The classical coastal vulnerability index (CVI) has five mutually exclusive integer scores: 1, 2, 3, 4, and 5. This paper proposes to plot the given US-ranked CVI input variables in polygonal graphical formats bounded by single straight lines for the maximum and minimum data sets for two, and three segments for each input variable, with extreme values and between maximum and minimum data set’s variation space is illustrated with logical rules of direct or inverse proportionality, leading to mathematical formulations. This innovative method is called the polygonal coastal index (IPCI), and the diagrams illustrate the relationship between each input variable and output scores. A numerical implementation of the IPCI concept is provided for an empirical data set selected from the variation space of each variable. The most important part of the IPCI calculation is the final output score estimate in the form of probabilistic theoretical cumulative distribution functions (CDFs). These functions enable risk and reliability assessment of vulnerability at various levels not achievable by any existing CVI procedure in the literature. It is possible without difficulty to extend and further develop the use of the IPCI procedure by taking into account as many input variables as possible.
Most existing reinforcement learning (RL)-based portfolio management models do not take into account the market conditions, which limits their performance in risk-return balancing. In this paper, we propose DeepTrader, a deep RL method to optimize the investment policy. In particular, to tackle the risk-return balancing problem, our model embeds macro market conditions as an indicator to dynamically adjust the proportion between long and short funds, to lower the risk of market fluctuations, with the negative maximum drawdown as the reward function. Additionally, the model involves a unit to evaluate individual assets, which learns dynamic patterns from historical data with the price rising rate as the reward function. Both temporal and spatial dependencies between assets are captured hierarchically by a specific type of graph structure. Particularly, we find that the estimated causal structure best captures the interrelationships between assets, compared to industry classification and correlation. The two units are complementary and integrated to generate a suitable portfolio which fits the market trend well and strikes a balance between return and risk effectively. Experiments on three well-known stock indexes demonstrate the superiority of DeepTrader in terms of risk-gain criteria.
In the immediacy of an event that disrupts the operation of an infrastructure, the time between its occurrence and the arrival of qualified personnel for emergency response can be valuable. For example, it can be used for gathering information about the status of the infrastructure by using automated reconnaissance devices. In an operation that precedes the intervention of human first responders, such devices can gather information about the situation, providing knowledge about the locations of stressors (e.g. fire), the inaccessibility of parts of the infrastructure or the presence of hazardous materials. In this study, we show how a Bayesian Networks can be used for knowledge representation and how it can be combined with methods from the realm of Multi-Criteria Decision Analysis (MCDA) for situation reconnaissance and route-optimisation in emergency situations, where different criteria (current belief about the location of zones of special interest, such as emergency exits, distance to the next point of interest, etc.) can be considered. As an example, we consider the case of an outbreak of a fire in a building. A pedantic check of all rooms by an automated reconnaissance device would take too long and thus delay intervention. Due to the limited time in which the building can be explored, the route is optimised to gather the greatest possible amount of information in the available time window. Results show how it is possible to maximise the information collected in a limited time window. This is done by discovering the location of fire and any hazardous materials through causal inferences automatically calculated by the Bayesian network. Route optimisation is facilitated by sequential MCDA using a parameter selection that meets the priorities of the specific application example.
This study focuses on localized groundwater flooding (GWF) in Zliten, Libya. The GWF caused significant damage to approximately 200 houses, leading to the relocation of 80 families. The lack of scientifically identified reasons for this groundwater upsurge poses challenges for effective remedial actions. To investigate the flooding causes, remote sensing techniques were employed. Preliminary results showed fluctuations in groundwater storage (GWS) over the past two decades in Zliten. Notably, a sustained decrease in groundwater levels occurred from 2008 to 2012. Sea Level Rise (SLR) patterns varied across Libya’s coastline, with Zliten experiencing an estimated mean SLR of 2.8 mm/yr. Satellite-based findings suggested a consistent decline in Zliten’s water storage capacity. It is possible that (i) overuse of the aquifers has disrupted the confined aquifer, leading to a groundwater upsurge, and/or (ii) recent extensive groundwater pumping activities have placed the confined aquifer under pressure exceeding atmospheric pressure. As a result, water has surged in the wells and even the land to relieve the pressure and reached its potentiometric level. An End-Member Mixing Analysis (EMMA) of water samples from the affected areas could further validate this hypothesis by determining the contributions of surface water, groundwater, or groundwater from the confined aquifer.
