Search Results (15,850)

Coastal shipping plays a critical role in meeting maritime decarbonization targets under the International Maritime Organization’s (IMO) Carbon Intensity Indicator (CII) and the European Union Emissions Trading System (EU ETS); however, operators currently lack robust tools to forecast route-specific carbon intensity and evaluate the causal benefits of fuel switching. This study developed a distribution-free causal forecasting framework for voyage-level Carbon Dioxide (CO₂) intensity using an enriched panel of 1440 real-world voyages across four Nigerian coastal routes (2022–2024). We employed a physics-informed monotonic Light Gradient Boosting Machine (LightGBM) model trained under a strict leave-one-route-out (LORO) protocol, integrated with split-conformal prediction for uncertainty quantification and Causal Forests for estimating heterogeneous treatment effects. The model predicted emission intensity on completely unseen corridors with a Mean Absolute Error (MAE) of 40.7 kg CO₂/nm, while 90% conformal prediction intervals achieved 100% empirical coverage. While the global average effect of switching from heavy fuel oil to diesel was negligible (≈−0.07 kg CO₂/nm), Causal Forests revealed significant heterogeneity, with effects ranging from −74 g to +29 g CO₂/nm depending on route conditions. Economically, targeted diesel use becomes viable only when carbon prices exceed ~100 USD/tCO₂. These findings demonstrate that effective coastal decarbonization requires moving beyond static baselines to uncertainty-aware planning and targeted, route-specific fuel strategies rather than uniform fleet-wide policies. Full article

Water quality prediction serves as a crucial basis for water environment supervision and is of great significance for water resource protection. This study utilized meteorological and water quality data from 40 monitoring stations in the Tuojiang River Basin, Sichuan Province, China. A Gated Recurrent Unit (GRU) model and a Graph Attention Network–Gated Recurrent Unit (GAT-GRU) model were constructed. Furthermore, based on the GAT-GRU framework, an Enhanced Spatio-Temporal Relation-Guided Gated Recurrent Unit (ESRG-GRU) model was developed by incorporating an explicit river network topology and a loss function that is sensitive to extreme values to strengthen spatio-temporal relationships. Water quality predictions were made for all 40 stations, and the performance of the three models was compared. The results show that, during the 7-day forecasting period, the training time of both the ESRG-GRU and the GAT-GRU models was only about 1/40 of that required for the GRU model. In terms of prediction accuracy, the average Nash–Sutcliffe efficiency (NSE) values over the 7-day forecast period were ESRG-GRU (0.7904) > GAT-GRU (0.7557) > GRU (0.6870), while the average root mean square error (RMSE) values were ESRG-GRU (0.0156) < GAT-GRU (0.0168) < GRU (0.0185). Regarding accuracy across different regions and seasons within the river basin, the ESRG-GRU model, guided by enhanced spatio-temporal deep learning, consistently outperformed both the GRU and the GAT-GRU models. This method can effectively enhance both the efficiency and accuracy of water quality prediction, thereby providing support for water environment supervision and regional water quality improvement. Full article

Time series forecasting plays a critical role in a wide range of applications, including energy load forecasting, traffic flow management, weather prediction, and vision-based state prediction for autonomous driving. In the context of autonomous vehicles, accurate forecasting of sequential visual information—such as traffic participant trajectories, road condition variations, and obstacle motion trends perceived by onboard sensors—is a fundamental prerequisite for safe and reliable decision-making. To overcome the limitations of existing long-term time series forecasting models, particularly their insufficient capability in temporal feature extraction, this paper proposes a Local–Global Adaptive Transformer (LGAT) for long-term time series forecasting. The proposed model incorporates three key innovations: (1) a period-aware positional encoding mechanism that embeds intrinsic periodic patterns of time series into positional representations and adaptively adjusts encoding parameters according to data-specific periodicity; (2) a temporal feature enhancement module based on gated convolution, which effectively suppresses noise in raw inputs while emphasizing discriminative temporal characteristics; and (3) a local–global adaptive attention layer that combines sliding window–based local attention with importance-aware global attention to simultaneously capture short-term local variations and long-term global dependencies. Experimental results on five public benchmark datasets demonstrate that LGAT consistently outperforms most baseline models, indicating its strong potential for time series forecasting applications in autonomous driving scenarios. Full article

