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Madsen PM, Dillon RL, Morris ET. Using near misses, artificial intelligence, and machine learning to predict maritime incidents: A U.S. Coast Guard case study. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024. [PMID: 39009377 DOI: 10.1111/risa.15075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 05/24/2024] [Accepted: 06/21/2024] [Indexed: 07/17/2024]
Abstract
Two recent trends made this project possible: (1) The recognition that near misses can be predictors of future negative events and (2) enhanced artificial intelligence (AI) and machine learning (ML) tools that make data analytics accessible for many organizations. Increasingly, organizations are learning from prior incidents to improve safety and reduce accidents. The U.S. Coast Guard (USCG) uses a reporting system called the Marine Information for Safety and Law Enforcement (MISLE) database. Because many of the incidents that appear in this database are minor ones, this project initially focused on determining if near misses in MISLE could be predictors of future accidents. The analysis showed that recent near-miss counts are useful for predicting future serious casualties at the waterway level. Using this finding, a predictive AI/ML model was built for each waterway type by vessel combination. Random forest decision tree AI/ML models were used to identify waterways at significant accident risk. An R-based predictive model was designed to be run monthly, using data from prior months to make future predictions. The prediction models were trained on data from 2007 to 2022 and tested on 10 months of data from 2022, where prior months were added to test the next month. The overall accuracy of the predictions was 92%-99.9%, depending on model characteristics. The predictions of the models were considered accurate enough to be potentially useful in future prevention efforts for the USCG and may be generalizable to other industries that have near-miss data and a desire to identify and manage risks.
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Affiliation(s)
- Peter M Madsen
- Marriott School of Business, Brigham Young University, Provo, Utah, USA
| | - Robin L Dillon
- McDonough School of Business, Georgetown University, Washington, District of Columbia, USA
| | - Evan T Morris
- Office of Standards and Evaluations, U.S. Coast Guard, Washington, District of Columbia, USA
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Sahay R, Estay D, Meng W, Jensen CD, Barfod MB. A Comparative Risk Analysis on CyberShip System with STPA-Sec, STRIDE and CORAS. Comput Secur 2023. [DOI: 10.1016/j.cose.2023.103179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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AbdolkhaniNezhad T, Monavari SM, Khorasani N, Robati M, Farsad F. Comparative analytical study of the results of environmental risk assessment of urban landfills approach: bowtie, network analysis techniques (ANP), TOPSIS (case study: Gilan Province). ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:854. [PMID: 36205805 PMCID: PMC9540170 DOI: 10.1007/s10661-022-10513-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
Most landfill projects run in a dynamic and complex environment; therefore, uncertainty and risk are inherent. To improve the performance and reduce the damage caused by waste, risk study and its management have become necessary in implementing landfill location projects. As a result of the biodegradation of organic matter in waste, landfills produce various materials such as leachate, and gas. Therefore, it is necessary to conduct environmental risk assessments so that the destructing factors and their effects on the environment can be identified, and subsequently, control and management solutions offered. In the present study, the author has identified the most critical risks of construction phases and operation of landfills in Gilan province, using the Analytic Network Process (ANP), Delphi, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) techniques. According to the results, the environmental sector represents the highest risk in the construction and operation phases. Therefore, solutions for reducing or eliminating adverse outcomes have been proposed according to the bowtie method. Solutions to reduce or eliminate the adverse effects of leachate leakage from the landfill floor that causes pollution and infiltration into groundwater: installation of a conventional control system. Routing of landfill gases by passing soil filters at the highest points of landfills using the bowtie method is recommended. The results showed that anthropogenic activities related to sanitary landfilling of waste have greatly affected Gilan province in recent years.
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Affiliation(s)
- Talieh AbdolkhaniNezhad
- Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Seyed Masoud Monavari
- Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Nematollah Khorasani
- Department of Environmental Sciences, Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | - Maryam Robati
- Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Forough Farsad
- Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Rawson A, Brito M, Sabeur Z. Spatial Modeling of Maritime Risk Using Machine Learning. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2022; 42:2291-2311. [PMID: 34854116 DOI: 10.1111/risa.13866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 09/14/2021] [Accepted: 11/09/2021] [Indexed: 06/13/2023]
Abstract
Managing navigational safety is a key responsibility of coastal states. Predicting and measuring these risks has a high complexity due to their infrequent occurrence, multitude of causes, and large study areas. As a result, maritime risk models are generally limited in scale to small regions, generalized across diverse environments, or rely on the use of expert judgement. Therefore, such an approach has limited scalability and may incorrectly characterize the risk. Within this article a novel method for undertaking spatial modeling of maritime risk is proposed through machine learning. This enables navigational safety to be characterized while leveraging the significant volumes of relevant data available. The method comprises two key components: aggregation of historical accident data, vessel traffic, and other exploratory features into a spatial grid; and the implementation of several classification algorithms that predicts annual accident occurrence for various vessel types. This approach is applied to characterize the risk of collisions and groundings in the United Kingdom. The results vary between hazard types and vessel types but show remarkable capability at characterizing maritime risk, with accuracies and area under curve scores in excess of 90% in most implementations. Furthermore, the ensemble tree-based algorithms of XGBoost and Random Forest consistently outperformed other machine learning algorithms that were tested. The resultant potential risk maps provide decisionmakers with actionable intelligence in order to target risk mitigation measures in regions with the greatest requirement.
