Search Results (128)

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

Predicting tropical cyclone (TC) precipitation is an important step in disaster prevention and mitigation. However, in the probability prediction of TC precipitation, traditional deep learning models are highly sensitive to initial conditions and can only provide deterministic forecasts, making it difficult to quantify uncertainty. In this study, we develop an AI-driven deep learning model based on diffusion models, incorporating historical data to reduce sensitivity to initial conditions and enhance precipitation distribution accuracy. Compared with traditional deep learning methods, this model outperforms other models in terms of the SSIM and PSNR for deterministic prediction of TC precipitation in 0–12 h. For probabilistic prediction, this model also achieves lower CRPS and Brier scores. Therefore, diffusion-based deep learning models not only show broad application prospects in TC-precipitation forecasting but also hold promise for providing probabilistic prediction methods for various disasters, enabling the widespread adoption of probabilistic forecasting across different prediction domains. Full article

Reliable short-term wind power forecasting is crucial for smart grid stability. However, high-dimensional noise and stochastic fluctuations in wind sequences often degrade the accuracy of traditional forecasting models. Moreover, wind power time series typically exhibit asymmetric rising and decaying patterns, which further complicate accurate modeling. To address these challenges, this study proposes a hybrid intelligent system that integrates three components: data preprocessing, heterogeneous ensemble learning, and probabilistic interval forecasting. First, we build a multi-stage preprocessing workflow. Adaptive DBSCAN and Local Outlier Factor (LOF) remove spatial and density anomalies. Then multivariate variational mode decomposition (MVMD) synchronously separates multi-scale oscillatory patterns while preserving cross-channel correlations and frequency-domain symmetry across input variables. SHAP analysis quantifies feature importance, ensuring interpretability. The selected features are fed into a heterogeneous ensemble model consisting of Transformer, BPNN, ELM, XGBoost, and QRLSTM, which collectively capture multi-scale temporal dependencies and diverse data patterns. The ensemble weights are dynamically optimized by a modified multi-objective dragonfly algorithm (MMODA) that balances forecast accuracy and stability. Based on this ensemble, we apply MMODA to tune kernel density estimation for generating high-quality forecast intervals, maximizing coverage while minimizing interval width. Experiments on two wind farms in Shandong show that our MMODA-optimized ensemble reduces mean absolute percentage error by about 44.7% compared to single models, and ablations confirm that MVMD preprocessing adds a further 10.7% reduction. The proposed system provides an interpretable and reliable decision-support tool for sustainable grid operations. Full article

Post-processing of numerical weather prediction (NWP) models constitutes a pivotal link in enhancing forecast performance. Despite their recognition as cutting-edge point-based post-processing techniques, systematic comparative evaluations of ecPoint (ECWMF for point forecasts) and Ensemble Model Output Statistics (EMOS)—particularly assessments of their applicability outside Europe and to Chinese ensemble forecasting systems—remain sparse. In this study, we evaluate two advanced post-processing techniques—EMOS and the ecPoint—for calibrating ensemble precipitation forecasts. A comprehensive assessment of the performance of these ensemble post-processing methods is conducted using the CMA-GEPS (China Meteorological Administration’s Global Ensemble Forecasting System forecast over eastern China. The results demonstrate that both methods significantly mitigate systematic biases and improve the reliability and dispersion of ensemble forecasts. Notably, improvement in forecast accuracy is observed even under convective weather conditions and early-warning capability of extreme precipitation events is improved. Overall, while both methods show comparable performance, they exhibit distinct behaviours across different regions. The ecPoint method slightly outperforms EMOS in terms of Continuous Ranked Probability Score (CRPS) and provides improved resolution and early-warning capabilities at various precipitation thresholds. Full article