Daily runoff series exhibit high complexity and significant fluctuations, which often lead to large prediction errors and limit the scientific basis of water resource scheduling and management. This study proposes a runoff prediction framework that incorporates upstream–downstream hydrological correlation information and integrates Improved Empirical Wavelet Transform (IEWT), SHAP-based interpretable feature selection, Improved Population-Based Training (IPBT), and the Modern Temporal Convolutional Network (ModernTCN) to enhance forecasting accuracy and model robustness. First, IEWT is employed to perform multi-scale decomposition of the daily runoff sequence, extracting structural features at different temporal scales. Then, upstream–downstream hydrological correlation information is introduced, and the SHAP method is used to evaluate the importance of multi-source basin features, eliminating redundant variables to improve input quality and training efficiency. Finally, IPBT is applied to optimize ModernTCN hyperparameters, thereby constructing a high-performance forecasting model. Case studies at the Hankou station demonstrate that the proposed IPBT-IEWT-SHAP-ModernTCN model significantly outperforms benchmark methods such as LSTM, iTransformer, and TCN in terms of accuracy, stability, and generalization. Specifically, the model achieves a root mean square error of 342.14, a mean absolute error of 251.01, and a Nash–Sutcliffe efficiency of 0.9992. These results indicate that the proposed method can effectively capture the nonlinear correlation characteristics between upstream and downstream hydrological processes, thus providing an efficient and widely adaptable framework for daily runoff prediction and scientific water resources management. Full article

Wind-assisted ship propulsion (WASP) has gained considerable interest as a means of reducing fuel consumption and Greenhouse Gas (GHG) emissions, with further benefits when combined with weather-optimized routing. This study employs and extends a National Technical University of Athens (NTUA) weather-routing optimization tool to more realistically assess WASP performance through integrated modeling. The original tool minimized fuel consumption using forecasted weather data and a physics-based performance model. A previous extension to account for the WASP effect introduced a 1-Degree Of Freedom (DOF) model that accounted only for longitudinal hydrodynamic and aerodynamic forces, estimating the reduced main-engine power required to maintain speed in given conditions. The current study incorporates a 3-DOF model that includes side forces and yaw moments, capturing resulting drift and rudder deflection effects. A Kamsarmax bulk carrier equipped with suction sails served as the case study. Initial simulations across various operating and weather conditions compared the two models. The 1-DOF model predicted fuel-saving potential up to 26% for the tested apparent wind speed and the range of possible headings, whereas the 3-DOF model indicated that transverse effects reduce WASP benefits by 2–7%. Differences in Main Engine (ME) power estimates between the two models reached up to 7% Maximum Continuous Rating (MCR) depending on the speed of wind. The study then applied both models within a weather-routing optimization framework to assess whether the optimal routes produced by each model differ and to quantify performance losses. It was found that the revised optimal route derived from the 3-DOF model improved total Fuel Oil Consumption (FOC) savings by 1.25% compared with the route optimized using the 1-DOF model when both were evaluated with the 3-DOF model. Full article

In Long Short-Term Memory (LSTM)-based runoff forecasting models, the selection of input schemes is critically important. This study, using daily rainfall and runoff data from the Jingle Basin (2006–2014), investigated three input schemes to evaluate their forecasting efficacy and employed the Shapley Additive Explanation (SHAP) method to quantitatively analyze variable contributions. The results demonstrate that LSTM model performance deteriorates with increasing lead time, achieving optimal accuracy at a 1-day lead (MAE: 0.90 m³/s, RMSE: 3.09 m³/s, NSE: 0.84). The results, validated by significance testing, are reasonable; incorporating precipitation characteristics significantly enhances model performance compared to baseline schemes, reducing RMSE by 6–34% and improving NSE by 9–14%. SHAP analysis reveals antecedent runoff as the dominant influencing factor, accounting for 65.9–84.7% of total importance. Furthermore, the contributions of trend, seasonal, and residual components progressively increase with extended lead times, demonstrating non-negligible roles in forecast outcomes. These findings, confirmed by significance testing, provide quantitative insights into input variable contributions to target uncertainty and enhance the mechanistic understanding of precipitation-runoff relationships, offering valuable references for optimizing hydrological forecasting systems. Full article

