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

2014 ◽  
Vol 2014 (1) ◽  
pp. 299678
Author(s):  
Rodrigo Fernandes ◽  
Filipe Lourenço ◽  
Frank Braunschweig ◽  
Ramiro Neves
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Oil Spill ◽  
Numerical Models ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Risk Levels ◽  
Dynamic Risk ◽  
Risk Algorithm ◽  
The Impact

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.

scholarly journals Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

2015 ◽  
Vol 12 (4) ◽  
pp. 1327-1388 ◽  
Author(s):  
R. Fernandes ◽  
F. Braunschweig ◽  
F. Lourenço ◽  
R. Neves
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Oil Spill ◽  
Situational Awareness ◽  
Assessment Tool ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Automatic Identification ◽  
Decision Support Model ◽  
Risk Levels

Abstract. The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events. A combined methodology to dynamically estimate time and space variable shoreline risk levels from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area – Portuguese Continental shelf – 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 virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time. Shoreline risks can be computed in real-time or from previously obtained data. Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical 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.

scholarly journals Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

Ocean Science ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 285-317 ◽  
Author(s):  
R. Fernandes ◽  
F. Braunschweig ◽  
F. Lourenço ◽  
R. Neves
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Oil Spill ◽  
Assessment Tool ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Automatic Identification ◽  
Accident Risk ◽  
Decision Support Model ◽  
Risk Levels

Abstract. The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events. A combined methodology to dynamically estimate time and space variable individual vessel accident risk levels and shoreline contamination risk from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area – the Portuguese continental shelf – 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 virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time, or as an alternative, a correction factor based on vessel distance from coast. Shoreline risks can be computed in real time or from previously obtained data. Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical 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.

Applying the risk principle to optimize accuracy and equity in correctional risk assessment: Results From a Simulation

2021 ◽  
pp. 0306624X2098652
Author(s):  
Grant Duwe
Keyword(s):  
Risk Assessment ◽  
Predictive Accuracy ◽  
Ethnic Disparities ◽  
Simulated Data ◽  
Risk Assessments ◽  
Risk Level ◽  
Risk Levels ◽  
Risk Principle ◽  
Changes In Risk ◽  
The Impact

As the use of risk assessments for correctional populations has grown, so has concern that these instruments exacerbate existing racial and ethnic disparities. While much of the attention arising from this concern has focused on how algorithms are designed, relatively little consideration has been given to how risk assessments are used. To this end, the present study tests whether application of the risk principle would help preserve predictive accuracy while, at the same time, mitigate disparities. Using a sample of 9,529 inmates released from Minnesota prisons who had been assessed multiple times during their confinement on a fully-automated risk assessment, this study relies on both actual and simulated data to examine the impact of program assignment decisions on changes in risk level from intake to release. The findings showed that while the risk principle was used in practice to some extent, the simulated results showed that greater adherence to the risk principle would increase reductions in risk levels and minimize the disparities observed at intake. The simulated data further revealed the most favorable outcomes would be achieved by not only applying the risk principle, but also by expanding program capacity for the higher-risk inmates in order to adequately reduce their risk.

scholarly journals Artificial Intelligence-Enabled Traffic Monitoring System

2020 ◽  
Vol 12 (21) ◽  
pp. 9177
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Stage Of Development ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

scholarly journals Construction worker productivity in hot and humid weather conditions

