Search Results (710)

This study presents a comprehensive investigation of exact fractional wave solutions and bifurcation analysis for the (3 + 1)-dimensional extended shallow water wave (3D-eSWW) equation with $\beta$-derivative, which models nonlinear wave phenomena in fluid dynamics and coastal engineering. Leveraging the flexibility of the fractional derivative, the model provides a more generalized and adaptable framework for describing shallow water wave propagation. The Modified Extended Direct Algebraic Method (MEDAM) is systematically employed to derive a broad spectrum of novel exact analytical solutions. These include the following: dark solitary waves, singular solitons, singular periodic waves, periodic solutions expressed via trigonometric and Jacobi elliptic functions, polynomial solutions, hyperbolic wave patterns, combined dark–singular structures, combined hyperbolic–linear waves, and exponential-type wave profiles. Each solution family is presented with explicit parameter constraints that ensure both mathematical consistency and physical relevance, thereby offering a robust classification of wave regimes under diverse conditions. A thorough bifurcation analysis is conducted on the reduced dynamical system to examine parametric dependence and stability transitions. Critical bifurcation thresholds are identified, and distinct solution branches are mapped in the parameter space spanned by wave numbers, nonlinear coefficients, external forcing, and the fractional order $\beta$. The analysis reveals how solution dynamics undergo qualitative transitions—such as the emergence of solitary waves from periodic patterns or the appearance of singular structures—driven by the interplay of nonlinearity, dispersion, and fractional-order effects. These insights are crucial for understanding wave stability, predictability, and the onset of extreme events in shallow water contexts. Graphical representations of selected solutions validate the analytical results and illustrate the influence of $\beta$ on wave morphology, propagation, and stability. The simulations demonstrate that varying the fractional order can significantly alter wave profiles, highlighting the role of fractional calculus in capturing complex real-world behaviors. This work demonstrates the efficacy of the MEDAM technique in handling high-dimensional fractional nonlinear PDEs and provides a systematic framework for predicting and classifying wave regimes in real-world shallow water environments. The findings not only enrich the solution inventory of the 3D-eSWW equation but also advance the analytical toolkit for studying complex spatio-temporal dynamics in fractional mathematical physics and fluid mechanics. Ultimately, this research contributes to the development of more accurate models for coastal protection, tsunami forecasting, and marine engineering applications. 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

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article

The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS. Full article

In this work, we examine the prospects of matching the Kadomtsev–Petviashvili (pKP) equation with the B-type Kadomtsev–Petviashvili (BKP) equation, which we will call the pKP-BKP equation. The resulting model gives a rigorous mathematical framework for describing long wave phenomena in oceans, impoundments and estuaries and for forecasting tsunamis; river, tide and irrigation flows; and wave patterns in the atmosphere. Using a consolidated method of analysis based on symmetry reductions and rational function transformations, we obtain several classes of exact solutions composed of rational, periodic, breather and kink-wave structures. These methods shed light on the interplay between symmetries that control the formation of soliton solutions, hence allowing the construction of new families of analytical soliton solutions. The solutions obtained are linked together through spectral degeneracies and reductions in symmetry. These methodologies are presented in a systematic way, emphasizing their applicability to a general class of nonlinear evolution equations. The results of the analysis are substantiated through direct substitution, and the structural characteristics of the solutions are discussed in detail. As a result, these results expand the solution space of the pKP–BKP equation and provide better analytical insights into Kadomtsev–Petviashvili-type nonlinear evolution equations. Full article

This paper investigates the optical solitons to the M-truncated fractional $(1+1)$-dimensional nonlinear generalized Bretherton model with arbitrary constants. It is employed to forecast the movement of liquid droplets or gas bubbles in microchannels, which is crucial for drug delivery systems, biomedical diagnostics, and lab-on-a-chip technologies. We obtain optical soliton solutions using the extended hyperbolic function method (EHFM) and the modified extended tanh method (METM). Numerous solutions, such as singular, periodic–singular, bright, and dark optical solitons, are obtained from our investigation. The $2D$ graphical depiction of the solutions shows a variety of wave patterns that change with varied values of $\alpha$ and t. The wave’s amplitude forms become more apparent as $\alpha$ and t increase. Using 2D plots, the comparison of fractional effects for the M-truncated fractional derivative is demonstrated by giving specific values to the fractional parameter. Full article

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development. Full article

Accurate wave prediction in coastal waters is essential for marine safety and engineering, yet it is significantly influenced by uncertainties in wind forcing and dissipation parameterization. This study evaluates the sensitivity of the SWAN model around the Korean Peninsula using 2021 data from 138 observation stations. To address structural biases in wind fields, the Drag Coefficient Scaling Factor (CDFAC) was implemented alongside the Komen and ST6 physics packages. While the Komen scheme provided stable performance under normal conditions, the ST6 + CDFAC configuration exhibited superior physical consistency during extreme events. Notably, applying CDFAC to the ST6 package reduced the high-wave (H_s > 3 m) RMSE by approximately 32.7%, decreasing from 0.52 m to 0.35 m. Bathymetric stratified analysis further confirmed that the ST6 scheme maintains robust performance in offshore and deep-water regions (depth > 50 m), achieving a correlation of 0.94 and an RMSE of 0.20 m. This is attributed to ST6’s frequency-dependent saturation approach, which effectively decouples wind-sea and swell components in environments where whitecapping dissipation is the governing energy sink. In contrast, improvements in coastal waters (depth < 50 m) were moderated by topographical dissipation mechanisms such as bottom friction and depth-induced breaking. These findings demonstrate that integrating wind input bias correction with frequency-dependent dissipation physics is vital for reliable wave forecasting and coastal disaster mitigation. Full article

