Dynamic Risk Map Research Articles

Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation.

During the COVID-19 pandemic, individuals depended on risk information to make decisions about everyday behaviors and public policy. Here, we assessed whether an interactive website influenced individuals' risk tolerance to support public health goals. We collected data from 11,169 unique users who engaged with the online COVID-19 Event Risk Tool (https://covid19risk.biosci.gatech.edu/) between 9/22/21 and 1/22/22. The website featured interactive elements, including a dynamic risk map, survey questions, and a risk quiz with accuracy feedback. After learning about the risk of COVID-19 exposure, participants reported being less willing to participate in events that could spread COVID-19, especially for high-risk large events. We also uncovered a bias in risk estimation: Participants tended to overestimate the risk of small events but underestimate the risk of large events. Importantly, even participants who voluntarily sought information about COVID risks tended to misestimate exposure risk, demonstrating the need for intervention. Participants from liberal-leaning counties were more likely to use the website tools and more responsive to feedback about risk misestimation, indicating that political partisanship influences how individuals seek and engage with COVID-19 information. Lastly, we explored temporal dynamics and found that user engagement and risk estimation fluctuated over the course of the Omicron variant outbreak. Overall, we report an effective large-scale method for communicating viral exposure risk; our findings are relevant to broader research on risk communication, epidemiological modeling, and risky decision-making.

A dynamic, convenient and accurate method for assessing the flood risk of people and vehicle

With the increase of extreme rainstorm caused by climate change, and the development of urbanization and the improvement of people's living standard, there is an urgent need to draw a dynamic, convenient and accurate flood risk map for different disaster bearing bodies, so as to protect people's lives and properties, as well as improve people's risk awareness and facilitate people's lives. This study mainly researched a method of drawing a dynamic, convenient and accurate flood risk map for people and vehicle. In this study, the surface runoff is simulated by GPU (Graphics Processing Unit) Accelerated Surface Water Flow and Transport model (GAST model), meanwhile, the flood risk of people and vehicle is graded base on the incipient velocity formulas, the most unfavorable principle and grading method, finally, the method is applied in two application cases. The following results are obtained: (1) this method could assess the flood risk of people and vehicle dynamically, conveniently and accurately; (2) the flood risk of people is less than that of vehicle, for the same flood hazard, time and place; (3) the adverse effect of water depth on flood risk for vehicle is greater than that for people. This method of assessing the flood risk map of people and vehicle is of great significance, for flood risk management, land use plan and emergency management department to reduce flood disaster risk.

Integration of data analytics with cloud services for safer process systems, application examples and implementation challenges

Emerging sensors, computers, network technologies, and connected platforms result potentially in an immeasurable collection of data within plant operations. This creates the possibility of solving problems innovatively. Because most of the data appear to be unstructured or semi-structured, organizations shall design and adopt new strategies. Further, workflow architectures with data analytics are needed including machine learning tools and artificial intelligence techniques before proto-type solutions can be developed. We shall discuss several prospects of using (big) data analytics integrated with cloud services to produce solutions for improving plant operations. The paper outlines the vision and a systematic framework highlighting the data analytics lifecycle in the area of plant operation, process safety, and environmental protection. Four rather diverse example case studies are demonstrated including (1) deep learning-based predictive maintenance monitoring modeling, (2) Natural Language Processing (NLP) for mining text, (3) barrier assessment for dynamic risk mapping (DRA), and (4) correlation development for sustainability indicators. It further discusses the challenges in both research and implementation of proposed solutions in the industry. It is concluded that a well-balanced integrated approach including machine supporting decisions integrated with expert knowledge and available information from various key resources is required to enable more informed policy, strategic, and operational risk decision-making leading to safer, reliable and more efficient operations.

A probabilistic risk assessment framework considering lane-changing behavior interaction

Understanding the dynamic characteristics of surrounding vehicles and estimating the potential risk of mixed traffic can help reliable autonomous driving. However, the existing risk assessment methods are challenging to detect dangerous situations in advance and tackle the uncertainty of mixed traffic. In this paper, we propose a probabilistic driving risk assessment framework based on intention identification and risk assessment of surrounding vehicles. Firstly, we set up an intention identification model (IIM) via long short-term memory (LSTM) networks to identify the intention possibility of the surrounding vehicles. Then a risk assessment model (RAM) based on the driving safety field is employed to output the potential risk. Specifically, driving safety field can reflect the coupling relationship of drivers, vehicles, and roads by analyzing their interaction. Finally, an integrated risk evaluation model combining both IIM and RAM is developed to form a dynamic potential risk map considering multi-vehicle interaction. For example, in a typical but challenging lane-changing scenario, an intelligent vehicle can assess its driving status by calculating a risk map in real time that represents the risk generated by the estimated intentions of surrounding vehicles. Furthermore, simulations and naturalistic driving experiments are conducted in the extracted lane-changing scenarios, and the results verify the effectiveness of the proposed model considering lane-changing behavior interaction.