The extreme volatility of European energy markets in 2022 has exposed the limitations of conventional forecasting models, necessitating more robust architectures capable of handling non-linear price shocks. This study proposes a novel multi-parallel hybrid forecasting framework that integrates seven heterogeneous neural networks to predict day-ahead electricity prices. The architecture employs a hierarchical approach where six parallel base models (NN1–NN6) feed into a meta-network (NN7) to generate baseline forecasts. To further enhance predictive fidelity, these results undergo a calibration stage using probabilistic error distribution analysis to produce final probability-adjusted forecasts. The model was validated using the Lithuanian electricity market during the highly volatile period of 2020–2022. Empirical results demonstrate a clear “stacking effect,” where the incremental integration of base networks consistently reduces forecasting residuals. The final probability-adjusted configuration achieved a notable nMAE of 1.57% and a sMAPE of 34.25%, significantly outperforming baseline ensemble outputs and state-of-the-art benchmarks reported in recent literature. Specifically, the probability-based refinement proved highly effective in mitigating systematic biases during nighttime and early morning hours, confirming the model’s capacity to maintain accuracy under extreme market stress. Full article

Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with a focus on how input perturbation design affects forecast skill and uncertainty representation. We adapt a GNN architecture to the Canary Islands region in the North Atlantic and implement a homogeneous ensemble approach inspired by bagging, where diversity is introduced during inference by perturbing initial ocean states rather than retraining multiple models. Several noise-based ensemble generation strategies are evaluated, including Gaussian noise, Perlin noise, and fractal Perlin noise, with systematic variation of noise intensity and spatial structure. Ensemble forecasts are assessed over a 15-day horizon using deterministic metrics (RMSE and bias) and probabilistic metrics, including the Continuous Ranked Probability Score (CRPS) and the Spread–skill ratio. The results show that, while deterministic skill remains comparable to the single-model forecast, the type and structure of input perturbations influence uncertainty representation, particularly at longer lead times. Ensembles generated with spatially coherent perturbations, such as low-resolution Perlin noise, achieve improved calibration and lower CRPS compared to purely random Gaussian perturbations. These findings highlight the role of noise structure and scale in ensemble GNN design, indicating that specifically structured input perturbations can improve ensemble diversity and calibration without additional training cost. These results provide a methodological contribution toward the study of ensemble-based GNN approaches for regional ocean forecasting. Full article

Accurate short-term lightning forecasting requires reliable representations of both lightning occurrence and intensity, as well as the underlying convective processes. While ensemble prediction systems (EPSs) provide valuable probabilistic information, their ability to resolve mesoscale and convective-scale variability remains limited. In this study, we assess the added value of mesoscale information for probabilistic lightning forecasting over eastern China. A mesoscale ensemble is constructed from deterministic forecasts of the China Meteorological Administration (CMA) Mesoscale Model (MESO) using spatiotemporal neighborhood and time-lagged techniques and is combined with predictors from the CMA Regional Ensemble Prediction System (REPS). Lightning occurrence and counts are modeled within a Bayesian additive model for location, scale, and shape (BAMLSS) framework, using a hurdle-based count regression to account for excess zeros and overdispersion. Influential nonlinear predictors are selected via stability selection combined with gradient boosting. Forecast performance with and without MESO-derived predictors is systematically evaluated. The results indicate that incorporating mesoscale information generally improves forecast skill for both lightning occurrence and intensity across multiple verification metrics. These improvements are associated with MESO-derived predictors related to convective available potential energy and convective precipitation, suggesting the importance of mesoscale processes for probabilistic lightning forecasting. Full article

Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article

Prescribed fire is a vital tool for ecosystem management and wildfire risk reduction but its escalation is constrained by overly conservative burn windows because of uncertainties, for instance, in wind forecasts. This review describes the state of the art in weather product use by fire/smoke models and identifies three priority research gaps that artificial intelligence/machine learning (AI/ML) is well positioned to address: (1) spatial and temporal downscaling to meter-scale, sub-hourly wind fields; (2) bias correction for systematic model errors in complex terrain; and (3) robust uncertainty quantification to inform ensemble-based simulations. Emerging AI/ML techniques offer promising frameworks to address all three challenges. By providing high-resolution, bias-corrected, and probabilistic wind fields, AI/ML-enhanced forecasts will allow for expanded burn windows, improved ignition strategy design and a reduced reliance on expert intuition, especially when a prescribed fire is introduced into new areas. Full article