This paper presents a comprehensive real-time forecasting and mapping cycle of a regional flood event, encompassing quantitative precipitation forecasting, runoff production and routing, and inundation mapping. The objective of this study is to highlight the significant uncertainties inherent in each step of the fully automated cycle, despite the utilization of state-of-the-art models and remote sensing technologies. The case study focuses on a significant flood event that occurred in the Turkey River and Upper Iowa River, in rural Iowa, United States, resulting in localized damage and disruption to several small communities. The novelty of this study is that it demonstrates the limited utility of satellite-based remote sensing in the absence of other forecasting and mapping system elements, emphasizing the need for the timely integration of information from diverse sources to accurately forecast and map floods. To achieve this, we assembled and analyzed precipitation data from weather radars, streamflow estimates derived from river stages and rating curves, and cross-sectional data from river channels to characterize the movement of the flood wave. These data were integrated into hydrologic and hydraulic models to generate flood inundation estimates for the more severely affected areas. Remote sensing imagery was obtained and used as reference to assess the accuracy of the modeled inundated areas. Our findings illustrate that, despite the increasing availability of satellite data sources, there are still significant limitations to tracking inundation using satellite remote sensing, particularly for medium-sized basins. Flood modeling processes are not merely complementary to satellite-based flood estimation, but essential for comprehensive flood risk assessment. Full article

Coastal erosion is increasingly influenced by anthropogenic alterations to the sediment cycle and morphological transformations. Traditional shallow water models often neglect the mechanical behavior of the seabed and its rheological response to hydrodynamic forcing, limiting their accuracy in forecasting erosion patterns. To address these limitations, this study extends the classical one-dimensional Saint-Venant (shallow water) model by incorporating effects of viscosity, frictional effects, sediment transport and viscoelasticity. The seabed is treated as a Kelvin–Voigt material, characterized by an elastic modulus and a viscous damping coefficient, to account for both immediate and time-dependent mechanical responses. Using the COMSOL Multiphysics platform, the evolution of the water column and seabed was simulated in six idealized case studies under various conditions, including changes in seabed topography and different frictional and dispersive regimes. The results demonstrate the influence of seabed topography, friction $S_f$, diffusion/dispersion regularization term $E$, and viscoelastic properties on wave seabed interactions and morphodynamic bed evolution (Exner-type). The inclusion of viscoelastic damping contributes to the stabilization of morphological evolution, mitigating abrupt changes in bathymetry and enhancing the physical realism of the simulations. The whole research aims to improve the prediction capabilities of erosion processes and advance the current modeling tools. Full article

The uncertainty and enormous potential of wave energy have drawn attention and research efforts on predicting offshore wave behavior to aid wave energy harvesting. The movement of offshore waves generates huge amounts of available renewable energy and creates a unique offshore energy source. Because offshore waves are mainly generated by wind, this paper focused on using wind speed as the main factor to predict offshore wave power density to assist wave energy harvesting. The dynamic behaviors of wave energy were displayed in this paper in a format of wave power density distribution, which was extracted and visualized in MATLAB. The model was reconstruction based on a long short-term memory (LSTM) neural network for one week and 3 h wave power density forecasting, integrated with wind conditions as input in two scenarios. One scenario explored the location effect for wave density forecasting. Another scenario compared the influence of different time series input of the structure. RMSE was used as a criteria estimator of the accuracy. The data period ranges from 1979 to 2019 in the Gulf of Mexico exacted from WaveWatch III. The lowest RMSE among different locations is 0.104, while the different time step scenario has an RMSE of 0.715. Because wind speed data is much easier to get from either hindcast dataset or actual measurement, the proposed method with the resulting accuracy will make the forecasting of wave power density much easier. The method has the ability to be implemented in other wave thriving locations, which fills the gap of forecasting on wave height and period based on buoy data given a lack of measurements, as well as reflecting the correlations between wind speed and wave density, thus providing support for a quantitative correlation model based on a deep-learning-based model. Full article

The reported evidence for an isotropic gravitational-wave background (GWB) from pulsar timing array (PTA) collaborations has motivated searches for extrinsic and intrinsic anisotropies. Kinematic anisotropies may arise as a consequence of a boosted observer moving with respect to the frame in which the GWB appears isotropic. In this work, we present an analytical toolbox to describe the effects of kinematic anisotropies on the overlap reduction function. Our analytical results differ from previous findings at the quadrupole order and are detailed in three appendices. For the first time, we also derive the corresponding auto-correlation using two approaches, taking the pulsar distances to be infinite or finite, respectively. Our formulas can be used in forecasts or Bayesian analysis pipelines. Full article

