic significance.The electrocardiogram (ECG), acupoint electrical signals, and pulse, as physiological signals, contain abundant information on human health status. Integrating the information from these three signals can offer a more comprehensive and accurate risk prediction of cardiovascular and cerebrovascular diseases in patients. This approach represents a promising avenue for advancing the field of healthcare and disease management.Firstly, we analyzed the myocardial infarction and cerebral infarction prediction system requirements. The research determines four pulse collection points, two ECG, and ten acupoint electricity collection points. Then, we completed the cardiovascular and cerebrovascular disease risk warning system’s overall architecture design and front-end scheme design of ECG, acupoint electricit and pulse wave acquisition. The system successfully collects data from the test subjects by connecting to the computer.

A web-based system to determine risk of investment in international rail construction projects

Manual evaluation of investment risk make results and solutions are not timely. The objective of the study is to explore intelligent risk data collecting and risk early warning of international rail construction. First, this study has identified risk variables by content mining. Second, risk thresholds are calculated by the quantile method based on data from 2010 to A.D. 2019. Third, this study has developed risk early warning system by the gray system theory model, the matter-element extension method and the entropy weight method. Fourth, the risk early warning system is verified using Nigeria coastal railway project in Abuja. This study found that: (1) the framework of the developed risk warning system contains a software and hardware infrastructure layer, a data collection layer, an application support layer, and an application layer. (2) 37 investment risk variables are recognized; (3) 12 risk variables thresholds intervals are not equally divided between 0 and 1, the others are evenly distributed; (4) based on the application of Nigeria coastal railway project in Abuja, the system verification results are consistent with real situations, which is shown that risk early warning system is reasonable and feasible. These findings offer a good reference for intelligent risk management.

The Territorial Debris Flow Early Warning System of Piemonte (North-western Italy)

Debris flows are one of the most frequent and dangerous phenomena affecting the Alpine environment; they are responsible of 36% of casualties due to gravitational phenomena in the Italian Alps during the last century. In the Western Italian Alps (Piemonte, Italy) a Territorial Debris Flow Early Warning System (Te-DFEWS) aimed to forecast and predict the occurrence of sediment mass-transport has been developed, based on the characterization of small Alpine catchments (< 50 km2) and the processes that take place in these. The Te-DFWES is based on the identification of predisposing and triggering factors that determinate debris flow occurrence. The Te-DFEWS operates from 2010 in present-time and nowcasting using weather radar observations as input data. Recently (2021), the Te-DFEWS has been improved extending the forecasting window (up to 48h from simulation) by the introduction of Quantitative Precipitation Estimate/Forecast (QPE/QPF) input from COSMO-2I and ICON-IT, local high-resolution weather models. The Te-DFEWS, named DEFENSE (DEbris Flows triggEred by storms - Nowcasting SystEm) and the related warning procedures are presented as an operational tool integrated in the Regional Warning System for Geo-hydrological and Hydraulic Risk at the Functional Centre of Piemonte, managed by the Regional Agency for Environmental Protection of Piemonte (Arpa Piemonte).

Risk assessment and warning system for strategic biological resources in China

Risk assessment and warning system for strategic biological resources in China

A New Method for Environmental Risk Assessment of Pollutants Based on Multi-Dimensional Risk Factors.

Pollutant discharge causing the deterioration of the watershed environment has seriously threatened human health and ecosystem function. The importance of improving the risk warning system is becoming more and more prominent. Traditional chemical risk assessment methods focused on toxicity and the exposure of pollutants without considering the impact of persistent pollutants in different environmental media. In this study, a new approach was proposed to reflect multi-dimensional evaluation with a synthetic risk factor (SRF) of pollutants. The integrating parameters of SRF include toxicity endpoint values, environmental exposure level, persistent properties, and compartment features. Selected pesticides, perfluorinated compounds, organophosphate esters and endocrine disruptors were analyzed by the proposed and traditional methods. The results showed a higher risk outcome using SRF analysis for PFOS, imazalil, testosterone, androstenedione and bisphenol A, which were different from those obtained by the traditional method, which were consistent with existing risk management. The study demonstrated that the SRF method improved the risk assessment of various pollutants in different environmental media in a more robust fashion, and also provided a more accurate decision basis for ecological environment protection.

Internet of Things-Based Risk Warning System for Distribution Grid Operation State

The distribution network is the most terminal part of the power system. The safety and stability of its operation directly affect the power supply reliability of the power system. The operation status of the equipment has always been the focus of researchers. Once a problem occurs in the operation of the distribution network, it will have a significant impact on public safety and social development. Therefore, real-time monitoring of the operation status of the distribution network is very important. In order to meet the needs of real-time intelligent detection of the operation status of the distribution network, this paper introduces a risk warning system based on the Internet of Things for the operation status of the distribution network. This paper firstly analyzes the weight of risk indicators to determine the detection indicators of the risk early warning system, and secondly, through the analysis of the risk early warning indicator system of the distribution network operation status and the establishment of the risk early warning evaluation model, to determine the operation status of the distribution network the following risk early warning detection needs, and finally the Internet of Things technology is used in the design of the risk early warning system of the operating state of the distribution network, and then the system is tested. The test shows that the real-time detection data error of the system is less than 5%.