The core component to ensure the refined and safe operation of distribution network scheduling is 10 kV bus load probabilistic prediction. However, existing probabilistic prediction methods suffer from insufficient dynamic feature extraction and compromised prediction reliability caused by quantile crossing. To address these issues, this paper proposes a 10 kV bus load probabilistic prediction method integrating multi-value quantile regression (MQR) and a temporal fusion ensemble learning model (ELM). Firstly, a temporal fusion ensemble learning model is constructed, which integrates multiple temporal fusion network (TFN) sub-models through a stacking framework to parallel extract multi-dimensional temporal features of loads, effectively enhancing its feature capture capability for complex load data. Secondly, MQR is introduced as the core objective function to synchronously generate multi-quantile load forecasting results, comprehensively depicting the load probability distribution. Finally, a Listwise Maximum Likelihood Estimation (ListMLE) ranking constraint mechanism is embedded, which optimizes quantile ordering through monotonicity constraints, significantly reducing the degree of quantile crossing and improving the interpretability of forecasting results. The results show that the MQR-ELM algorithm achieves a Prediction Interval Coverage Probability of 94.624% (close to the nominal coverage rate of 95%), a Prediction Interval Averaged Width of 588.526, a Crossing Degree Index of only 0.0476, and a Continuous Ranked Probability Score as low as 84.931. All core indicators are significantly superior to those of the comparative algorithms. Full article

Modeling nonstationary time series in financial and energy markets remains challenging due to nonlinear dynamics, volatility clustering, and frequent regime shifts that distort the underlying probabilistic structure of the data. This study introduces a novel probabilistic–statistical decomposition framework, termed Robust Adaptive Decomposition (RAD), designed to preserve probabilistic symmetry between deterministic and stochastic components. In this context, symmetry refers to maintaining statistical balance—particularly in the means, variances, and distributional structures—between the extracted modes and the residual series, thereby preventing artificial bias or variance distortion during decomposition. The RAD framework adaptively determines the optimal number of modes needed to effectively separate short-term fluctuations from long-term structural movements. Unlike conventional techniques, such as Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and CEEMDAN, the proposed method incorporates a robustness mechanism that mitigates mode mixing and reduces distortions induced by extreme shocks and regime transitions. The empirical evaluation is conducted on six oil-related energy commodities—Brent crude oil, kerosene, propane, sulfur diesel, heating oil, and gasoline—whose price dynamics exhibit pronounced nonlinearity and structural volatility. When integrated with ARIMA forecasting models, the RAD-based framework consistently outperforms benchmark decomposition approaches. Across all datasets, RAD–ARIMA achieves reductions of approximately 65–90% in MAE, 60–85% in RMSE, and up to 95% in MAPE relative to CEEMDAN-based models. These results demonstrate that RAD provides a mathematically rigorous and computationally efficient preprocessing mechanism that preserves statistical equilibrium while effectively disentangling deterministic structures from stochastic noise. Beyond oil markets, the framework offers broad applicability in econometric modeling, financial forecasting, and risk management, contributing to probability- and statistics-driven symmetry analysis in complex dynamic systems. Full article

In 2024, an abrupt drought-to-flood transition (ADFT) event occurred in Henan Province, China, causing severe losses to agriculture and the economy. Predicting the spatiotemporal evolution of such compound extremes remains challenging at the subseasonal scale. This study employs soil moisture percentiles to identify local droughts and floods, connects them into coherent patches, and detects an ADFT event spatiotemporally. The proposed three-dimensional identification method is further applied to evaluate the ECMWF S2S reforecasts of the 2024 ADFT event. At a 1-week lead, the ECMWF ensemble mean successfully captures the transition. However, the spatial extent is underpredicted substantially at a 2-week lead. In terms of probabilistic forecast, the Brier skill scores for drought, transition, and flood stages are 0.38, 0.57, and 0.38 at a 1-week lead, respectively. However, these scores drop sharply at a 2-week lead, particularly for the transition and flood stages. The decreased forecast skill is jointly influenced by internal dynamical errors in the model and biases in the positions of the subtropical high- and low-pressure systems at long lead. This study assesses the capability of a numerical model to predict a compound extreme from both deterministic and probabilistic perspectives, and highlights the critical role of atmospheric circulation in achieving skillful prediction. Full article