Significant wave height (SWH) is a key descriptor of sea state, yet providing accurate, site-specific forecasts at low computational cost remains challenging. This study proposes a transfer-learning-based framework for SWH forecasting that combines Seasonal and Trend decomposition using Loess (STL), a stacked long short-term memory (LSTM) network, and an efficient sliding-window updating scheme. First, STL is applied to decompose the SWH time series into trend, seasonal, and remainder components; the resulting sub-series are then fed into a transfer-learning architecture in which the parameters of the stacked LSTM backbone are kept fixed, and only a fully connected output layer is updated in each window. Using multi-year observations from five National Data Buoy Center (NDBC) buoys, the proposed STL-LSTM-T model is compared with a STL-LSTM configuration that is fully retrained after each STL decomposition. For example, the transfer-learning setup reduces MAE, MSE, and RMSE by up to 11.2%, 19.2%, and 14.5% at buoy 46244, respectively, while reducing the average training time per update to about one-fifth of the baseline. Parameter analyses indicate that a two-layer LSTM backbone and moderate continuous forecast step (6–12 steps) provide a good balance between predictive accuracy, error accumulation, and computational cost, making STL-LSTM-T suitable for SWH forecasting on resource-constrained platforms. Full article

Precision agriculture has increasingly adopted controlled agricultural microclimates, particularly smart greenhouses, as a strategy to enhance crop yields while maintaining environmental conditions within suitable ranges for each crop. Among the variables that govern the water balance in these systems, air humidity plays a critical role; therefore, accurate humidity forecasting is essential for implementing timely control actions that support productivity levels. However, greenhouse conditions are frequently perturbed by extreme weather events, which lead to nonlinear and non-stationary humidity dynamics. In this context, the aim of this study was to design an optimized evolving fuzzy inference system for humidity forecasting that can adapt to changing and unforeseen situations in agricultural microclimates. A prototyping-based methodology was followed, including phases of communication, quick planning, modeling and quick design, construction of the prototype, and deployment. A hybrid genetic algorithm was used to optimize the parameters of an evolving Mamdani-type fuzzy inference system, extended to handle missing values in online data streams. Thirty independent optimization runs were performed, and the best configuration achieved a mean squared error of 1.20 × 10⁻² in humidity forecasting using one minute of data for three months. The resulting model showed high interpretability, with an average number of 1.35 rules, tolerance for missing values, imputing 2% of the data, and robustness to sudden changes in the data stream with a p-value of 0.01 for the Augmented Dickey–Fuller test at alpha = 0.05. In general, the optimized evolving fuzzy inference system obtained an effectiveness rate greater than 90% and demonstrated adaptability to extreme weather conditions, suggesting its applicability to other phenomena with similar characteristics. Full article

The crude oil futures market is highly susceptible to policy changes and international relations, which often trigger abrupt jumps in prices. The existing literature rarely considers jump volatility and the underlying impact mechanisms. This study proposes a hybrid forecasting model integrating a convolutional neural network (CNN) and self-attention (Transformer) for high-frequency financial data, based on the complex network characteristics between trading information and multi-market financialization indicators. Empirical results demonstrate that incorporating complex network indicators enhances model performance, with the CNN–Transformer model with a complex network achieving the highest predictive accuracy. Furthermore, we verify the model’s effectiveness and robustness in the WTI crude oil market via Diebold–Mariano tests and external event shock. Notably, this study also extends the analytical framework to jump intensity, thereby providing a more accurate and robust jump forecasting model for risk management and trading strategies in the crude oil futures market. Full article