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Affiliation(s)
- Andrew Rawson
- Electronics and Computer Science, University of Southampton, Highfield, Southampton, UK
| | - Mario Brito
- Centre for Risk Research, Southampton Business School, University of Southampton, Highfield, Southampton, UK
| | - Zoheir Sabeur
- Department of Computing and Informatics, Talbot Campus, University of Bournemouth, Bournemouth, UK
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Hassan S, Wang J, Kontovas C, Bashir M. Modified FMEA hazard identification for cross-country petroleum pipeline using Fuzzy Rule Base and approximate reasoning. J Loss Prev Process Ind 2022. [DOI: 10.1016/j.jlp.2021.104616] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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ANN-Based Integrated Risk Ranking Approach: A Case Study of Contaminants of Emerging Concern of Fish and Seafood in Europe. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041598. [PMID: 33567765 PMCID: PMC7915293 DOI: 10.3390/ijerph18041598] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/28/2021] [Accepted: 02/04/2021] [Indexed: 12/15/2022]
Abstract
Seafood, one of the most important food commodities consumed worldwide, is considered a high-quality, healthy, and safe food option. However, marine ecosystems are the ultimate destination for a large group of chemicals, including contaminants of emerging concern, and seafood consumption is a major pathway of human exposure. With growing awareness of food safety and food quality, and increased demand for information on the risk of contaminants of emerging concern, there is a need to assess food safety issues related to harmful contaminants in seafood and ensure the safety of marine food resources. In this study, the risks of emerging compounds (endocrine disruptors, brominated flame retardants, pharmaceuticals and personal care products, and toxic elements) in fish and seafood were analyzed according to their PBT (persistence, bioaccumulation, toxicity) properties as well as in terms of their concentration levels in seafood. A hazard index (HI) was estimated for each compound by applying an artificial neural network (ANN) approach known as Self-Organizing-Maps. Subsequently, an integrated risk rank (IRI) was developed considering the values of HI and the concentrations of emerging compounds in seafood species gathered from the scientific literature. Current results identified HHCB, MeHg, NP, AHTN and PBDE209 as the top five highest ranked compounds present in seafood, according to the 50th percentile (mean) of the IRI. However, this ranking slightly changed when taking into account the 99th percentile of the IRI, showing toxic elements, methylmercury and inorganic arsenic, as having the highest risk. The outcome of this study identified the priority contaminants and should help in regulatory decision-making and scientific panels to design screening programs as well as to take the appropriate safety measures.
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Cruise Ship Safety Management in Asian Regions: Trends and Future Outlook. SUSTAINABILITY 2020. [DOI: 10.3390/su12145567] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The sinking of the Titanic has brought cruise ship safety onto the international agenda. However, different shipwrecks have been occurring in the cruise industry with relatively high frequency for more than one century due to human errors. In order to improve cruise ship safety, the International Maritime Organization and the Cruise Lines International Association introduced a set of safety enhancement policies and measurements. However, the expansion of ships and fairly weak safety regulations continue to pose risks of human life loss during cruise ship accidents, particularly in Asian regions. Asian countries have been constantly implementing various safety measures, but serious cruise ship accidents still occur from time to time, even after significant past experiences. Are the cruise ship accidents predominantly the result of human failures and organizational factors? This paper undertakes a detailed historical review of cruise ship accidents since 1972 through an intensive overview of the documents published by the Safety of Life at Sea (SOLAS) Convention and the Maritime Safety Committee. Furthermore, a set of case studies of representative cruise ship accidents are conducted as a part of this study. The outcomes of this study will help cruise shipping companies to better understand the factors influencing cruise ship accident occurrence and to construct appropriate safety policy measures, aiming to prevent cruise ship accidents in Asian regions.