This paper presents probabilistic wind energy forecasting using quantile regression averaging combined with a conformal prediction modelling framework. The study uses data from Eskom, South Africa’s power utility company. The data is from April 2019 to November 2023. A partial linear additive quantile regression (PLAQR) averaging method is used to combine forecasts from two competing forecasting models: eXtreme Gradient Boosting (XGBoost) and Principal Component Regression (PCR). To compare the predictive abilities of the models, two data splits are used: 80%, 10% and 10% for the first set, and 85%, 10% and 5% for the second set, for training, validation and testing, respectively. Empirical results suggest that the combined predictions from PLAQR perform better than the individual models, significantly improving calibration and accuracy. The proposed combination has the smallest root mean square error (RMSE) and the highest probability of change in direction (POCID). The combination captures nonlinearities and produces well-calibrated probabilistic results. Probability integral transform histograms validate this. This performance gain reflected the importance of data volume. This is reinforced by the fact that the PLAQR model, which combines the benefits of tree-based approaches and linear models, is a robust modelling approach for reliable renewable energy forecasting. Future research directions should consider more varied ensembles. Full article

Sustainable pavement solutions that lessen the dependency on virgin materials are required due to mounting environmental and economic pressures. Although recycled asphalt concrete (RAC) has structural and environmental advantages, binder heterogeneity and non-linear material interactions make it difficult to predict the ideal bitumen content in RAC mixtures. This study predicts the bitumen content of asphalt mixtures infused with RAC by combining sophisticated machine learning (ML) with traditional laboratory testing. While this study combines AI-driven predictions with experimental insights to create a state-of-the-art framework for sustainable pavement engineering, 780 data points were obtained from the preparation and testing of three mixtures (0%, 30%, and 50% RAC) for volumetric and mechanical characteristics. Controlled Autoregressive Integrated Moving Average (CARIMA), Swapped Autoregressive Integrated Moving Average (SARIMA), radial basis function artificial neural network (RBF), bagging (BAG), multilayer perceptron (MLP) artificial neural network, and boosting (BOT) ensembles were among the models created. BAG-CARIMA-LGM is a new hybrid model that combines logistic probabilistic generalization, ensemble variance reduction, and time-series forecasting. Higher predictive accuracy and resilience across different RAC levels were attained by the hybrid BAG-CARIMA-LGM model, which performed noticeably better than standalone algorithms. The findings demonstrated improved Marshall stability and controlled flow along with a progressive decrease in mean bitumen content as RAC increased. While 50% RAC with rejuvenators maintained durability and structural integrity, the 30% RAC mixture produced the most balanced performance. The model’s capacity to manage non-linear interactions, volumetric variability, and aging effects was validated by statistical analyses. The BAG-CARIMA-LGM hybrid model optimizes RAC incorporation in asphalt mixtures, supports circular economy goals, and improves technical accuracy. The results point to a revolutionary route towards intelligent, environmentally friendly road systems that support international sustainability objectives. Full article

Low-level wind shear (LLWS) is a critical aviation hazard that can cause flight disruptions and pose significant safety risks. Despite its operational importance, forecasting LLWS remains a challenging task. To improve LLWS prediction, probabilistic forecasting approaches based on ensemble prediction systems are increasingly used. In this study, LLWS forecasts were generated using a high-resolution, limited-area ensemble model, which allows for the representation of forecast uncertainty and variability in atmospheric conditions. Forecasts for Incheon International Airport were generated twice daily over the period from December 2018 to February 2020. To enhance forecast skill, statistical post-processing techniques, specifically Ensemble Model Output Statistics (EMOS), were applied and calibrated using Aircraft Meteorological Data Relay (AMDAR) observations. Prior to calibration, rank histograms were examined to assess the reliability and distributional consistency of the ensemble forecasts. Forecast performance was evaluated using commonly applied probabilistic verification metrics, including the mean absolute error (MAE), the continuous ranked probability score (CRPS), and probability integral transform (PIT). The results indicate that ensemble forecasts adjusted through statistical post-processing generally provide more reliable and accurate predictions than the unprocessed raw ensemble outputs. Full article