Modern port operations face increasing challenges from rapidly changing weather and environmental conditions, requiring accurate short-term forecasting to support safe and efficient maritime activities. This study presents a sensor fusion-based machine learning framework for real-time multi-target nowcasting of wind gust speed, sustained wind speed, and wind direction using heterogeneous data collected at the Port of Livorno from February to November 2025. Using an IoT architecture compliant with the oneM2M standard and deployed at the Port of Livorno, CNIT integrated heterogeneous data from environmental sensors (meteorological stations, anemometers) and vessel-mounted LiDAR systems through feature-level fusion to enhance situational awareness, with gust speed treated as the primary safety-critical variable due to its substantial impact on berthing and crane operations. In addition, a comparative performance analysis of Random Forest, XGBoost, LSTM, Temporal Convolutional Network, Ensemble Neural Network, Transformer models, and a Kalman filter was performed. The results show that XGBoost consistently achieved the highest accuracy across all targets, with near-perfect performance in both single-split testing (R² ≈ 0.999) and five-fold cross-validation (mean R² = 0.9976). Ensemble models exhibited greater robustness than deep learning approaches. The proposed multi-target fusion framework demonstrates strong potential for real-time deployment in Maritime Autonomous Surface Ship (MASS) systems and port decision-support platforms, enabling safer manoeuvring and operational continuity under rapidly varying environmental conditions. Full article

This study focuses on forecasting and optimizing the performance of a real-world object modelled by a multi-server queueing system that processes two types of users: primary (new) users and repeating users. The repeating users are those who succeeded in entering processing upon arrival and then decided to repeat it. These users have privilege and can enter processing when they wish once at least one device is idle. The primary user is admitted to the system only if the number of occupied devices is less than some threshold value and the quantity of repeating users residing in the system does not exceed certain thresholds. Repeating users are impatient and non-persistent. Arrivals of primary users are described by the Markovian arrival process. Processing times of primary and repeating users have distinct phase-type distributions. Utilizing the concept of the generalized phase–time distributions, the dynamics of this queueing system are formally characterized by the multidimensional Markov chain, which is examined in this paper. The ergodicity condition is derived. The relation of the key performance characteristics of the system and the thresholds defining the policy of the primary user’s admission is numerically highlighted. Optimal threshold selection is demonstrated numerically. Full article

Aiming at the problem that the current traditional apple yield estimation methods rely on manual investigation and do not make full use of multi-source information, this paper proposes an apple orchard yield prediction framework combining multispectral remote sensing features and ground features. The framework is oriented to the demand of yield prediction at different scales. It can not only realize the prediction of apple yield at the district and county scales, but also modify the prediction results of small-scale orchards based on the acquisition of orchard features. The framework consists of three parts, namely, apple orchard planting area extraction, district and county large-scale yield prediction and small-scale orchard yield prediction correction. (1) During apple orchard planting area extraction, the samples of some apple planting areas in the study area were obtained through field investigation, and the orchard and non-orchard areas were classified and discriminated, providing a spatial basis for the collection of subsequent yield prediction-related data. (2) In the large-scale yield prediction of districts and counties, based on the obtained orchard-planting areas, the corresponding multispectral remote sensing features and environmental features were obtained using Google Earth engine platform. In order to avoid the noise interference caused by local pixel differences, the obtained data were median synthesized, and the feature set was constructed by combining the yield and other information. On this basis, the feature set was divided and sent to Apple Orchard Yield Prediction Network (APYieldNet) for training and testing, and the district and county large-scale yield prediction model was obtained. (3) During the part of small-scale orchard yield prediction correction, the optimal model for large-scale yield prediction at the district and county levels is utilized to forecast the yield of the entire planting area and the internal local sampling areas of the small-scale orchard. Within the local sampling areas, the number of fruits is identified through the YOLO-A model, and the actual yield is estimated based on the empirical single fruit weight as a ground feature, which is used to calculate the correction factor. Finally, the proportional correction method is employed to correct the error in the prediction results of the entire small-scale orchard area, thus obtaining a more accurate yield prediction for the small-scale orchard. The experiment showed that (1) the yield prediction model APYieldNet (MAE = 152.68 kg/mu, RMSE = 203.92 kg/mu) proposed in this paper achieved better results than other methods; (2) the proposed YOLO-A model achieves superior detection performance for apple fruits and flowers in complex orchard environments compared to existing methods; (3) in this paper, through the method of proportional correction, the prediction results of APYieldNet for small-scale orchard are closer to the real yield. Full article