With the rapid advancement of industrialization and urbanization, emerging pollutants has brought unprecedented challenges to environmental protection and posed significant threats to human health. In this context, artificial intelligence (AI), leveraging its efficiency and precision, is gradually becoming a critical tool for emerging pollutant governance. This study reviews the current status and major challenges regarding emerging pollutant governance, and proposes an AI-based framework for managing emerging pollutants. In the screening phase, deep learning and natural language processing technologies are utilized to identify potential emerging pollutants from vast amounts of data, enhancing screening speed and accuracy. In risk assessment, machine learning models integrate multidimensional data to construct a dynamic evaluation system that can quantitatively assess environmental behaviors and health risks of pollutants in real time. In the control phase, AI technology enables intelligent monitoring, optimal technology selection, and dynamic regulation, promoting continuous optimization of governance strategies. Furthermore, the study proposes a large model framework for emerging pollutants, aiming to integrate multimodal environmental data to assist in the identification, risk assessment, and optimization of governance strategies for emerging pollutants. Research recommendations include establishing an intelligent identification and monitoring system for emerging pollutants, developing a data-driven risk assessment and prediction platform, optimizing pollution control technology and management platforms, and building a knowledge-driven large-model-assisted decision-making system. These efforts aim to precisely improve AI-based governance of emerging pollutants, providing references for scientific research, industry applications, and policy-making in related fields.
Bedrock-soil layer slopes (BSLSs) are widely distributed in nature. The existence of the interface between bedrock and soil layer (IBSL) affects the failure modes of the BSLSs, and the seismic action makes the failure modes more complex. In order to accurately evaluate the safety and its corresponding main failure modes of BSLSs under seismic action, a system reliability method combined with the upper bound limit analysis method and Monte Carlo simulation (MCS) is proposed. Four types of failure modes and their corresponding factors of safety (Fs) were calculated by MATLAB program coding and validated with case in existing literature. The results show that overburden layer soil’s strength, the IBSL’s strength and geometric characteristic, and seismic action have significant effects on BSLSs’ system reliability, failure modes and failure ranges. In addition, as the cohesion of the inclination angle of the IBSL and the horizontal seismic action increase, the failure range of the BSLS gradually approaches the IBSL, which means that the damage range becomes larger. However, with the increase of overburden layer soil’s friction angle, IBSL’s depth and strength, and vertical seismic actions, the failure range gradually approaches the surface of the BSLS, which means that the failure range becomes smaller.
Marco Bianchetti, Gabriele D'Acunto, Gianmarco De Francisci Morales
et al.
We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO), formulate the corresponding optimization problem, and propose an efficient Risk-Aware Trading Swarm (RATS) algorithm to solve it. The key elements of RATPO are a generic initial portfolio P, a specific set of Unique Eligible Instruments (UEIs), their combination into an Eligible Optimization Strategy (EOS), an objective function, and a set of constraints. RATS searches for an optimal EOS that, added to P, improves the objective function repecting the constraints. RATS is a specialized Particle Swarm Optimization method that leverages the parameterization of P in terms of UEIs, enables parallel computation with a large number of particles, and is fully general with respect to specific choices of the key elements, which can be customized to encode financial knowledge and needs of traders and risk managers. We showcase two RATPO applications involving a real trading portfolio made of hundreds of different financial instruments, an objective function combining both market risk (VaR) and profit&loss measures, constrains on market sensitivities and UEIs trading costs. In the case of small-sized EOS, RATS successfully identifies the optimal solution and demonstrates robustness with respect to hyper-parameters tuning. In the case of large-sized EOS, RATS markedly improves the portfolio objective value, optimizing risk and capital charge while respecting risk limits and preserving expected profits. Our work bridges the gap between the implementation of effective trading strategies and compliance with stringent regulatory and economic capital requirements, allowing a better alignment of business and risk management objectives.
Szymon Kalisz, Katarzyna Kibort, Joanna Mioduska
et al.
Waste generated due to mining activity poses a serious issue due to the large amounts generated, even up to 65 billion tons per year, and is often associated with the risk posed by its storage and environmental management. This work aims to review waste management in the mining industry of metals ores, coal, oil and natural gas. It includes an analysis and discussion on the possibilities for reuse of certain types of wastes generated from mining activity, and discusses the benefits, disadvantages and the impact of waste management on the environment. The article presents current methods of waste management arising during the extraction and processing of raw materials and the threats resulting from its application. Furthermore, the potential methods of mining waste management are discussed through an in-depth characterization of the properties and composition of various types of rocks. The presented work addresses not only the issues of more sustainable management of waste from the mining industry, but also responds to the current efforts to implement the assumptions of a circular economy, which is aimed at closing the loop. The methods of recycling by-products and treating waste as a resource more and more often not only meet environmental expectations, but also become a legal requirement. In this respect, the presented work can serve as a valuable support in decision-making about waste management.