2019 ◽  
Vol 27 (1) ◽  
pp. 83-108
Author(s):  
Ammar Saeed Mohammed Moohialdin ◽  
Fiona Lamari ◽  
Marc Miska ◽  
Bambang Trigunarsyah
Keyword(s):  
Real Time ◽  
Construction Industry ◽  
Productivity Loss ◽  
Weather Conditions ◽  
Research Field ◽  
Future Research ◽  
Time Data ◽  
Worker Productivity ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper shows the effect of hot and humid weather conditions (HHWCs) on workers that has resulted in considerable loss in the construction industry, especially during the hottest periods due to decline in worker productivity (WP). Until the last few decades, there is very limited research on construction WP in HHWCs. Nevertheless, these studies have sparked interests on seeking for the most appropriate methods to assess the impact of HHWCs on construction workers. Design/methodology/approach This paper begins by reviewing the current measuring methods on WP in HHWCs, follows by presenting the potential impact of HHWCs on WP. The paper highlights the methodological deficiencies, which consequently provides a platform for scholars and practitioners to direct future research to resolve the significant productivity loss due to global warming. This paper highlights the need to identify the limitations and advantages of the current methods to formulate a framework of new approaches to measure the WP in HHWCs. Findings Results show that the methods used in providing real-time response on the effects of HHWCs on WP in construction at project, task and crew levels are limited. An integration of nonintrusive real-time monitoring system and local weather measurement with real-time data synchronisation and analysis is required to produce suitable information to determine worker health- and safety-related decisions in HHWCs. Originality/value The comprehensive literature review makes an original contribution to WP measurements filed in HHWCs in the construction industry. Results of this review provide researchers and practitioners with an insight into challenges associated with the measurements methods and solving practical site measurements issues. The findings will also enable the researchers and practitioners to bridge the identified research gaps in this research field and enhance the ability to provide accurate measures in HHWCs. The proposed research framework may promote potential improvements in the productivity measurements methods, which support researchers and practitioners in developing new innovative methods in HHWCs with the integration of the most recent monitoring technologies.

scholarly journals Exploring the Spatial-Temporal Relationship between Rainfall and Traffic Flow: A Case Study of Brisbane, Australia

2020 ◽  
Vol 12 (14) ◽  
pp. 5596 ◽  
Author(s):  
Yanmin Qi ◽  
Zuduo Zheng ◽  
Dongyao Jia
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Transportation Network ◽  
Central Business District ◽  
Weather Conditions ◽  
Inclement Weather ◽  
Real Time Traffic ◽  
Policy Analysts ◽  
Business District ◽  
The Impact

The impact of inclement weather on traffic flow has been extensively studied in the literature. However, little research has unveiled how local weather conditions affect real-time traffic flows both spatially and temporally. By analysing the real-time traffic flow data of Traffic Signal Controllers (TSCs) and weather information in Brisbane, Australia, this paper aims to explore weather’s impact on traffic flow, more specifically, rainfall’s impact on traffic flow. A suite of analytic methods has been applied, including the space-time cube, time-series clustering, and regression models at three different levels (i.e., comprehensive, location-specific, and aggregate). Our results reveal that rainfall would induce a change of the traffic flow temporally (on weekdays, Saturday, and Sunday and at various periods on each day) and spatially (in the transportation network). Particularly, our results consistently show that the traffic flow would increase on wet days, especially on weekdays, and that the urban inner space, such as the central business district (CBD), is more likely to be impacted by inclement weather compared with other suburbs. Such results could be used by traffic operators to better manage traffic in response to rainfall. The findings could also help transport planners and policy analysts to identify the key transport corridors that are most susceptible to traffic shifts in different weather conditions and establish more weather-resilient transport infrastructures accordingly.

Risk Assessment Overrides: Shuffling the Risk Deck Without Any Improvements in Prediction

2020 ◽  
Vol 47 (12) ◽  
pp. 1609-1629
Author(s):  
Thomas H. Cohen ◽  
Christopher T. Lowenkamp ◽  
Kristin Bechtel ◽  
Anthony W. Flores
Keyword(s):  
Risk Assessment ◽  
Risk Prediction ◽  
Risk Classification ◽  
Federal System ◽  
Sex Offenses ◽  
Supervision System ◽  
Risk Levels ◽  
Risk Reclassification ◽  
The Impact

In the federal supervision system, officers have discretion to depart from the risk designations provided by the Post Conviction Risk Assessment (PCRA) instrument. This component of the risk classification process is referred to as the supervision override. While the rationale for allowing overrides is that actuarial scores cannot always capture an individual’s unique characteristics, there is relatively limited literature on the actual effects of overrides on an actuarial tool’s predictive efficacies. This study examines overrides in the federal system by assessing the extent to which risk levels are adjusted through overrides as well as the impact of overrides on the PCRA’s risk prediction effectiveness. Findings show that nearly all overrides lead to an upward risk reclassification, that overrides tend to place substantial numbers of persons under federal supervision (especially those convicted of sex offenses) into the highest supervision categories, and that overrides result in a deterioration of the PCRA’s risk prediction capacities.

