Hyeong-Joo Kim, Mahfuzur Rahman, Zulfiqar Hammad
et al.
This study examines environmental susceptibilities in coastal regions by integrating geospatial indices to evaluate flood susceptibility, drought severity, and crop water stress. A data fusion framework was developed using the composite coastal flood susceptibility index (CCFSI), composite drought index (CDI), and crop water stress index (CWSI), validated against 70 ground truth points. The analysis revealed that the “Very Low Flood” covered the largest area of 131,244 hectares, while the “Very High Flood” occupied 277.04 hectares. In drought severity, the “Very High Drought” dominated with 122,306 hectares, whereas the “Low Drought” spanned just 1.56 hectares. For crop water stress, the “Very Low CWSI” encompassed 93,116.8 hectares, and the “Very High CWSI” covered 10,791.9 hectares, highlighting the variations in susceptibility across regions. Validation metrics showed high reliability, with F1 scores of 0.91 for flood prediction and 0.92 for drought prediction. The composite maps highlighted key susceptibilities, with the “High Flood-High Drought” covering 3471.35 hectares, the “High Drought-High CWSI” spanning 13,162.60 hectares, and the “High Flood-High CWSI” limited to 12.51 hectares, emphasizing the areas most affected by combined environmental stressors. The proposed framework is scalable, adaptable, and provides valuable tools for policymakers to enhance climate resilience and sustainable resource management globally.
A devastating flash flood hit southwestern China on July 12, 2022, causing 18 fatalities in the Heishui River watershed of Pingwu County, Sichuan Province. Because global climate change and various human activities exacerbate flash flood damage, it is important to apply lessons from typical flood events to inform prevention, mitigation, and emergency strategies. This study used disaster site investigations and hydrological–hydrodynamic simulations to reconstruct the flooding process and response behaviors. Heavy rainfall in the upstream watershed was the direct cause of the flash flood. Indirect causes included the presence of built-up areas in the downstream estuary floodplain, coupled with limited flood risk perception and early warning capabilities in the mountainous regions, leading to slow emergency responses. This case study addresses flash flood modeling challenges in ungauged watersheds by demonstrating how post-disaster field investigations and historical flood mark data provide reliable validation to reproduce flood processes and inform prevention strategies.
Buhlebenkosi F. Mpofu, Nnenesi Kgabi, Stuart Piketh
This research used descriptive statistics to analyse rainfall trends in the Cuvelai- Etosha Basin (Namibia) over a 50-year historical period (1968 to 2018). The results revealed that rainfall fell over a period of 6 months between the months of November and April. Rainfall amounts were also observed to be higher in the first 3 months of each year, and annual levels ranged between 200 mm and 700 mm. The trend revealed that rainfall levels between 1977 and 1992 were consistently below the calculated average of 410 mm, and the rainfall amounts, and rain season were observed to have significantly shortened between the years 2009 and 2018. The rainfall trend observed over the 50-year period did not provide a definitive indication of whether the pattern followed a specific trajectory. The trend line’s position was below the average line for many seasons, and it indicated that many of the seasons experienced rainfall levels below the annual average; however, an increase was observed from the years 2008 -2012 and the year 2018 wherein the rainfall received was above average and fell intensely over a brief period and these are the years where flooding was reported.
Contribution: An epileptic pattern was observed that could not be used to definitively define a trend but was useful to highlight that there was an occurrence of episodes of heavy rainfall being experienced in the months of January through March and any resilience efforts need to be prioritised during this time.
This study pioneers the application of the Gai-Kapadia framework, originally developed for interbank contagion, to global equity markets. It offers a novel approach to assess systemic risk and default cascades. Using a 20-asset network (13 Brazilian and 7 developed market assets) from 2015 to 2025, we construct exposure-based networks from price co-movements, applying thresholds theta = 0.3 and theta = 0.5 to capture significant interconnections. Cascade dynamics are evaluated through Monte Carlo simulations (n = 1000) with shocks ranging from 10 to 50 percent, complemented by deterministic propagation analysis. Results show that high clustering among Brazilian assets (Ci approx 1.0) leads to localized contagion, with an average of 2.0 failed assets per simulation. In contrast, developed markets with lower connectivity (Ci approx 0.2 to 0.4) show resilience, with zero failures beyond Brazil in all scenarios. Network visualizations highlight structural vulnerabilities: deterministic cascades reach up to 20 assets at theta = 0.3, but only 3 to 4 at theta = 0.5. Risk measures such as VaR and CVaR at 95 percent confidence confirm higher tail risks in emerging markets. This adaptation of the Gai-Kapadia model provides a robust framework for systemic risk assessment. The findings suggest that regulators should target high-clustering nodes in emerging markets, while portfolio managers may benefit from the resilience of developed markets to enhance diversification.