Earthquakes are major catastrophes that cause great life and economic losses to human society and environment. This paper reviews and synthesizes relevant studies, drawing from a systematic examination of 4229 articles from the Web of Science core collection (1982–2023). Employing the CiteSpace visualization and analysis tool, current research and emerging trends in seismic risk assessment are discussed and analyzed. This paper provides a holistic overview of principal contributions, knowledge sources, interdisciplinary characteristics, and principal research topics in this field. Additionally, we propose key technologies that are in urgent need of enhancement, including data availability, quantity and quality of data, interpretability of machine learning models, performance improvement of machine learning methods and application of foundation models, as well as real-time risk assessment techniques. These insights support both theoretical understanding and practical applications of seismic risk assessment and damage analysis.
A significant roadblock to the development of principled multi-agent reinforcement learning is the fact that desired solution concepts like Nash equilibria may be intractable to compute. To overcome this obstacle, we take inspiration from behavioral economics and show that -- by imbuing agents with important features of human decision-making like risk aversion and bounded rationality -- a class of risk-averse quantal response equilibria (RQE) become tractable to compute in all $n$-player matrix and finite-horizon Markov games. In particular, we show that they emerge as the endpoint of no-regret learning in suitably adjusted versions of the games. Crucially, the class of computationally tractable RQE is independent of the underlying game structure and only depends on agents' degree of risk-aversion and bounded rationality. To validate the richness of this class of solution concepts we show that it captures peoples' patterns of play in a number of 2-player matrix games previously studied in experimental economics. Furthermore, we give a first analysis of the sample complexity of computing these equilibria in finite-horizon Markov games when one has access to a generative model and validate our findings on a simple multi-agent reinforcement learning benchmark.
In this paper, a novel cyber-insurance model design is proposed based on system risk evaluation with smart technology applications. The cyber insurance policy for power systems is tailored via cyber risk modeling, reliability impact analysis, and insurance premium calculation. A stochastic Epidemic Network Model is developed to evaluate the cyber risk by propagating cyberattacks among graphical vulnerabilities. Smart technologies deployed in risk modeling include smart monitoring and job thread assignment. Smart monitoring boosts the substation availability against cyberattacks with preventive and corrective measures. The job thread assignment solution reduces the execution failures by distributing the control and monitoring tasks to multiple threads. Reliability assessment is deployed to estimate load losses convertible to monetary losses. These monetary losses would be shared through a mutual insurance plan. To ensure a fair distribution of indemnity, a new Shapley mutual insurance principle is devised. Effectiveness of the proposed Shapley mutual insurance design is validated via case studies. The Shapley premium is compared with existent premium designs. It is shown that the Shapley premium has high indemnity levels closer to those of Tail Conditional Expectation premium. Meanwhile, the Shapley premium is nearly as affordable as the coalitional premium and keeps a relatively low insolvency probability.
In statistical analysis, many classic results require the assumption that models have finite mean or variance, including the most standard versions of the laws of large numbers and the central limit theorems. Such an assumption may not be completely innocent, and it may not be appropriate for datasets with heavy tails (e.g., catastrophic losses), relevant to financial risk management. In this paper, we discuss the importance of infinite-mean models in economics, finance, and related fields, with recent results and examples. We emphasize that many results or intuitions that hold for finite-mean models turn out to fail or even flip for infinite-mean models. Due to the breakdown of standard thinking for infinite-mean models, we argue that if the possibility of using infinite-mean models cannot be excluded, great caution should be taken when applying classic methods that are usually designed for finite-mean cases in finance and insurance.
INTRODUCTION Underground mining is considered one of the most hazardous industries and is often associated with serious work-related fatalities; this paper addresses job-related hazards and associated risks. METHOD A risk assessment approach is proposed (Pythagorean fuzzy environment) and a case study is carried out in an underground copper and zinc mine. RESULTS Results of the study demonstrate that hazards can be categorized into different risk levels via compromised solutions of the fuzzy approach. CONCLUSION The study provides a theoretical contribution by suggesting a Pythagorean fuzzy numbers-based VlseKriterijumska Optimizacija I Kompromisno Resenje (PFVIKOR) approach. Moreover, it contributes to improving overall safety levels of underground mining by considering and advising on the potential hazards of risk management. Practical applications: The proposed approach will improve the existing safety risk assessment mechanism in underground copper and zinc mining.
