Search Results (55)

Global climate change is causing an increase in extreme rainfall events, which impacts the risk of hydrometeorological disasters. To support disaster mitigation and early warning systems, accurate and reliable rainfall predictions are required. Although ensemble forecasting is widely used to model atmospheric uncertainty, raw ensemble results often exhibit insufficient bias and dispersion. Therefore, post-processing techniques are needed to improve the quality of probabilistic predictions. The most commonly used calibration method is Bayesian Model Averaging (BMA). This study conducted a scoping review of peer-reviewed papers on ensemble forecast calibration using BMA, based on the PRISMA-ScR framework. Furthermore, this study presents a comprehensive bibliometric analysis involving co-authorship networks of productive authors and bibliometric maps with clustered terms. A total of 35 relevant articles were identified from 49 screened publications. The bibliometric analysis revealed that “ensemble forecasting” and “Gaussian distribution” are the most dominant terms in the research network, indicating that Gaussian-based approaches remain more widely used in ensemble forecast calibration studies. In contrast, studies explicitly applying Gamma-based approaches are still relatively limited despite their relevance for modeling asymmetric rainfall data. The results obtained in this study highlight the importance of developing and integrating more appropriate probability distributions, such as those within the Extreme Value Theory framework, into BMA models. These findings suggest that the selection of appropriate probabilistic distributions in BMA-based calibration frameworks plays an important role in improving forecast reliability and the representation of uncertainty in rainfall prediction. Furthermore, the development of more suitable probability distributions, including Extreme Value Theory (EVT)-based distributions, has strong potential to enhance probabilistic calibration performance for asymmetric rainfall data. This approach is expected to improve the accuracy and reliability of extreme rainfall predictions. The findings of this study provide an important contribution to the development of early warning systems for hydrometeorological disasters and support the achievement of Sustainable Development Goals (SDGs). Full article

Reliable prediction of post-initiation run-out distance of rainfall-induced landslides is essential for hazard assessment, evacuation planning, and disaster-risk mitigation. However, most existing data-driven approaches formulate run-out prediction as a deterministic regression problem and therefore provide limited information on predictive uncertainty, rare long-runout events, and explainable decision support. To address these limitations, this study proposes CQR-EVT-XAI, a trustworthy AI framework that integrates Quantile LightGBM, Conformalized Quantile Regression (CQR), Extreme Value Theory (EVT), and Explainable Artificial Intelligence (XAI) for uncertainty-aware and explainable landslide run-out risk prediction. Based on 10,158 rainfall-induced landslide samples, physics-informed features are constructed from elevation difference H, source area A, source volume V, and mean slope angle $\theta$. The proposed framework generates calibrated prediction intervals, threshold-based exceedance probabilities, upper-tail risk indicators, and interpretable risk levels. The CQR-LightGBM median model achieves high point-prediction accuracy, with $R^2$ = 0.939, RMSE = 18.03 m, and MAE = 6.55 m. Conformal calibration improves the empirical coverage of the nominal 90% and 95% prediction intervals from 0.813 to 0.903 and from 0.876 to 0.953, respectively. Tail-risk analysis shows that the upper prediction bound ${\widehat{L}}_{95}$ effectively identifies extreme long-runout events, achieving recall values of 0.974 and 0.900 for L > 300 m and L > 500 m, respectively. SHAP analysis reveals that elevation difference H, source volume V, and energy-related derived features dominate both median run-out prediction and upper-tail risk behavior, while slope-related variables mainly influence predictive uncertainty and exceedance-risk levels. These results demonstrate that the proposed CQR-EVT-XAI framework provides a practical workflow for calibrated uncertainty quantification, tail-risk identification, and explainable decision support in rainfall-induced landslide run-out risk assessment. Full article