We propose a novel framework for risk-sensitive reinforcement learning (RSRL) that incorporates robustness against transition uncertainty. We define two distinct yet coupled risk measures: an inner risk measure addressing state and cost randomness and an outer risk measure capturing transition dynamics uncertainty. Our framework unifies and generalizes most existing RL frameworks by permitting general coherent risk measures for both inner and outer risk measures. Within this framework, we construct a risk-sensitive robust Markov decision process (RSRMDP), derive its Bellman equation, and provide error analysis under a given posterior distribution. We further develop a Bayesian Dynamic Programming (Bayesian DP) algorithm that alternates between posterior updates and value iteration. The approach employs an estimator for the risk-based Bellman operator that combines Monte Carlo sampling with convex optimization, for which we prove strong consistency guarantees. Furthermore, we demonstrate that the algorithm converges to a near-optimal policy in the training environment and analyze both the sample complexity and the computational complexity under the Dirichlet posterior and CVaR. Finally, we validate our approach through two numerical experiments. The results exhibit excellent convergence properties while providing intuitive demonstrations of its advantages in both risk-sensitivity and robustness. Empirically, we further demonstrate the advantages of the proposed algorithm through an application on option hedging.
Systemic risk measures aggregate the risks from multiple financial institutions to find system-wide capital requirements. Though much attention has been given to assessing the level of systemic risk, less has been given to allocating that risk to the constituent institutions. Within this work, we propose a Nash allocation rule that is inspired by game theory. Intuitively, to construct these capital allocations, the banks compete in a game to reduce their own capital requirements while, simultaneously, maintaining system-level acceptability. We provide sufficient conditions for the existence and uniqueness of Nash allocation rules, and apply our results to the prominent structures used for systemic risk measures in the literature. We demonstrate the efficacy of Nash allocations with numerical case studies using the Eisenberg-Noe aggregation mechanism.
Every publicly traded U.S. company files an annual 10-K report containing critical insights into financial health and risk. We propose Tiny eXplainable Risk Assessor (TinyXRA), a lightweight and explainable transformer-based model that automatically assesses company risk from these reports. Unlike prior work that relies solely on the standard deviation of excess returns (adjusted for the Fama-French model), which indiscriminately penalizes both upside and downside risk, TinyXRA incorporates skewness, kurtosis, and the Sortino ratio for more comprehensive risk assessment. We leverage TinyBERT as our encoder to efficiently process lengthy financial documents, coupled with a novel dynamic, attention-based word cloud mechanism that provides intuitive risk visualization while filtering irrelevant terms. This lightweight design ensures scalable deployment across diverse computing environments with real-time processing capabilities for thousands of financial documents which is essential for production systems with constrained computational resources. We employ triplet loss for risk quartile classification, improving over pairwise loss approaches in existing literature by capturing both the direction and magnitude of risk differences. Our TinyXRA achieves state-of-the-art predictive accuracy across seven test years on a dataset spanning 2013-2024, while providing transparent and interpretable risk assessments. We conduct comprehensive ablation studies to evaluate our contributions and assess model explanations both quantitatively by systematically removing highly attended words and sentences, and qualitatively by examining explanation coherence. The paper concludes with findings, practical implications, limitations, and future research directions. Our code is available at https://github.com/Chen-XueWen/TinyXRA.
Risk management often plays an important role in decision making under uncertainty. In quantitative risk management, assessing and optimizing risk metrics requires efficient computing techniques and reliable theoretical guarantees. In this paper, we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics. We consider several risk metrics and study decision models that involve the metrics, with a main focus on the related computing techniques and theoretical properties. We show that stochastic optimization, as a powerful tool, can be leveraged to effectively address these problems.
Tety Wahyuningsih Manurung, Siti Unvaresi Misonia Beladona, Muh. Supwatul Hakim
et al.
Abundant coal reserves make this material a substitute fuel choice, especially for industry. The use of coal carries a high risk due to incomplete combustion and produces fly ash products. Fly ash cause pollution and health risks as well as environmental contamination when they are released, deposited, or leached into the ecosystem over short or long periods of time. The high content of silica and alumina in fly ash can be utilized and modified into new materials with added value. This research aims to modify the surface of fly ash using stearic acid as a hydrophobic inorganic material. Fly ash from Asam-asam Coal Power Plant was characterized by using XRD and modified by immersing in stearic acid (2,4,6, and 8%) and 98% ethanol. The result showed that the contact angle increases when fly ash is modified on the surface using stearic acid. The contact angle increases with increasing stearic acid concentration. The highest contact angle was obtained at a stearic acid concentration of 8%, and the lowest at 2% was about 112.9 and 102.2, respectively. The fly ash composition was primarily silica and alumina, which were crystalline, as confirmed by XRD. These findings provide several aspects of fly ash and its potential as a candidate material for environmental remediation and waste management.