New method for environmental monitoring in armed conflict zones: a case study of Syria.

Today, armed conflict affects some twenty countries, covering an area making up 11% of the surface area of the Earth. Any degradation of nature in these areas represents a harmful depletion of the world's natural heritage. Despite this, environmental issues are neglected during these periods of conflict, considered secondary to the urgency of restoring peace and safeguarding human life. Yet their consequences are potentially severe. In these areas, it is future generations who will suffer the effects of the current devastation for a very long time. In this context, the method developed in this study, named (Geographic Information System) for Environmental Monitoring in Wartime, can be used to calculate a risk indicator for environmental degradation, spatial monitoring and risk management. This will make it possible to identify the main threats to protected areas, catalogue the damage caused to the environment by armed conflicts and create a dynamic risk map. In this paper, GIS-EMW has been applied to calculate a risk indicator for environmental degradation in Syria.

How Big Data & Analytics can improve process and plant safety and become an indispensable tool for risk management

With the advances in digitization, Information Technology (IT), and connected devices, data are becoming plentiful. And with the past 30 years of developments of Artificial Intelligence tools leading to great enhancements in dealing with various levels and types of uncertainty, much has become tangible, where in the past it used to remain vague and fuzzy. Tools like neural networks can distil information from datasets, while probabilistic methods can characterize randomness. Bayesian causation networks enable finding critical pathways and help to design and monitor effective safeguards, while Petri nets enable analysis of time-critical events. Interval analysis, Dempster-Shafer theory, and fuzzy logic can assist in delimiting uncertainty in measurement results and expert judgment. System dynamics modeling and Functional resonance analysis may unravel interactively degrading processes. All this can improve understanding about communication lines and mechanisms of interactions within a plant socio-technical system, and the influences on achievement and performance. This will result in reformed work processes, manufacturing conditions and help in identifying abnormal trends. Therefore, while planning and prediction are based on observational evidence and trends, the new technologies will be a strong support for management, in recognizing and evaluating risks, including safety risks. Although applications of big data and analytics are still young, nevertheless in process control and reliability prediction of equipment a few achievements have already been demonstrated. However, much more is possible. For example, in the case of process safety performance indicators, lagging indicators are usually available but the techniques may stimulate the recording of the more important leading indicators for the prediction of safety and culture trend in a company in relation to its economic health. The paper will present more details on the methods and an example of dynamic risk mapping.

A Risk-Aware Path Planning Strategy for UAVs in Urban Environments

This paper presents a risk-aware path planning strategy for Unmanned Aerial Vehicles in urban environments. The aim is to compute an effective path that minimizes the risk to the population, thus enforcing safety of flight operations over inhabited areas. To quantify the risk, the proposed approach uses a risk-map that associates discretized locations of the space with a suitable risk-cost. Path planning is performed in two phases: first, a tentative path is computed off-line on the basis on the information related to static risk factors; then, using a dynamic risk-map, an on-line path planning adjusts and adapts the off-line path to dynamically arising conditions. Off-line path planning is performed using riskA*, an ad-hoc variant of the A* algorithm, which aims at minimizing the risk. While off-line path planning has no stringent time constraints for its execution, this is not the case for the on-line phase, where a fast response constitutes a critical design parameter. We propose a novel algorithm called Borderland, which uses the check and repair approach to rapidly identify and adjust only the portion of path involved by the inception of relevant dynamical changes in the risk factor. After the path planning, a smoothing process is performed using Dubins curves. Simulation results confirm the suitability of the proposed approach.

Dynamic Risk Assessment of Shoreline Contamination from Ships: Integrating an Oil Spill Model