Nowadays one of the characteristic features of society's functioning is high uncertainty. This is manifested in the impossibility of predicting future events, which creates fear and apprehension to perform some activity in order to protect oneself from undesirable consequences. Uncertainty is the prerequisite for the risks emergence. The purpose of the study is to define the essence of "risk" concept, its classification and sys-tematization, which will allow a better understanding which factors affect tourism activity in the world for the further risk management programs formation. Despite the large number of studies, the authors mostly consider the risks of the tourism in-dustry which affect a separate tourist enterprise activities. The decisive factor for risk management at the micro (enterprise) level is the ability to respond quickly and flexibly, the presence of risk management programs and measures to eliminate consequences. Each group of risks has a different effect on a separate industry or enterprise, and mostly they have a negative character. The tourism industry suffers significantly from conditions caused by natural, climatic, eco-nomic, political, foreign economic and other factors. The most devastating event in recent years that caused a significant drop in tourism industry was the COVID-19 pandemic, with an average of 70% drop in international tourist arrivals for 2020-2022. Due to the existing risks, the tourism industry bears significant financial losses, bankruptcies or structural changes. Different risks require appro-priate solutions and support measures. To carry out the research, general scientific and special methods were used, in particular the-oretical generalization - to highlight the theoretical aspects of risks in tourism; synthesis, compari-son and systematization; statistical analysis method - for analyzing statistical data of tourist flows. The information base is scientific publications of Ukrainian and foreign scientists, as well as data from the World Tourism Organization (WTO). The conducted study of the tourism industry and the risks impact on it gives reason to con-clude that it is very sensitive to the risks that arise in society, even one adverse event can leave a negative impression on the destination or the tourist attraction of the country. The results of this re-search can be useful to develop practical recommendations for risk management at the level of en-terprises and the industry as a whole. The article proposes a classification of risks in tourism with information about countries that are most vulnerable to such phenomena as natural disasters, terrorism, environmental and political dangers, etc., thanks to which it is possible to assess the safety of individual destinations. Examples of the various factors influence on the tourism industry of the countries and regions are also given.
Decision theory recognizes two principal approaches to solving problems under uncertainty: probabilistic models and cognitive heuristics. However, engineers, public planners and decision-makers in other fields seem to employ solution strategies that do not fall into either field, i.e., strategies such as robust design and contingency planning. In addition, identical strategies appear in several fields and disciplines, pointing to an important shared toolkit. The focus of this paper is to develop a systematic understanding of such strategies and develop a framework to better employ them in decision making and risk management. The paper finds more than 110 examples of such strategies and this approach to risk is termed RDOT: Risk-reducing Design and Operations Toolkit. RDOT strategies fall into six broad categories: structural, reactive, formal, adversarial, multi-stage and positive. RDOT strategies provide an efficient response even to radical uncertainty or unknown unknowns that are challenging to address with probabilistic methods. RDOT could be incorporated into decision theory using workflows, multi-objective optimization and multi-attribute utility theory. Overall, RDOT represents an overlooked class of versatile responses to uncertainty. Because RDOT strategies do not require precise estimation or forecasting, they are particularly helpful in decision problems affected by uncertainty and for resource-constrained decision making.
A. Tubis, S. Werbińska-Wojciechowska, Adam Wróblewski
Recently, there has been a growing interest in the mining industry in issues related to risk assessment and management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies have indicated that risk in mining should be analyzed not only in the human factor aspect, but also in strategic (environmental impact) and operational ones. However, there is a lack of research on systematic literature reviews and surveys of studies that would focus on these identified risk aspects simultaneously. Therefore, the purpose of this article is to develop a literature review in the area of analysis, assessment and risk management in the mining sector, published in the last decade and based on the concept of a human engineering system. Following this, a systematic search was performed with the use of Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria were: (a) not older than 10 years, (b) article written in English, (c) publication type (scientific article, book, book chapter), (d) published in chosen electronic collections (Springer, Taylor and Francis, Elsevier, Science Direct, JSTOR). This resulted in the selection of the 94 most relevant papers in the area. First, the general bibliometric analysis was conducted. Later, the selected papers in this review were categorized into four groups and the critical review was developed. One of the main advantages of this study is that the results are obtained from different scientific sources/databases thanks to using a multi-search tool. Moreover, the authors identified the main research gaps in the area of the implementation of risk management in the mining industry.
One of the greatest public health crises in recent times, the COVID-19 pandemic, has come with a myriad of challenges in terms of health communication and public cooperation to prevent the spread of the disease. Understanding which are the key determinants that make certain individuals more cooperative is key in effectively tackling pandemics and similar future challenges. In the present study (N = 800), we investigated whether gender differences in compliance with preventive health behaviors (PHB) at the onset of the COVID-19 pandemic could be established, and, if so, whether the personality traits of agreeableness and conscientiousness could help explain this presumed relationship. Consistent with our theorizing, we found women to score higher than men on agreeableness and conscientiousness, and to be more willing to comply with a set of PHB. Importantly, both personality traits were found to mediate the gender-compliance link. This means that women's greater compliance levels with PHB could, at least in part, be attributed to their higher agreeableness and conscientiousness scores. A greater understanding of the determinants of PHB in terms of gender and associated personality traits may help identify options for developing more effective communication campaigns, both in terms of communication channel selection and message content.