Nelly L. Ramírez-Serrato, S. A. García-Cruzado, G. S. Herrera
et al.
Sinkholes pose a significant hazard in Mexico City (CDMX), causing substantial economic damage. While the link between sinkhole formation and groundwater extraction has been studied, specific mechanisms vary by site. Our overall aim is to characterize the phenomenon of sinkholes in CDMX. To achieve this, we create a database with 13 influencing factors, including population density, well density, distance to faults, fractures, roads, streams, elevation, slope, clay thickness, lithology, subsidence rate, geotechnical zones, and soil texture. Sinkhole locations were obtained from CDMX’s Risk Atlas (2017–2019). We shaped a susceptibility map based on statistical regression methods derived from applying linear regression models. For the susceptibility map, results showed that 40% of variables are significantly correlated with sinkhole density. Despite the regression model explained 24% of sinkhole density variability, it helped choosing variables for the susceptibility map that correlate better (89.7%). Hence, we identified that the northeast CDMX was the most susceptible zone. Therefore, the compound assessment of environmental factors is useful for the evaluation of susceptibility maps to identify prone factors for the generation of sinkholes. This framework provides relevant information for better use of the territory throughout the development of public policies.
This study aims to evaluate the effectiveness of various individual machine learning and their ensemble techniques such as Stacking, Voting and Meta-learning in landslide susceptibility assessment taking Poyang, Jiangxi, China as an example. Multi-source geo-environmental data including field surveys, Sentinel-2A/B satellite images, Digital Elevation Models (DEM), and geological and hydrological data were utilized to construct and validate landslide susceptibility models. Results show that the Stacking Classifier outperformed other models, achieving the highest F1 Score of 0.846 and AUC (Area Under ROC Curve) of 0.923, demonstrating its strong predictivity, followed by the Voting Classifier with the F1 Score of 0.829 and AUC of 0.922. Among the individual models, the Multi-Layer Perceptron (MLP) performed best with the F1 Score of 0.828 and AUC of 0.904. Furthermore, the explainable Artificial Intelligence (XAI) technique was applied to better understand the mechanism of classifiers in predicting landslide susceptibility and it suggests a significant correlation between land use, distance to fault, and landslide occurrences. In conclusion, Stacking and Voting hybrid learning models show clear advantages over the individual ones for landslide risk zoning. The results of study may provide technical support for disaster mitigation efforts and future urban planning in areas prone to landslides in Poyang.
This study explores integrating Building Information Modeling (BIM) technology into risk management practices for construction projects, aiming to enhance project performance through improved risk identification, assessment, and mitigation. The research employs the Analytical Hierarchy Process (AHP) to prioritize BIM-based strategies across multiple risk management dimensions, including technical, financial, sustainability, and time management. The findings demonstrate that BIM-based financial strategies rank highest among BIM-driven risk management, followed by sustainability and time. In contrast, technical, operation, and maintenance capabilities have the lowest rank. Given the high priority of BIM financial strategies, they have been applied to conduct sensitivity analysis; the sensitivity analysis results demonstrate the dynamic nature of a BIM sub-criteria strategy in response to changes in the weight of financial considerations. As financial concerns diminish, the shift towards sustainability, health, safety, and time efficiency underscores the importance of a more balanced approach in BIM strategy prioritization. BIM-based risk management improves project outcomes by enabling real-time data-driven decision-making, enhancing stakeholder collaboration and optimizing resource use, cost control, and sustainability. This research contributes to theoretical and practical advancements in construction risk management, suggesting that BIM can be a transformative tool for optimizing project performance while addressing the complexities and uncertainties inherent in the construction industry.