The main objective of this work is to characterize Sudden Stratospheric Warming (SSW) conditions and their impact on local weather forecasting and climate change, using SSW definition criteria. The SSWs strongly affect Arctic vortex structure and midlatitude weather conditions. This work evaluates the frequency, amplitude, and dynamical–thermal characteristics of SSWs under historical and Representative Concentration Pathway (RCP) 4.5 scenarios, focusing on stratospheric air temperature (T_s) and zonal wind speed (U_h) at the 10° N and 60° N latitudes. The fifth-generation ECMWF atmospheric reanalysis (ERA5) is employed as the reference dataset. Simulations of five Coupled Model Intercomparison Project Phase 5 (CMIP5) models, represented by M1 to M5, are analyzed. The primary group of models included 1) the Australian Community Climate and Earth-System Simulator, version 1.3 (ACCESS1-3, M1), 2) the Hadley Center Global Environmental Model, version 2—Carbon Cycle (HadGEM2-CC, M2), and 3) the Max Planck Institute Earth System Model—Medium Resolution (MPI-ESM-MR, M3). The analysis period covers SSW events related to the Quasi-Biennial Oscillation (QBO) in the Northern Hemisphere (NH) from 1980 to 2100. The key findings indicate that while M1, M2, and M3 simulate SSW occurrence correctly for the 21st century, they exhibit significant systematic deficiencies in capturing the structural dynamics of SSW events. Specifically, the M1, M2, and M3 models underestimate the polar stratospheric temperature amplitude (T_amp) by approximately 75–80% and zonal wind amplitude (U_amp) by more than 60% compared to the ERA5 analysis. Furthermore, ERA5 exhibits a strong negative correlation (R ≈ −0.8) between U_h and T_s that is not estimated accurately using the present models. The importance of the horizontal resolution of the models and wave–mean flow interactions in determining SSW intensity and occurrence is also found to be a critical metric. Results suggest that SSW definition criteria affect Arctic and midlatitude weather system prediction at a rate of 61–82%. It is concluded that the primary configurations of CMIP5 models for accurately capturing the dynamical structure and evolution of QBO–SSW interactions are needed, and that they affect future projections of SSW events. Full article

In recent years, the increasing frequency of strong and super typhoons has been attributed to rising sea surface temperatures due to global warming. This study utilized the Weather Research and Forecasting (WRF) and Simulating WAves Nearshore (SWAN) models to analyze 30 years of wind and wave data for the Yangjiang sea area in China. The accuracy of the numerical simulations was validated using observed data from typhoons Ty201213, Ty201522, Ty201822, and Ty202118, along with wind and wave data from December 2024. This study utilized the P-III distribution to analyze design wind parameters. At a height of 10 m, the 3 s and 10 min mean wind speeds for the 100- and 50-year return periods were 62.21 m/s, 47.85 m/s, 57.99 m/s, and 44.61 m/s, respectively. At hub height (170 m), the corresponding values were 80.27 m/s, 61.75 m/s, 74.84 m/s, and 57.57 m/s. Furthermore, this study successfully applied a 2D-KDE approach to construct a joint probability model and derive environmental contours for extreme environmental assessments. The HS and TP at project point P for the 100- and 50-year return periods are 13.61 m and 15.91 s, as well as 12.39 m and 15.07 s, respectively. Full article

The Damietta–Port Said coast, Nile Delta, has experienced extreme morphological change over the past four decades due to sediment reduction due to Aswan High Dam and continued anthropogenic pressures. Using multi-temporal Landsat (1985–2025) and high-resolution RapidEye and PlanetScope imagery with 50 m-spaced transects, the study documents major shoreline shifts: the Damietta sand spit retreated by >1 km at its proximal apex while its distal tip advanced by ≈3.1 km southeastward under persistent longshore drift. Sectoral analyses reveal typical structure-induced patterns of updrift accretion (+180 to +210 m) and downdrift erosion (−50 to −330 m). To improve predictive capability beyond linear DSAS extrapolation, Nonlinear Autoregressive Exogenous (NARX) and Bidirectional Long Short-Term Memory (BiLSTM) neural networks were applied to forecast the 2050 shoreline. BiLSTM demonstrated superior stability, capturing nonlinear sediment transport patterns where NARX produced unstable over-predictions. Furthermore, coupled wave–flow modeling validates a sustainable management strategy employing successive short groins (45–50 m length, 150 m spacing). Simulations indicate that this configuration reduces longshore current velocities by 40–60% and suppresses rip-current eddies, offering a sediment-compatible alternative to conventional breakwaters and seawalls. This integrated remote sensing, hydrodynamic, and AI-based framework provides a robust scientific basis for adaptive, sediment-compatible shoreline management, supporting the long-term resilience of one of Egypt’s most vulnerable deltaic coasts under accelerating climatic and anthropogenic pressures. Full article