Wave Prediction Research Articles

Intussusception as gastrointestinal manifestation in COVID-19 patients in paediatric age group

As knowledge of the varied manifestations of coronavirus disease 2019 (COVID-19) infection expands, symptoms such as vomiting and diarrhoea are noted as the main gastrointestinal symptoms in these patients, especially paediatric age group. With the predictions of third wave of COVID approaching soon and likely to affect paediatric population, especially in a developing country like ours, it is important to know the various presentations and managements of COVID 19. Intussusception is a common cause of bowel obstruction in the paediatric age group. Preceding viral illness and fever have been established as its underlying cause. In this report, we are reporting 2 cases of COVID 19 infection presenting as intussusception. Till June 2021, only 10 such cases have been reported worldwide.

Propagation Prediction of Body Waves in Fluid-Saturated Soils with Flow-Independent Viscosity

A systematic study of wave theory in thermoviscoelastic soil is essential for engineering applications such as geophysical exploration. In the present work, the influences of flow-independent viscosity of the soil skeleton and the thermal effect on elastic waves are considered, and the propagation behaviors of body waves in thermoviscoelastic saturated soil are investigated. Firstly, the thermoviscoelastic dynamic coupling model of saturated soil were established by employing the Biot model, the generalized thermoelastic theory, and the Kelvin–Voigt linear viscoelastic model. Secondly, the dispersion equations of body waves in thermoviscoelastic saturated soil were theoretically derived with structural symmetry considered. Finally, the variations of wave velocity and the attenuation coefficient of the body waves with the thermophysical parameters are discussed. The results revealed that the enhancement of the relaxation time of soil caused an increase of wave velocity and the attenuation coefficient of P1, P2, and S waves, and a decrease of the wave velocity and attenuation coefficient of the thermal wave. Different ranges of the permeability coefficient and frequency have different effects on the P1, P2, and S waves. The variation of thermal conductivity and the phase-lags of heat flux and temperature gradient only affect the thermal wave.

Generation mechanism and prediction of an observed extreme rogue wave

Rogue waves are individual ocean surface waves with crest height eta or trough-to-crest height H that are large compared to the significant wave height H_s of the underlying sea state: H/H_s>2.2 or eta /H_s>1.25. The physics of rogue wave generation and the potential of predicting the rogue wave risk are open questions. Only a few rogue waves in high sea states have been observed directly, but they can pose a danger to marine operations, onshore and offshore structures, and beachgoers. Here we report on a 17.6m high rogue wave in coastal waters with eta /H_s=1.98 and H/H_s=2.9 which are likely the largest normalized heights ever recorded. Simulations of random superposition of Stokes waves in intermediate water depth show good agreement with the observation. Non-linear wave modulational instability, a well known cause for rogue waves in laboratory settings, did not contribute significantly to the rogue wave generation. A parameter obtained from a routine spectral wave forecast provides a practical risk prediction for rogue waves. These results confirm that probabilistic prediction of oceanic rogue waves based on random superposition of steep waves are possible and should replace predictions based on modulational instability.

Damping rate measurements and predictions for gravity waves in an air–oil–water system

Dissipation of standing gravity waves of frequencies within 1–2 Hz is investigated experimentally. The waves are generated in a rectangular tank filled with water, the surface of which is covered with an oil layer of mean thickness, d. Damping rates are measured as a function of d, and compared with results from established theoretical models—in particular, with those from a recently developed three-fluid dissipation model that considers waves in a system of semi-infinitely deep fluids that lie above and below an interfacial fluid layer of finite thickness. Based on a comparison of experimental data with predictions, the oil–water interfacial elasticity, E2, is empirically determined to be a linear function of d. The theoretical predictions include contributions from the three-fluid dissipation model, which accounts for energy losses due to shear layers at the interfaces, friction in the fluid bulk, and compression–expansion oscillations of the elastic interfaces; and from a boundary-layer dissipation model, which accounts for energy losses due to boundary layers at the tank's solid surfaces. The linear function, E2(d), is used to compute the three-fluid model damping rate. An effective viscosity of the oil–water system is used to compute the boundary-layer model damping rate. The theoretical predictions are, on average, within 5% of measurements for all the wave frequencies considered. The promise shown by the three-fluid model is highlighted, as are the assumptions involved in the analysis and comparisons.