This study investigates the impact of Hofstede's cultural dimensions on abnormal core earnings management in multiple national cultural contexts. We employ an Ordinary Least Squares (OLS) regression model with abnormal core earnings as the dependent variable. The independent variables analyzed include Hofstede's dimensions: Power Distance Index (PDI), Individualism (IDV), Masculinity (MAS), and Uncertainty Avoidance Index (UAI). Our findings reveal that individualism is positively associated with abnormal core earnings, suggesting that cultures characterized by high individualism may encourage practices that inflate earnings due to the prominence of personal achievement and rewards. In contrast, masculinity negatively correlates with abnormal core earnings, indicating that the risk-taking attributes associated with masculine cultures may deter earnings management. Interestingly, uncertainty avoidance is positively linked to abnormal core earnings, supporting the notion that managers tend to engage more in earnings management to minimize fluctuations in financial reports in cultures with high uncertainty avoidance. The relationship between power distance and abnormal core earnings is found to be non-significant, indicating no substantial effect in this context. These findings contribute to the literature on cultural influences in financial reporting, providing valuable insights for policymakers and multinational firms concerning the cultural contexts within which financial decisions and reporting occur.
This study introduces a quantitative scenario-building method for analyzing emergency scenarios based on ontological methods and the EOC (element-object-consequence) model. The ontological structure of disasters concisely describes the knowledge, concepts, attributes, and relationships of the disaster scenario. It reduces the granularity of the data from the document to the data level. Disaster ontologies comprise a set of basic knowledge of a given domain, which is reusable, relatively fixed, and applicable in different areas at different periods. The EOC model is based on the ontology of a disaster and adopts a multiclass structure for the development of a complete process scenario and the adaptation of a disaster scenario by combining objects, elements, environments, and consequences.
Subodh Chandra Pal, Rabin Chakrabortty, Abu Reza Md. Towfiqul Islam
et al.
AbstractOne of the most important aspects of the ‘sub-tropical’ monsoon-influenced environment is the issue of ‘soil erosion’ and its related ‘land degradation’. On the other hand, the climate in this area has become quite extreme. According to this viewpoint, it is important to research a future ‘soil erosion’ scenario in front of the probable effects of climate change and land use change. For the objective of assessing the extent of soil erosion in this area, this study took into account both the USLE and the RUSLE. Compared to the USLE that has been validated, RUSLE has a comparatively greater quantitative efficiency. In RUSLE, the ‘very high’ (>20) and ‘high’ (15–20) ‘soil erosion’ zones tend to be associated with the ‘north-western, western, south-western, and southern’ regions of the river basin. The ‘soil erosion’ that will occur in the future has been estimated by taking into account the projected rainfall, land use and land cover (LULC). ‘Soil erosion’ has increased from the previous time to the projected time. Predicted R factor values for SSP 585 range from 399.92 to 493.72. In addition, a growing erosion tendency associated with increased shared socio-economic pathways (SSPs) has been found.
In this study, civil gas energy accidents reported by the China Gas Network and related organizations from 2012 to 2021 were collected, and a comprehensive multidimensional correlation analysis was conducted considering factors such as accident timing, geography, causes, and casualties. The results identified July and August, Mondays and Sundays, and the morning, mid-day, and evening cooking times as the high-incidence months, days, and times for gas accidents, respectively. Gas accidents were found to occur more frequently in eastern coastal areas, provincial capitals, and larger cities, while residential and construction sites were identified as high-risk areas for gas accidents. Explosions were the most prevalent type of gas accident, followed by leaks, fires, and poisoning. Third-party construction and valve issues were identified as the primary factors contributing to gas leakage, whereas cooking was identified as the most common ignition source. An analysis of the Pearson correlation coefficient indicated a significant correlation among the gas accident factors. Moreover, a time-series prediction model was developed to forecast gas accidents in China, with the results demonstrating fluctuating gas accidents. This study proposes targeted preventive measures in terms of publicity, education, equipment, and facilities to provide scientific support to government units to improve civil gas energy security measures.
Quinn Grundy, Fiona Webster, Daniel Z Buchman
et al.
Objectives Pharmaceutical industry involvement in medical education, research and clinical practice can lead to conflicts of interest. Within this context, this study examined how the ‘Suboxone Education Programme’, developed and delivered by a pharmaceutical company as part of a federally regulated risk management program, was presented as a solution to various kinds of risks relating to opioid use in public documents from medical institutions across Canada.Setting These documents were issued during the Canadian opioid crisis, a time when the involvement of industry in health policy was being widely questioned given industry’s role in driving the overprescribing of opioid analgesics and contributing to population-level harms.Design A critical discourse analysis of 69 documents collected between July 2020 and May 2021 referencing the Suboxone Education Program spanning 13 years (2007–2021) from medical, nursing and pharmacy institutions sourced from every Canadian province and territory. Discursive themes were identified through iterative and duplicate analyses using a semistructured data extraction instrument.Results Documents characterised the Programme as addressing iatrogenic risks from overprescribing opioid analgesics, environmental risks from a toxic street drug supply and pharmacological risks relating to the dominant therapeutic alternative of methadone. The programme was identified as being able to address these risks by providing mechanisms to surveil healthcare professionals and to facilitate the prescribing of Suboxone. Medical institutions legitimised the Suboxone Education Programme by lending their regulatory, epidemiological and professional authority.Conclusions Addressing risk is considered as a central, moral responsibility of contemporary healthcare services. In this case, moral imperatives to address opioid crisis-related risks overrode other ethical concerns regarding conflicts of interest between industry and public welfare. Failing to address these conflicts potentially imperils efforts of mitigating population health harms by propagating an important driving force of the opioid crisis.