This study uses and compares stationary and non-stationary Generalised Extreme Value Distribution (GEVD) to model the behaviour of nitrogen dioxide (NO₂) emission maxima from each of 13 Eskom’s coal-fuelled power stations. The pollutant is modelled to facilitate monitoring and regulation in order to protect public health and the environment. The Maximum Likelihood Estimate (MLE) and Generalised Maximum Likelihood Estimate (GMLE) parameter estimation methods are used and compared in finding the best-fitting model per power station. The results show that a non-stationary model with time-dependent location and/or scale parameter(s) produced the best fit for ten of the power stations, while a stationary model gave the best fit for three, as confirmed by the diagnostic tools. Future extremely high NO₂ emissions were estimated by making use of the 40 and 100 quarter return levels based on the best-fitting models. This study shows how stationarity may not hold for all NO₂ emission data from Eskom’s coal-fired power stations. Modelling data using time-dependent non-stationary GEVD models can be useful, especially in identifying and predicting trends or patterns in worsening high NO₂ emissions with time. This modelling approach is important in providing information for planning and policy formulation of extreme emissions from coal-fired electricity-generating power stations at Eskom (South Africa). Full article

The assumption that highly correlated financial assets share identical risk profiles often overlooks crucial distributional asymmetries. This study introduces a Goodness-of-Fit (GoF) framework to evaluate stochastic symmetry and structural alignment of equity returns. Moving beyond linear correlation, we apply non-parametric GoF tests—Kolmogorov–Smirnov, permutation-based Anderson–Darling, and Epps–Singleton—complemented by Energy Distance metrics, Extreme Value Theory (EVT) for 1% and 5% tail asymptotics, and robust L-moments to quantify tail asymmetry. We analyze major stocks against market indices and sectoral ETFs using ARMA-GARCH filtered innovations to isolate IID components. Our findings reveal a significant decoupling between correlation and stochastic symmetry; highly correlated assets frequently exhibit tail asymmetry and structural drift. Energy Distance decomposition isolates shape-driven deviations from scale-driven volatility. Furthermore, hierarchical clustering categorizes assets into distinct risk profiles, bridging structural divergence and left-tail risk. A 1000-iteration bootstrapped backtest shows that integrating our GoF framework with tail-risk penalties improves risk-adjusted performance, evidenced by superior Sharpe ratios (outperforming 80.3% of random allocations). In conclusion, high linear correlation does not guarantee distributional symmetry. The proposed framework offers deeper insights into asymmetric asset behavior than conventional second moment metrics, providing a robust tool for portfolio risk management under non-Gaussian market conditions. Full article

The escalating frequency and intensity of extreme rainfall events driven by climate change threaten infrastructure resilience and societal safety, underscoring the urgent need for robust models to predict these events. Previous studies on the integration of Extreme Value Theory (EVT) and machine learning in modelling extreme rainfall events have not explored the use of a time-varying threshold. This study introduces a novel time-varying threshold Generalised Pareto (GP) regression tree for modelling extreme rainfall in Durban, South Africa. The proposed hybrid model combines EVT with covariate-driven regression tree partitioning, allowing the threshold to evolve dynamically with meteorological conditions. Using daily rainfall and meteorological covariate data from 1981 to 2025, the model was developed, pruned, and benchmarked against a static-threshold GP regression tree and a time-varying threshold Generalised Pareto Distribution (GPD). Evaluation based on the Bayesian Information Criterion (BIC) and log-likelihood demonstrated the superior performance of the proposed model in capturing covariate-driven heterogeneity and temporal variability of rainfall extremes. Four distinct climatic regimes with different tail behaviours and return levels were identified. This study provides the first meteorological application of a time-varying threshold GP regression tree and offers practical insights into flood risk assessment and climate resilience planning in the city of Durban. Full article