Mathematical Modeling of COVID-19 and Prediction of Upcoming Wave

We investigate the problem of mathematical modeling of new corona virus (COVID-19) spread in practical scenarios in various countries, specifically in India, the United States of America (USA), France, Brazil, and Turkey. We propose a mathematical model to characterize COVID-19 disease and predict the new/upcoming wave of COVID-19. This prediction is very much required to prepare medical set-ups and proceed with future plans of action. A mixture Gaussian model is proposed to characterize the COVID-19 disease. Specifically, the data corresponding to new active cases of COVID-19 per day is considered, and then we try to fit the data to a mathematical function. It is observed that the Gaussian mixture model is suitable to characterize the new active cases of COVID-19. Further, it is assumed that there are N waves of COVID-19 and the information of each upcoming wave is present in the current and previous waves as well. By using this concept, prediction of the upcoming wave can be performed. A close match between analytical results and the available results shows the correctness of the considered model.

How a Realistic Magnetosphere Alters the Polarizations of Surface, Fast Magnetosonic, and Alfvén Waves.

System‐scale magnetohydrodynamic (MHD) waves within Earth's magnetosphere are often understood theoretically using box models. While these have been highly instructive in understanding many fundamental features of the various wave modes present, they neglect the complexities of geospace such as the inhomogeneities and curvilinear geometries present. Here, we show global MHD simulations of resonant waves impulsively excited by a solar wind pressure pulse. Although many aspects of the surface, fast magnetosonic (cavity/waveguide), and Alfvén modes present agree with the box and axially symmetric dipole models, we find some predictions for large‐scale waves are significantly altered in a realistic magnetosphere. The radial ordering of fast mode turning points and Alfvén resonant locations may be reversed even with monotonic wave speeds. Additional nodes along field lines that are not present in the displacement/velocity occur in both the perpendicular and compressional components of the magnetic field. Close to the magnetopause, the perpendicular oscillations of the magnetic field have the opposite handedness to the velocity. Finally, widely used detection techniques for standing waves, both across and along the field, can fail to identify their presence. We explain how all these features arise from the MHD equations when accounting for a non‐uniform background field and propose modified methods that might be applied to spacecraft observations.

Estimation of surface wave induced fluid mud thickness on the seafloor

Mud-induced dissipation of surface wave energy is a key mechanism that affects wave propagation in muddy environments. The properties of fluid mud on seafloors required to simulate wave dissipation over shallow waters are often unknown in investigations where wave energy reduction over onshore fetch is a key factor in design of coastal infrastructure. Direct observation and estimation of the thickness of fluid mud on seafloors are complicated; this brings a major complication against accurate prediction of waves in muddy environments. As a step towards filling this gap, this study presents for the first time the estimation of the thickness of fluid mud layers surface waves generate over muddy shelves, using three methods : (1) a method based on vertical structure of acoustic backscatter that requires less assumptions, (2) a mass balance based on vertical structure of sediment concentration, and (3) an empirical approach. Comprehensive field observations of wave–mud interaction were collected for two months at 4 m water depth and analyzed here. The results, assumptions, and approaches of these three methods are evaluated. The analysis of the field observations and the estimates show that fluid mud layers formed in the wake of four significant frontal storms with relatively strong low-frequency swell energy (wave periods ≥ 7 s) during which the wave-induced orbital velocity at the bed exceeded the same threshold of ub∼ 0.5 m/s. The thickness of fluid mud layers varied between 4 cm and 15 cm among the four events. Using an onset density of fluid mud based on laboratory experiments, the mass balance method gives fluid mud thicknesses that agree with, but underestimating, the results of the acoustic backscatter method. The empirical approach agrees best with the acoustic backscatter method, when a settling velocity of 3.5 mm/s and a hydraulic roughness of 0.001 m are used, in agreement with the previous sediment and hydrodynamic data collected at the study site. Applicability of these methods to determine fluid mud thickness at other locations requires accurate description of sediment conditions near the seafloor under the influence of surface waves and hydrodynamics.

A hybrid EMD-LSTM model for non-stationary wave prediction in offshore China

Accurate wave forecasts provide vital safety guarantees for marine operations of ships. The difficulty in the study of ocean wave forecasting is that ocean waves have non-linear and non-stationary characteristics. The Auto-Regressive (AR) model is widely used in time series forecasting, but it is weak in processing non-linearity and non-stationarity. The Long Short Term Memory (LSTM) model developed for timing problems has strong non-linear processing capabilities, but there are shortcomings in the treatment of non-stationarity. Empirical Mode Decomposition (EMD) can effectively separate non-linearity and non-stationarity in data. This paper combines the advantages of the LSTM model and EMD and proposes an EMD-LSTM method. Based on significant wave heights from three locations offshore China, it has been found that the error of the EMD-LSTM method is lower than that of the LSTM model. Meanwhile, it has been found that the EMD-LSTM method can increase the forecast time in advance by more than doubled under the same tolerance by analyzing the forecast effect of different forecast times. It has been proved that the EMD-LSTM model has good superiority for the prediction of non-linear and non-stationary waves.