Exposure to high concentrations of fine particles (PM<sub>2.5</sub>) with toxic metals can have significant health effects, especially during the Chinese spring festival (CSF), due to the large amount of fireworks’ emissions. Few studies have focused on the potential health impact of PM<sub>2.5</sub> pollution in small cities in China during the 2020 CSF, which coincided with the COVID-19 outbreak that posed a huge challenge to the environment and obvious health issues to countries around the world. We examined the characteristics of PM<sub>2.5</sub>, including carbonaceous matter and elements, for three intervals during the 2020 CSF in Taizhou, identified the sources and evaluated the health risks, and compared them with those of 2018. The results showed that PM<sub>2.5</sub> increased by 13.20% during the 2020 CSF compared to those in the 2018 CSF, while carbonaceous matter (CM) and elements decreased by 39.41% and 53.84%, respectively. The synergistic effects of emissions, chemistry, and transport may lead to increased PM<sub>2.5</sub> pollution, while the lockdown measures contributed to the decrease in CM and elements during the 2020 CSF. Fe, Mn, and Cu were the most abundant elements in PM<sub>2.5</sub> in both years, and As and Cr(VI) should be of concern as their concentrations in both years exceeded the NAAQS guideline values. Industry, combustion, and mineral/road dust sources were identified by PCA in both years, with a 5.87% reduction in the contribution from industry in 2020 compared to 2018. The noncarcinogenic risk posed by As, Co, Mn, and Ti in 2018 and As and Mn in 2020 was significant. The carcinogenic risk posed by As, Cr(VI), and Pb exceeded the accepted precautionary limit (1 × 10<sup>−6</sup>) in both years. Mn was the dominant contributor to the total noncarcinogenic risks, while Cr(VI) showed the largest excessive cancer risks posed by metals in PM<sub>2.5</sub>, implying its associated source, industry, was the greatest risk to people in Taizhou after exposure to PM<sub>2.5</sub>. Despite the increase in PM<sub>2.5</sub> mass concentration, the health impacts were reduced by the lockdown policy implemented in Taizhou during the 2020 CSF compared to 2018. Our study highlights the urgent need to consider the mitigation of emissions in Taizhou and regional joint management efforts based on health protection objectives despite the rough source apportionment by PCA.
The global coronavirus (COVID-19) pandemic has created a whole new set of risks in construction industries generating unprecedented delays, disruptions, and uncertainty on construction projects, and has forced the industries in adopting more sophisticated technologies while combating the reduced workforce on job sites. Further, the post-pandemic era of construction is expected to be a lot different as the industries will embrace the technology as the augmentation and collaboration strategy. Thus, it will be extremely hard to sustain for construction industries in the absence of effective risk management. The existing risk plans need to be inspected for their capability of handling new risks arising from COVID-19 and the project managers will need to make the necessary revisions as needed. This paper discusses on past (NORM), present (NEW NORM), and future (Post COVID-19 NORM) of the construction industry and highlights key strategies for managing projects and construction risks during and post COVID-19 pandemic.
Emanuele Casamassima, Lech A. Grzelak, Frank A. Mulder
et al.
Understanding mortgage prepayment is crucial for any financial institution providing mortgages, and it is important for hedging the risk resulting from such unexpected cash flows. Here, in the setting of a Dutch mortgage provider, we propose to include non-linear financial instruments in the hedge portfolio when dealing with mortgages with the option to prepay part of the notional early. Based on the assumption that there is a correlation between prepayment and the interest rates in the market, a model is proposed which is based on a specific refinancing incentive. The linear and non-linear risks are addressed by a set of tradeable instruments in a static hedge strategy. We will show that a stochastic model for the notional of a mortgage unveils non-linear risk embedded in a prepayment option. Based on a calibration of the refinancing incentive on a data set of more than thirty million observations, a functional form of the prepayments is defined, which accurately reflects the borrowers' behaviour. We compare this functional form with a fully rational model, where the option to prepay is assumed to be exercised rationally.