Ship–bridge collisions in cross-sea bridge waterways are rare but potentially catastrophic events, making conventional accident-based risk assessment difficult to implement effectively. Existing AIS-based indicators capture vessel behaviors but insufficiently quantify bridge-passage safety margins, especially dynamic aspects such as crossing posture and readiness prior to bridge transit. To address this limitation, this study proposes a data-driven framework for detecting unsafe ship–bridge passages using two bridge-passage-oriented surrogate safety measures (SSMs) and extreme value theory (EVT). The Bridge-passage Lateral Clearance Margin (BLCM) quantifies the effective lateral safety margin retained during the realized bridge-crossing stage, while the Bridge-passage Readiness Lead Time (BRLT) measures how early a vessel becomes stably prepared for bridge passage before crossing. The Peaks Over Threshold (POT) model is first used to characterize the marginal extremes of the two indicators, and a bivariate threshold exceedance model (BTE) is then established to examine their joint risk behavior. Case studies of the Jintang Bridge and Zhoudai Bridge waterways demonstrate that the proposed framework can effectively screen and identify trajectories with unsafe or margin-deficient bridge-passage characteristics. The results show that unsafe passages are typically associated with both reduced lateral clearance and insufficient preparation time, and that joint modeling of the two indicators improves risk identification performance. The findings suggest that ship–bridge risk is better interpreted from the perspective of passage quality deficiency rather than simple geometric proximity. The proposed framework provides an interpretable tool for retrospective unsafe passage screening, traffic monitoring support, and post-event safety analysis in complex bridge waterways. Full article

Real-time crash risk assessment is a key component of proactive road safety management, enabling the identification of hazardous conditions within short temporal intervals before crashes occur. Traditional crash-based models are unsuitable for such applications due to the rarity, reporting delay, and stochastic nature of crash data. Traffic conflicts, capturing near-miss interactions between road users, provide a practical alternative for real-time safety analysis. Over the past decade, numerous modelling approaches have been developed to translate conflict information into crash risk estimates; however, the literature remains fragmented and lacks a unified analytical synthesis. This review presents a state-of-the-art, model-centric analysis of conflict-based approaches, classifying them into five paradigms: statistical/regression-based, Bayesian, extreme value theory (EVT), machine learning (ML), and hybrid models. Beyond classification, the study conducts a structured cross-paradigm comparison across key dimensions, including conflict representation, data characteristics, temporal modelling, uncertainty treatment, validation strategies, computational complexity, and operational readiness. The paradigms are further interpreted through the complementary lenses of conflict frequency and severity. The review identifies key research gaps, including fragmented conflict definitions, challenges in modelling rare and extreme events, incomplete treatment of uncertainty and spatiotemporal dynamics, and limitations in validation, transferability, and deployment. Emerging research directions include standardized and adaptive conflict indicators, EVT–machine learning integration, integrated uncertainty-aware frameworks, advanced spatiotemporal modelling, transferable models, and scalable real-time implementation. By combining structured evidence mapping and cross-paradigm synthesis, this study supports model selection, development, and deployment for dynamic crash risk assessment. Full article

Accurate extreme rainfall representation is critical for resilient hydrological design and sustainable water management in tropical regions. This study evaluates the GPM IMERG product across three diverse watersheds in East Java (Welang, Kedak, and Grindulu) using Extreme Value Theory (EVT). By employing Generalized Extreme Value (GEV) and Peaks Over Threshold (POT) methods, the research assesses the reliability of satellite estimates in characterizing the extreme events that safeguard community security and infrastructure longevity. Results indicate that while GPM IMERG excels at monthly scales, it lacks the daily precision required for effective flash flood mitigation, particularly in small basins. Crucially, GEV analysis reveals a structural mismatch: ground observations exhibit heavy-tailed (Fréchet) distributions, while GPM IMERG follows bounded (Weibull) distributions. Consequently, the satellite product underestimates high-magnitude events at long return periods, the exact events that define the design limits of adaptive hydraulic structures. Complementary POT analysis identifies scale-dependent biases across catchments. These findings suggest that while GPM IMERG is robust for regional monitoring, it requires distribution-specific bias correction to support disaster-resilient engineering. Addressing these gaps is essential for achieving climate-responsive sustainable development in data-scarce regions. Full article