Latest scientific and technological developments on coastal monitoring and operational oceanography have provided the opportunity of building complex and integrative decision support systems for coastal risk management. An innovative methodology to dynamically produce quantified risks has been developed, integrating numerical metocean forecasts and oil spill simulations with the existing monitoring tools. The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in the study area with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the associated oil amount reaching shoreline and the environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behavior model. Shoreline risk is variable in time, based on variable vessel positions (from AIS) and metocean conditions (from operational numerical models). The simultaneous calculation of the risk posed by each vessel crossing a study area is integrated, allowing the generation of a dynamic shoreline risk map for the study area. Shoreline risks can be computed in real time or from previous obtained data. The whole system has been implemented in real time on the Portuguese and Galician Coast. Since several ships cross this area, optimization was performed to allow running the oil spill model for multiple virtual spills from ships along time. The integrated oil spill model uses MOHID lagrangian particle tracking system, where all major transport and weathering processes are considered, including full 3D movement of oil particles, wave-induced currents, and a novel implementation of oil-shoreline interaction. The relevance of integrating the oil spill model in the risk algorithm is evaluated. To perform this, risk levels are compared considering the impact of virtual spilled oil reaching shoreline based on oil spill model simulations, or simply considering the vessel shoreline proximity as impact factor. The integration of an oil spill model in the shoreline risk levels, combined with adequate metocean modeling forecasts, allow a more realistic approach in the assessment of shoreline impacts, which can become even more important in case of regions with greater variability in marine weather conditions. The risk assessment from historic data can help finding typical risk patterns, “hot spots” or developing sensitivity analysis to specific conditions, whereas real time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

Construction of Dynamic Risk Maps for Large Metropolitan Areas

The paper describes a methodology used for constructing dynamic risk map for a virtual large «Russian GothamRG» city. The risk map is the core of the safety passport (SP) for the city, as required by the Russian EMERCOM and the Russian State Agency for Industrial Safety. The SP contains extensive risk analysis of all potential dangerous objects located within the boundaries of the city. It analyzes the natural and technological threats/disasters that may occur in the city.

Optimal investment policy in the time consistent mean–variance formulation

As a necessary requirement for multi-period risk measure, time consistency can be examined from two aspects: dynamic risk measure and optimal investment policy. In this paper, we first study the relationship between the time consistency of dynamic risk measure and the time consistency of optimal investment policy and obtain the following conclusions: if the dynamic risk mapping is time consistent and monotone, then the corresponding optimal investment policy satisfies the time consistency requirements; however, if the dynamic risk mapping is time consistent but not monotone, then the time consistency requirements of an optimal investment policy will no longer be satisfied. Since the variance operator does not satisfy the smoothing property, the optimal investment policy derived from the existing multi-period mean–variance model is not time consistent. To overcome this shortcoming, we propose the notation of a separable expected conditional mapping and then construct a time consistent dynamic mean–variance model. We prove that the optimal investment policy derived from our model is time consistent. Moreover, for two cases with or without a riskless asset, we obtain the time consistent analytical optimal investment policy and the mean–variance efficient frontier of the new model with the self-financing constraint. Finally, numerical results illustrate the flexibility and superiority of our multi-period mean–variance model and the optimal investment policy over those in the literature.

Temporal distribution and spatial pattern of abundance of the Rift Valley fever and West Nile fever vectors in Barkedji, Senegal

The temporal distribution and spatial pattern of abundance of mosquito vectors of Rift Valley fever (RVf) and West Nile fever (WNf) were studied during the 2005 and 2006 rainy seasons at Barkedji, Senegal. Mosquitoes were collected every two weeks with CDC light traps with dry ice at 79 sites including temporary ponds, barren, shrubby savannah, wooded savannah, steppes, and villages at different distances (between 0 and 600 m) from the nearest pond. The temporal distributions of these vectors varied between 2005 and 2006 and were positively correlated with rainfall for Aedes (Aedimorphus) vexans Patton, with rainfall after a lag time of one month for Culex (Culex) poicilipes (Theobald) and Culex (Culex) neavei Theobald. All the vectors had their highest abundances and parity rates between September and November. The highest vector abundances were observed in the barren and temporary ponds. The distance of trap location to the nearest ponds was negatively correlated to the abundance of the vectors. Taking into account the linear regression equations, it was predicted that mosquitoes would not disperse and be collected by the light trap, up to 1,500 m to the nearest ponds. The implications of these findings in the epidemiology and control of RVF and WNF at Barkedji are discussed.

Multi-factor Risk Analysis in a Building Fire by Two Step Cluster

Fire simulations and sensors are widely used in building fires, various data such as temperature, CO and CO2 concentration, visibility can be obtained by sensors and sensor-based simulation. And Injury produced by high temperature, carbon monoxide, or carbon dioxide may reduce a person's capability of judging, acting and memory. It is important to generate a risk map based on such data so that we can use it to make evacuation strategies. Using cluster analysis, likelihood distance is used to calculate the similarity of space state and danger state or safety state to get the risk level. The integrated influence of high temperature, carbon monoxide, or carbon dioxide on the occupants in a virtual building fire is dynamic analyzed to generate a risk map in the building. The dynamic risk map can realize the transformation of real-time signal to visualization risk distribution and support evacuation and rescue command.