scholarly journals Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

2017 ◽  
Author(s):  
Nicola Paltrinieri ◽  
Gabriele Landucci ◽  
Pierluigi Salvo Rossi
Keyword(s):  
Risk Assessment ◽  
Risk Analysis ◽  
Real Time ◽  
Risk Evaluation ◽  
Oil And Gas ◽  
Time Data ◽  
Dynamic Risk ◽  
System Risk ◽  
Offshore Oil And Gas ◽  
Offshore Oil

Recent major accidents in the offshore oil and gas (O&G) industry have showed inadequate assessment of system risk and demonstrated the need to improve risk analysis. While direct causes often differ, the failure to update risk evaluation on the basis of system changes/modifications has been a recurring problem. Risk is traditionally defined as a measure of the accident likelihood and the magnitude of loss, usually assessed as damage to people, to the environment, and/or economic loss. Recent revisions of such definition include also aspects of uncertainty. However, Quantitative Risk Assessment (QRA) in the offshore O&G industry is based on consolidated procedures and methods, where periodic evaluation and update of risk is not commonly carried out. Several methodologies were recently developed for dynamic risk analysis of the offshore O&G industry. Dynamic fault trees, Markov chain models for the life-cycle analysis, and Weibull failure analysis may be used for dynamic frequency evaluation and risk assessment update. Moreover, dynamic risk assessment methods were developed in order to evaluate the risk by updating initial failure probabilities of events (causes) and safety barriers as new information are made available. However, the mentioned techniques are not widely applied in the common O&G offshore practice due to several reasons, among which their complexity has a primary role. More intuitive approaches focusing on a selected number of critical factors have also been suggested, such as the Risk Barometer or the TEC2O. Such techniques are based on the evaluation of technical, operational and organizational factors. The methodology allows supporting periodic update of QRA by collecting and aggregating a set of indicators. However, their effectiveness relies on continuous monitoring activity and realtime data capturing. For this reason, this contribution focuses on the coupling of such methods with sensors of different nature located in or around and offshore O&G system. The inheritance from the Centre for Integrated Operations in the Petroleum Industries represents the basis of such study. Such approach may be beneficial for several cases in which (quasi) real-time risk evaluation may support critical operations. Two representative cases have been described: i) erosion and corrosion issues due to sand production; and ii) oil production in environmental sensitive areas. In both the cases, dynamic risk analysis may employ real-time data provided by sand, corrosion and leak detectors. A simulation of dynamic risk analysis has demonstrated how the variation of such data can affect the overall risk picture. In fact, this risk assessment approach has not only the capability to continuously iterate and outline improved system risk pictures, but it can also compare its results with sensor-measured data and allow for calibration. This can potentially guarantee progressive improvement of the method reliability for appropriate support to safety-critical decisions.

Professional Discretion and the Predictive Validity of a Juvenile Risk Assessment Instrument

2016 ◽  
Vol 15 (2) ◽  
pp. 103-118 ◽  
Author(s):  
James T. McCafferty
Keyword(s):  
Risk Assessment ◽  
Predictive Validity ◽  
Assessment Tool ◽  
Assessment Instrument ◽  
Adjusted Risk ◽  
Risk Levels ◽  
Risk Assessment Instrument ◽  
Actuarial Risk ◽  
Using Data ◽  
The Impact

The ability for professionals to override the results of an actuarial risk assessment tool is an essential part of effective correctional risk classification; however, little is known about how this important function affects the predictive validity of these tools. Using data from a statewide sample of juveniles from Ohio, this study examined the impact of professional adjustments on the predictive validity of a juvenile risk assessment instrument. This study found that the original and adjusted risk levels were significant predictors of recidivism, but the original risk levels were stronger predictors of recidivism than the adjusted risk levels that accounted for overrides.

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