The life insurance sector plays a critical role in financial system stability but is inherently exposed to extreme market fluctuations due to long-term liabilities and asset–liability mismatches. This study investigates extreme losses in the growth rates of the JSE Life Insurance Price Index (LIPI) using the Generalised Extreme Value Distribution (GEVD) and the Generalised Pareto Distribution (GPD) under the Extreme Value Theory (EVT) framework. Monthly data from January 2000 to October 2023 were transformed into a loss series, and extreme events were captured using quarterly block maxima and a POT threshold at the 95th percentile. Model parameters were estimated through Maximum Likelihood Estimation, and downside risk was assessed using return levels, Value-at-Risk (VaR), and Tail Value-at-Risk (tVaR). The GEVD model produced a negative shape parameter, consistent with a bounded Weibull-type tail, while the GPD indicated a heavy-tailed distribution. Return level estimates show escalating loss magnitudes and widening uncertainty over longer horizons, reflecting the challenges of projecting rare events. Kupiec backtesting confirms the adequacy and reliability of the GEVD-based VaR across all confidence levels, whereas the GPD underestimates risk at lower thresholds. These findings indicate significant tail risk within the South African life insurance equity segment and underscore the importance of EVT-based risk measures for capital planning and regulatory oversight. The study contributes to financial risk modelling in the life insurance sector and offers practical insights for strengthening solvency assessment and enterprise risk management frameworks. Full article

This paper examines how assets from emerging and developed stock markets can be efficiently allocated during periods of financial crisis by integrating traditional portfolio theory with Extreme Value Theory (EVT), using the Generalized Pareto Distribution (GPD) and Generalized Extreme Value (GEV) approaches to model tail risks. This study evaluates mean-variance portfolios constructed under each EVT framework and finds that portfolios based on GPD estimates consistently favour emerging market assets, which outperform both developed market and internationally diversified portfolios during extreme market conditions. In contrast, GEV-based portfolios indicate superior performance for developed market assets, highlighting the distinct behaviour of returns in the upper and lower tails of the distribution. These contrasting results reveal the unique nature of safe-haven characteristics associated with developed economies, the assets of which demonstrate greater stability and resilience during episodes of financial stress. By showing how tail-risk modelling alters optimal portfolio weights across market types, this paper contributes new evidence to the literature on crisis-informed asset allocation and offers practical insights for investors seeking robust diversification strategies under extreme market fluctuations. Full article

Climate change has resulted in frequent extreme weather events such as heavy rainfall and heat waves in Japan, making accurate forecasting and countermeasures an urgent issue. Therefore, it is urgently required to analyze the statistical characteristics of extreme rainfall events using the extreme value theory (EVT). The generalized extreme value (GEV) distribution, a core model for EVT, was applied in this study to rainfall data collected in Kakunodate, Akita Prefecture, Japan, spanning May 1976 to December 2023. The analysis results confirm the presence of extreme rainfall events. Through model fitting, the GEV parameters representing location, scale, and shape were accurately estimated. The model demonstrated a good fit, particularly for moderate-intensity rainfall. However, notable uncertainties emerged in the prediction of the most extreme events. Return period analysis results indicated that extreme rainfall events occur at intervals ranging from 2 to 100 years, suggesting the necessity of incorporating safety margins into long-term forecasting frameworks. Considering the increasing frequency of such events, cross-validation with alternative statistical methods and the potential adoption of non-smooth GEV models are recommended to enhance predictive reliability. Overall, the results of this study highlight the need for adaptive and flexible revisions to infrastructure design criteria in response to evolving patterns of extreme weather. Full article

Rapid urban air mobility (UAM) developments and new classes of vertical takeoff and landing (eVTOL) aircraft have changed the safety paradigm in urban airspace. eVTOL aircraft operations in dense urban environments are characterized by increased variability of external factors, highly dynamic flight scenarios, and an increased likelihood of rare but potentially critical events. Traditional safety assessment approaches do not capture the specific features of eVTOL designs, power plants, autonomy algorithms, and urban air traffic characteristics; this results in low threat prediction accuracy and limited development of modern incident prevention systems. Herein, the risk profile of eVTOL aircraft is analyzed, accounting for the multifactorial nature of urban environments and the complexity of integrating such vehicles into existing UAM infrastructure. The need for quantitative methods for assessing the probability of critical situation risks is also substantiated. These methods provide a statistically accurate description of extreme events and enable the identification of hidden dependencies in complex technical and organizational systems. Approaches based on probabilistic models, extreme value analysis, and systemic processing of operational data are considered, providing increased risk assessment accuracy and a deeper understanding of mechanisms underlying hazardous events. Results demonstrate the importance of applying the extreme value theory (EVT)–Copula model, which enables the quantitative assessment of the probability of extreme situations and loss of stability of eVTOL vehicles in the context of developing UAM. This model can be employed to obtain realistic predictions of flight processes, reduce uncertainty, and create scientifically valid tools for developing effective measures to minimize the risks of extreme events—a key factor in ensuring the safety of eVTOL flights in urban airspace. Full article