China’s local government debt risk assessment and countermeasures under the influence of COVID-19

Under the influence of COVID-19, the global economic and social development is facing great challenges. With the increase of government financial pressure and the decrease of debt paying ability, the problem of debt risk of local governments in China is attracting wide attention. In order to measure the level of China’s local government debt risk under the influence of COVID-19, this paper takes China’s Sichuan Province as an example, collects the core indicators data of measuring local government debt risk in 2017-2020 years, and uses AHP-TOPSIS method to make a comprehensive analysis of the local government debt risk situation in different periods before and after COVID-19. It is found that the local government debt risk in Sichuan Province is generally controllable. However, influenced by COVID-19, in 2020, the overall level of local government debt risk in Sichuan province expanded by 22.1% compared with 2019, this is mainly due to the further expansion of debt scale and slower economic growth. This paper suggests that the Chinese government should speed up the construction of comprehensive early warning and supervision system of local government debt risk, and prevent and resolve the debt risk of local government in advance.

Study on the Intelligent Risk Warning and Control System in Hospitals

Study on the Intelligent Risk Warning and Control System in Hospitals

Risk Warning System of Market Power Monitoring in Electricity Market

The electricity market is involved in a large number of stakeholders, the risk is complex, and the market power will exacerbate the crisis after openness of the electricity market. If regulators can build the electricity market risk warning system in advance, they can detect risk signals in advance and take effective measures to avoid electricity power crisis. This paper focuses on building the electricity market power risk warning system. Thus, we firstly establish a set of indicators that can reflect the electricity market risk condition scientifically, and then estimate the threshold value of risk indicators by using the VaR method based on GARCH model. The risk warning system with five levels of signals is proposed by using the estimated VaR values of the indicator under different confidence, and it is validated to be feasible and effective. Finally, some measures are proposed to respond to different types of risk warning.

Climate Risk and Early Warning Systems: Adaptation Strategies for the Most Vulnerable Communities

Climate risk and resultant natural disasters have significant impacts on human and natural environments. It is common for disaster responses to be reactive rather than proactive due to inadequate policy and planning mechanisms—such reactive management responses exacerbate human and economic losses in times of disaster. Proactive disaster responses maximize disaster resilience and preparation efforts in non-disaster periods. This report focuses on proactive, localized, and inclusive adaptation strategies for addressing impacts of three natural hazards: drought, floods, and tropical cyclones. Four key synergistic climate adaptation strategies are discussed—Post Disaster Reviews, Risk Assessments, Early Warning Systems and Forecast-based Financing. These strategies are further supported with a number of case studies and recommendations that will be of assistance for policymakers in developing evidence-based adaptation strategies that support the most vulnerable communities in the transition towards regarding disaster as a risk as opposed to a crisis.

Occurrence and risk assessment of pharmaceuticals and personal care products (PPCPs) against COVID-19 in lakes and WWTP-river-estuary system in Wuhan, China

The consumption of pharmaceuticals and personal care products (PPCPs) for controlling and preventing the COVID-19 would have sharply increased during the pandemic. To evaluate their post-pandemic environmental impacts, five categories of drugs were detected in lakes and WWTP-river-estuary system near hospitals of Jinyintan, Huoshenshan and Leishenshan in the three regions (J, H and L) (Regions J, H and L) in Wuhan, China. The total amount of PPCPs (ranging from 2.61 to 1122 ng/L in water and 0.11 to 164 ng/g dry weight in sediments) were comparable to historical reports in Yangtze River basin, whereas the detection frequency and concentrations of ribavirin and azithromycin were higher than those of historical studies. The distribution of concerned drugs varied with space, season, media and water types: sampling sites located at WWTPs-river-estuary system around two hospitals (Regions L and J) usually had relatively high waterborne contamination levels, most of which declined in autumn; lakes had relatively low waterborne contamination levels in summer but increased in autumn. The potential risks of detected PPCPs were further evaluated using the multiple-level ecological risk assessment (MLERA): sulfamethoxazole and azithromycin were found to pose potential risks to aquatic organisms according to a semi-probabilistic approach and classified as priority pollutants based on an optimized risk assessment. In general, the COVID-19 pandemic did not cause serious pollution in lakes and WWTPs-river-estuary system in Wuhan City. However, the increased occurrence of certain drugs and their potential ecological risks need further attention. A strict source control policy and an advanced monitoring and risk warning system for emergency response and long-term risk control of PPCPs is urgent.

Early warning and real-time control of construction safety risk of underground engineering based on building information modeling and internet of things

The objective of this study is to establish a safety risk warning system for the construction of underground engineering and to solve the problems existing in current construction of underground engineering, such as large investment, long construction period, complex construction environment, and many unforeseen risk factors. In this research, building information modeling (BIM) technology and Internet of things technology are used to analyze the construction of safety risk warning system for underground engineering. Firstly, the concept and application of BIM technology and safety risk warning of underground engineering construction are introduced. Then, aiming at the existing safety problems of underground construction, the construction principle of underground construction safety indicator system, the establishment steps of indicator system and safety risk indicator system are introduced in detail, and a set of early warning and control system of safety risk of underground construction based on BIM technology and Internet of things technology is established. At last, the working mode and real-time monitoring of underground engineering early warning based on BIM technology and Internet of things are expounded. The results show that the underground engineering prediction system based on BIM technology and the Internet of things adopts the working mode that combines the circular safety risk warning wheel mode and the three-dimensional safety risk warning mode. Moreover, the real-time dynamic monitoring of the security risk warning system can be achieved through the periodic recording and testing of process indicators by the BIM management platform. It is hoped that this study can provide a good management platform for underground construction safety and reduce the occurrence of safety accidents in underground construction.