A Deep Learning Approach for Wave Forecasting Based on a Spatially Correlated Wind Feature, with a Case Study in the Java Sea, Indonesia

For safety and survival at sea and on the shore, wave predictions are essential for marine-related activities, such as harbor operations, naval navigation, and other coastal and offshore activities. In general, wave height predictions rely heavily on numerical simulations. The computational cost of such a simulation can be very high (and it can be time-consuming), especially when considering a complex coastal area, since these simulations require high-resolution grids. This study utilized a deep learning technique called bidirectional long short-term memory (BiLSTM) for wave forecasting to save computing time and to produce accurate predictions. The deep learning method was trained using wave data obtained by a continuous numerical wave simulation using the SWAN wave model over a 20-year period with ECMWF ERA-5 wind data. We utilized highly spatially correlated wind as input for the deep learning method to select the best feature for wave forecasting. We chose an area with a complex geometry as the study case, an area in Indonesia’s Java Sea. We also compared the results of wave prediction using BiLSTM with those of other methods, i.e., LSTM, support vector regression (SVR), and a generalized regression neural network (GRNN). The forecasting results using the BiLSTM were the best, with a correlation coefficient of 0.96 and an RMSE value of 0.06.

Spectral Algorithm in Waves Profiling and Prediction From Radar Backscatter

Conventional X-band radars constitute a convenient cheap source of measurements for deterministic sea wave profiling and prediction. The radar backscatter measurements, however, suffer from several imperfections such as the effects of larger waves shadowing smaller waves. In order to extract the wave profiles and build a reliable (spectral) sea model, multiple radar scans must be processed. A technique based on 3DFFT has been proven to show promising results and has been implemented in commercially available wave profiling radars. Nonetheless, there are several drawbacks associated with using 3DFFT to determine the phase-resolved spectral coefficients in the sea model from a number of radar scans. In this article, we present an alternative spectral approach that can offer several advantages over 3DFFT in radar wave profiling and prediction. First, it has the ability to directly handle ship movement and radar rotation antenna effects with no approximation, interpolation, or extra cost. Second, it can offer an increase in the output update rate by eliminating repeating computations, which is achieved by its recursive nature (i.e., using past output) and by its multistep architecture. Third, it can be set to readily target the relative spectral components that can best describe the wave field. The proposed technique is evaluated using raw backscatter field data from a dedicated sea trial. A simulation study is also included to gain more understanding about the performance of the proposed technique in particular and radar wave sensing and prediction in general.

Tank testing experiment of the Mocean M100 wave energy converter: linear non-causal optimal control and wave prediction

This paper presents tank testing experimental results of applying a non-causal optimal control strategy to a hinged-raft wave energy converter (WEC). The linear non-causal optimal control (LNOC) algorithm is designed and implemented in real-time to calculate the PTO torque signal based on the WEC response feedback information and the incoming wave feed-forward information. A controllable DC motor is used to provide the required torque to the WEC hinge. Tank testing results show that, compared with a well-tuned passive damper, the LNOC can improve the absorbed mechanical power in most of the tested sea conditions by up to 126%.

Decomposition and Prediction of Initial Uniform Bi-Directional Water Waves Using an Array of Wave-Rider Buoys

Decomposition and Prediction of Initial Uniform Bi-Directional Water Waves Using an Array of Wave-Rider Buoys

Modeling and prediction of the third wave of COVID-19 spread in India

Abstract In this work, we proposed a simple SEIHR compartmental model to study and analyse the third wave of COVID-19 in India. In addition to the other features of the disease, we also consider the reinfection of recovered individuals in the model. For the purpose of parameter estimation we separate the infective and deaths classes and plot them against the cumulative counts of infective and deaths from data, respectively. The estimated parameters from these two are used for prediction and further numerical simulations.We note that the infective will keep on growing and only slow down after around three months. We have studied impact of various parameters on our model and observe that the parameters associated with mask usage, screening and the care giving toCOVID-19 patients have significant impact on the prevalence and time taken to slow down the infection.We conclude that better use of mask, effective screening and timely care to infective will reduce infective and can help in disease control. Our numerical simulations can explicitly provide a short term prediction for such time line. Also we note that providing better care facilities will help reducing peak as well as the disease burden of predicted infected cases.