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Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2019. [DOI: 10.3390/jmse7090299] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Liquefied nature gas (LNG) is a green energy. LNG-fueled vessels are extremely complex engineering systems. In view of the inherent hazardous properties of LNG fuel, LNG fueling is not only an important part, but it is also full of high risks in the operation of LNG-fueled vessels (LNGFVs). Therefore, it is necessary to study the risk factors, and the intrinsic relationship among them between the LNG and the vessel, and to simulate the system dynamics in the process of LNGFV operation. During the process of fueling of LNGFV, at every moment the vessel interacts with the energy and information of the surrounding environment. First, the impact of the three interactions of the fueling operation process, ship factors, and environmental factors were analyzed on the risk of fueling operation, and a complete node system was proposed as to the complex system dynamics mode. Second, by analyzing the boundary conditions of the system, the relationship of factors was established via the tools of system dynamics (SD). Based on the catastrophe theory (CA), the dynamics model for the fueling of LNG is set up to study the system’s risk mutation phenomenon. Third, combined with the simulation results of the case analysis, the risk evolution mode of the LNGFV during the fueling process was obtained, and constructive opinions were put forward for improving the safe fueling of the LNGFV. Application examples show that formal description of risk emergence and transition is a prerequisite for the quantitative analysis of the risk evolution mode. In order to prevent accidents, the coupling synchronization of risk emergence should be weakened, and meanwhile risk control should be implemented.
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Nadal M, Kumar V, Schuhmacher M, Domingo JL. Applicability of a neuroprobabilistic integral risk index for the environmental management of polluted areas: a case study. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2008; 28:271-286. [PMID: 18419648 DOI: 10.1111/j.1539-6924.2008.01018.x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Recently, we developed a GIS-Integrated Integral Risk Index (IRI) to assess human health risks in areas with presence of environmental pollutants. Contaminants were previously ranked by applying a self-organizing map (SOM) to their characteristics of persistence, bioaccumulation, and toxicity in order to obtain the Hazard Index (HI). In the present study, the original IRI was substantially improved by allowing the entrance of probabilistic data. A neuroprobabilistic HI was developed by combining SOM and Monte Carlo analysis. In general terms, the deterministic and probabilistic HIs followed a similar pattern: polychlorinated biphenyls (PCBs) and light polycyclic aromatic hydrocarbons (PAHs) were the pollutants showing the highest and lowest values of HI, respectively. However, the bioaccumulation value of heavy metals notably increased after considering a probability density function to explain the bioaccumulation factor. To check its applicability, a case study was investigated. The probabilistic integral risk was calculated in the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain), where an environmental program has been carried out since 2002. The risk change between 2002 and 2005 was evaluated on the basis of probabilistic data of the levels of various pollutants in soils. The results indicated that the risk of the chemicals under study did not follow a homogeneous tendency. However, the current levels of pollution do not mean a relevant source of health risks for the local population. Moreover, the neuroprobabilistic HI seems to be an adequate tool to be taken into account in risk assessment processes.
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Affiliation(s)
- Martí Nadal
- Laboratory of Toxicology and Environmental Health, Rovira i Virgili University, Reus, Catalonia, Spain
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Ung ST, Williams V, Bonsall S, Wang J. Test case based risk predictions using artificial neural network. JOURNAL OF SAFETY RESEARCH 2006; 37:245-60. [PMID: 16820171 DOI: 10.1016/j.jsr.2006.02.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2005] [Revised: 12/16/2005] [Accepted: 02/27/2006] [Indexed: 05/10/2023]
Abstract
INTRODUCTION The traditional fuzzy-rule-based risk assessment technique has been applied in many industries due to the capability of combining different parameters to obtain an overall risk. However, a drawback occurs as the technique is applied in circumstances where there are multiple parameters to be evaluated that are described by multiple linguistic terms. METHOD In this study, a risk prediction model incorporating fuzzy set theory and Artificial Neural Network (ANN) capable of resolving the problem encountered is proposed. An algorithm capable of converting the risk-related parameters and the overall risk level from the fuzzy property to the crisp-valued attribute is also developed. Its application is demonstrated by a test case evaluating the navigational safety within port areas. RESULTS It is concluded that a risk predicting ANN model is capable of generating reliable results as long as the training data takes into account any potential circumstance that may be met. IMPACT ON INDUSTRY This paper provides safety assessment practitioners with a novel and flexible framework of modelling risks using a fuzzy-rule-base technique. It is especially applicable in circumstances where there are multiple parameters to be considered. The proposed framework also enables the port industry to manage navigational safety in a rational manner.
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Affiliation(s)
- S T Ung
- Marine, Offshore and Transport Research Group, School of Engineering, Liverpool John Moores University, Liverpool, L3 3AF, UK
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