Accurate forecasting of solar energy production plays a crucial role in optimizing power system reliability, scheduling, and integration of renewable energy sources into the grid. From a sustainability perspective, improved forecasting accuracy supports more efficient day-ahead planning, reduces imbalance costs, and contributes to the sustainable operation of solar energy systems. Artificial neural networks (ANNs) are widely applied for this purpose due to their capability to capture complex nonlinear relationships between meteorological variables and solar power output. However, the performance of ANNs depends on the number of layers, the number of neurons in the hidden layer, the max failure value, and the transfer function. This study proposes a hybrid forecasting model that combines artificial neural networks with the recently developed Tyrannosaurus Optimization Algorithm (TROA), a metaheuristic optimization method. The aim is to optimize the hyperparameters of artificial neural networks to minimize the Mean Absolute Percentage Error (MAPE) in solar energy forecasting. The results of the TROA were compared with other metaheuristic methods, such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). The TROA gave the network structure for ANNs, which forecasted closer to the actual values than other metaheuristic methods and showed success on 105 days of the test dataset, with an MAPE rate of 3.64%. Additionally, an MAPE of 1.42% was obtained over a test period of 18 days used for out-of-evaluation, indicating competitive performance compared to the other methods. These findings highlight the effectiveness of the TROA in forecasting solar energy using ANNs. Full article

Shale oil reservoirs exhibit ultralow permeability and complex pore structures, which result in non-Darcy low-velocity flow and cause permeability to be stress-sensitive. Moreover, two-phase flow of oil and gas frequently occurs during the depletion of shale oil reservoirs. Consequently, investigating the rate-transient behavior of shale oil wells necessitates comprehensive consideration of multiphase flow, threshold pressure gradients, and stress sensitivity. Although numerous analytical models exist for rate-transient analysis of multistage fractured horizontal wells, none of them simultaneously incorporate these critical factors. In this study, we extend the classical five-region model to incorporate multiphase flow, threshold pressure gradients, and stress sensitivity. The proposed model is solved using Pedrosa’s transformation, perturbation theory, the Laplace transform, and the Stehfest numerical inversion method. A systematic analysis of the influence of various parameters on the oil production rate and cumulative oil production is conducted, and a field case study is presented to validate the applicability and effectiveness of the model. It is found that the permeability modulus of the main fracture, the half-length of the main fracture, and the threshold pressure gradient of the unstimulated reservoir have a significant influence on cumulative oil production spanning 20 years. With a 100% relative input error, these parameters result in prediction errors of 23.77%, 16.65%, and 17.78%, respectively. In contrast, the threshold pressure gradient of the main fracture and the threshold pressure gradient of the stimulated reservoir have a negligible impact; under the same level of input error (100%), they cause only 0.36% and 0.48% prediction errors in the 20-year cumulative oil production period, respectively. This research provides an efficient and reliable framework for analyzing production data and forecasting shale oil well performance. Full article

Accurate air quality forecasting remains challenging due to persistent biases in chemical transport models. Addressing this challenge, the current study develops pollutant-specific deep learning frameworks that correct systematic errors in the Community Multiscale Air Quality (CMAQ) simulations of nitrogen dioxide (NO₂) and coarse particulate matter (PM₁₀) over California. Building upon a previous study on ozone bias correction, a hybrid CNN–Attention–LSTM architecture is adapted, and a weighted Huber loss function is introduced for PM₁₀ to enhance the detection of extreme pollution events through a gated tail-weighting mechanism. Using data from twenty EPA monitoring stations (ten per pollutant) for 2010–2014, the proposed approach achieves substantial performance gains over the CMAQ baseline. For NO₂, RMSE decreases by ~51% with an average systematic bias reduction of ~80% and a random error reduction of ~42%. For PM₁₀, RMSE improves by ~49% while the systematic and random errors decrease by ~94% and ~33%, respectively. The PM₁₀ model also shows high consistency with observations (Index of Agreement improvement of ~105%) and a strong ability to capture peak events (F1 score improvement of ~270%), while the NO₂ model achieves large gains in explanatory power (R² improvement averaging ~816%). Both pollutants also demonstrate enhanced temporal agreement between predictions and observations, as confirmed by the Dynamic Time Warping analysis (NO₂: ~55%, PM₁₀: ~58%). These results indicate that pollutant-specific loss functions and architectural tuning can significantly improve both accuracy and event sensitivity, offering a transferable framework for bias correction across multiple pollutants and regions. Full article