Prediction of Third Covid Wave in India using ARIMA Model

Whole world has been battling against the novel Covid19 pandemic that outbroke from Wuhan, China during Dec 2019. Like most other countries, India has also witnessed two Covid-19 waves with second one, during April-May 2021, being more lethal. Important lessons learnt while fighting against Covid pandemic demands never relax attitude until the fight against the pandemic is completely over. To achieve this India launched world’s biggest vaccination program. Continuous concerns are being expressed by different sections of health workers and the government regarding probable, and even more lethal, third wave in near future. Hence this work is undertaken to predict the possibility of third wave. Predictive analysis is based on progression of Covid-19 in the country thus far. Any such prediction shall offer multifaceted benefits in raising timely alarm for country to prepare itself and take measures to tap the onset of probable third wave. ARIMA model was selected for making prediction. Datasets provided by [1],[2] are utilized for the purpose. The model predicted that the latest spurt in Covid19 cases is expected to continue and grow over the remaining part of current year. This spurt can in certain probabilities lead to third Covid-19 wave around the months of Nov-Dec 2021. These predictions are though limited by the recent seroprevalence findings by ICMR and humongous vaccination drive that may retard the onset of third wave. Moreover, confined nature of recent spike (to southern Indian states, mainly Kerala) may mean that the geographical extent of current spurt is comparatively small to become a countywide wave.

Refractive Index Perception and Prediction of Radio wave through Recursive Neural Networks using Meteorological Data Parameters

Radio refractivity is very crucial in the optimal performance of radio systems and is one of the attributes that affect electromagnetic waves in the troposphere. This study presented a comparison of different variants of recurrent neural networks to predict radio refractivity index. The radio refractivity index is predicted based on forty-one years (1980 to 2020) metrological data obtained from the MERRA-2 data re-analysis database. The refractivity index was computed using International Telecommunication Union (ITU) standard. The correlation refractivity index was categorized into strong, weak and no correlation. Rainfall, relative humidity, and air pressure fall in the first category, the temperature falls in the second category while wind speed falls in the last one. The true future and predicted values of the radio refractivity index are close with GRU performing better than the other two models (LSTM and BiLSTM) which proves the accuracy of the proposed model. In conclusion, the proposed model can establish a radio refractivity status of locations at different times of the season, which is of great importance in the effective design, development, and deployment of radio communication systems.

An improved adjoint-based ocean wave reconstruction and prediction method

We propose an improved adjoint-based method for the reconstruction and prediction of the nonlinear wave field from coarse-resolution measurement data. We adopt the data assimilation framework using an adjoint equation to search for the optimal initial wave field to match the wave field simulation result at later times with the given measurement data. Compared with the conventional approach where the optimised initial surface elevation and velocity potential are independent of each other, our method features an additional constraint to dynamically connect these two control variables based on the dispersion relation of waves. The performance of our new method and the conventional method is assessed with the nonlinear wave data generated from phase-resolved nonlinear wave simulations using the high-order spectral method. We consider a variety of wave steepness and noise levels for the nonlinear irregular waves. It is found that the conventional method tends to overestimate the surface elevation in the high-frequency region and underestimate the velocity potential. In comparison, our new method shows significantly improved performance in the reconstruction and prediction of instantaneous surface elevation, surface velocity potential and high-order wave statistics, including the skewness and kurtosis.

Deterministic Sea Wave Prediction Based on Least Squares With Regularization Algorithm Using Coherent Microwave Radar

Deterministic Sea Wave Prediction Based on Least Squares With Regularization Algorithm Using Coherent Microwave Radar

COVID-19: The fourth wave prediction and prevention

COVID-19: The fourth wave prediction and prevention

Wave Prediction in a Harbour using Deep Learning with Offshore Data

In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.

A new predictor of extreme events in irregular waves considering interactions of adjacent wave groups

An extreme wave may evolve from a wave group in random wave trains. In deep and intermediate water, coalescence and interaction of adjacent wave groups are common due to strong dispersive effects of waves, although this phenomenon is sometimes omitted in the prediction of extreme waves. In the present work, a new prediction method that considers coalescence and interaction of adjacent preliminary wave groups is proposed. This method can identify wave groups that may interact rapidly over short downstream distances and then uses these groups as the initial condition for the prediction of large events. The maximum of the scaled non-uniformity wavelet power of preliminary wave groups, and the summation of the maximum of the scaled non-uniformity wavelet power of coalesced wave groups are used as precursors to predict extreme waves generated due to nonlinear self-focusing of independent wave groups and interaction of adjacent wave groups, respectively. Then three theories (linear theory; an enhanced second-order solution; and the third-order solution) are adopted to predict when and where an extreme wave will occur. After testing a large number of cases, it is found that the enhanced second-order method predicts most extreme waves effectively within 40 peak wave lengths.