Unstable car-following behavior under truck-induced visual occlusion on mountainous two-lane roads significantly increases rear-end crash risk. However, compared with studies focusing on overtaking or curve risk prediction, the car-following-truck (CFT) risk and its influencing factors have received limited attention. Therefore, this study used unmanned aerial vehicles (UAVs) to collect high-resolution trajectory data of CFT scenarios on both straight and curved segments under truck-induced occlusion. First, the CFT risk was quantified based on an anticipated collision time (ACT) indicator, a two-dimensional surrogate safety measure that accounts for vehicle acceleration variations. Then, extreme value theory (EVT) was applied to calibrate alignment-specific risk thresholds. Finally, an XGBoost-based risk identification model was developed using vehicle dynamics-related features, and feature importance analysis combined with partial dependence interpretability was conducted to obtain key influencing factors. The results show that the calibrated ACT thresholds are approximately 3.838 s for straight segments and 4.385 s for curved segments, providing a reliable basis for risk classification. In addition, the XGBoost-based risk identification achieved accuracies of 90.63% and 95.87% for straight and curved segments, respectively. Further analysis indicates that CFT distance was the contributing factor. Moreover, risk increases markedly within a 10–20 m range on straight segments, while it rises rapidly once spacing falls below about 10 m on curved segments. Speed and acceleration differences exhibited stronger amplifying effects under short-spacing conditions. These findings provide a micro-behavioral basis for safety management and intelligent driving applications on mountainous roads with high truck mixing rates, supporting safer and more sustainable traffic operations. Full article

Institutional investors exert significant influence on the operations and development of financial institutions, with different categories of investors playing distinct roles. We contend that institutional investor diversity may affect systemic financial risk. This study proposes novel measures of institutional investor diversity across 84 China’s financial institutions and employs Extreme Value Theory (EVT) to estimate systemic financial risk. Based on this, we empirically examine the relationship and underlying mechanisms. Baseline regression indicates that greater institutional investor diversity plays an effective role in controlling systemic financial risk. We further find that institutional investor diversity significantly suppresses herding behavior, thereby indirectly reducing systemic risk. Moreover, this effect is more pronounced in financial institutions operating in more developed market environments, under stronger external supervision, and with higher levels of technological advancement, as well as in securities firms. These findings not only contribute to the literature on the economic impact of institutional investors but also provide valuable insights for strengthening systemic financial risk control. Full article

The increasing occurrence of extreme rainfall events often leads to flash floods, infrastructure damage, loss of human life, and significant economic impacts. There is a pressing need for data-driven assessments and the application of robust analytical approaches to better understand these changes. Analyzing ground-level daily rainfall data from 1985 to 2023 from 26 monitoring stations, this study first employs the Mann–Kendall test using robust statistics including minimum, median, various quartiles, and maximum rainfall values for detecting long-term trends across Saudi Arabia. Next, the k-means clustering technique is applied to characterize the annual rainfall cycles across different regions of the country. Finally, the Peaks Over Threshold (POT) approach within Extreme Value Theory (EVT) is employed to identify site-specific thresholds for extreme rainfall using the Generalized Pareto Distribution (GPD). This automated, data-driven method offers a more objective alternative to the commonly used ad hoc percentile-based threshold selection, thereby enhancing the rigour and reproducibility of extreme rainfall analysis. Local specific thresholds were computed ranging from about 16 to 47 mm from Arar and Jazan, respectively. These thresholds were then used to calculate the frequency and intensity of extreme rainfall events. The fitted GPD parameters were further used to estimate return levels (RLs) for different return periods (2-, 5-, 10-, 20-, 50-, and 100-year) into the future. The results underscore considerable spatial variability in extreme rainfall behaviour across Saudi Arabia, with a higher likelihood of intense and infrequent precipitation events in the coming decades. Full article