Real time wave forecasting using wind time history and numerical model

Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.     

Wave height forecasting in Dayyer, the Persian Gulf

Forecasting of wave parameters is necessary for many marine and coastal operations. Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24 h in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous 3 h, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6 h. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds.     

Quasi-linear model of interaction of surface waves with strong and hurricane winds

A quasi-linear model for determining the aerodynamic drag coefficient of the sea surface and the growth rate of surface waves under a hurricane wind is proposed. The model explains the reduction (stabilization) in the drag coefficient during hurricane winds. This model is based on the solution of the Reynolds equations in curvilinear coordinates with the use of the approximation of the eddy viscosity, which takes into account the presence of the viscous sublayer. The profile of the mean wind velocity is found with consideration for nonlinear wave stresses (wave momentum flux), whereas wave disturbances induced in air by waves on the water surface are determined in the context of linear equations. The model is verified by comparing the calculation results with experimental data for a wide range of wind velocities. The growth rate and drag coefficient for hurricane winds are calculated both with and without consideration for the shortwave portion of the windwave spectrum. On the basis of calculations with the quasi-linear model, a simple parametrization is proposed for the drag coefficient and the growth rate of surface waves during hurricane winds. This model is convenient for use in models of forecasting winds and waves.     

A forecasting model for wave heights based on a long short-term memory neural network

To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory(LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error(RMSE), scatter index(SI) and mean absolute error(MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.     

Wave Numerical Model for Shallow Water

The history of forecasting wind waves by wave energy conservation equation is briefly des-cribed.Several currently used wave numerical models for shallow water based on different wave theoriesare discussed.Wave energy conservation models for the simulation of shallow water waves are introduced,with emphasis placed on the SWAN model,which takes use of the most advanced wave research achieve-ments and has been applied to several theoretical and field conditions.The characteristics and applicabilityof the model,the finite difference numerical scheme of the action balance equation and its source termscomputing methods are described in detail.The model has been verified with the propagation refractionnumerical experiments for waves propagating in following and opposing currents;finally.the model is ap-plied to the Haian Gulf area to simulate the wave height and wave period field there,and the results arecompared with observed data.     

Application of artificial neural networks in tide-forecasting

An accurate tidal forecast is an important task in determining constructions and human activities in ocean environments. Conventional tidal forecasting has been based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters are required for the prediction of a long-term tidal level with harmonic analysis. Unlike conventional harmonic analysis, this paper presents an artificial neural network (ANN) model for forecasting the tidal-level using the short term measuring data. The ANN model can easily decide the unknown parameters by learning the input–output interrelation of the short-term tidal records. Three field data with three types of tides will be used to test the performance of the proposed ANN model. The numerical results indicate that the hourly tidal levels over a long duration can be predicted using a short-term hourly tidal record.     

基于Prophet算法的海南近海波浪长时段时序分析与预测

近年来,以大数据为基础的人工智能算法逐步兴起并被用于海洋波浪短期预测.本文采用2015-2019年海南近海逐时波浪实测时序数据,基于Prophet算法建立了海南近海波浪长时段时序预测模型,分析了2015-2019年海南近海波浪日、月、年变化特性,并对海南近海2020年波浪变化过程进行了预测.结果显示,Prophet算法...     

Numerical investigation of ocean waves generated by three typhoons in offshore China

The influences of the three types of reanalysis wind fields on the simulation of three typhoon waves occurred in 2015 in offshore China were numerically investigated. The typhoon wave model was based on the simulating waves nearshore model (SWAN), in which the wind fields for driving waves were derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-interim), the National Centers for Environmental Prediction climate forecast system version 2 (CFSv2) and cross-calibrated multi-platform (CCMP) datasets. Firstly, the typhoon waves generated during the occurrence of typhoons Chan-hom (1509), Linfa (1510) and Nangka (1511) in 2015 were simulated by using the wave model driven by ERA-interim, CFSv2 and CCMP datasets. The numerical results were validated using buoy data and satellite observation data, and the simulation results under the three types of wind fields were in good agreement with the observed data. The numerical results showed that the CCMP wind data was the best in simulating waves overall, and the wind speeds pertaining to ERA-Interim and CCMP were notably smaller than those observed near the typhoon centre. To correct the accuracy of the wind fields, the Holland theoretical wind model was used to revise and optimize the wind speed pertaining to the CCMP near the typhoon centre. The results indicated that the CCMP wind-driven SWAN model could appropriately simulate the typhoon waves generated by three typhoons in offshore China, and the use of the CCMP/Holland blended wind field could effectively improve the accuracy of typhoon wave simulations.     

Forecast of wave run-up on coastal structure using offshore wave forecast data

In this paper, first we introduce the wave run-up scale which describes the degree of wave run-up based on observed sea conditions near and on a coastal structure. Then, we introduce a simple method which can be used for daily forecast of wave run-up on a coastal structure. The method derives a multiple linear regression equation between wave run-up scale and offshore wind and wave parameters using long-term photographical observation of wave run-up and offshore wave forecasting model results. The derived regression equation then can be used for forecasting the run-up scale using the offshore wave forecasting model results. To test the implementation of the method, wave run-up scales were observed at four breakwaters in the East Coast of Korea for 9 consecutive months in 2008. The data for the first 6 months were used to derive multiple linear regression equations, which were then validated using the run-up scale data for the remaining 3 months and the corresponding offshore wave forecasting model results. A comparison with an engineering formula for wave run-up is also made. It is found that this method can be used for daily forecast and warning of wave run-up on a coastal structure with reasonable accuracy.     

Artificial neural network to translate offshore satellite wave data to coastal locations

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.     

Derivation of coastal wind and wave parameters from offshore measurements of TOPEX satellite using ANN

The paper discusses an artificial neural network (ANN) approach to project information on wind speed and waves collected by the TOPEX satellite at deeper locations to a specified coastal site. The observations of significant wave heights, average wave period and wind speed at a number of locations over a satellite track parallel to a coastline are used to estimate corresponding values of these three parameters at the coastal site of interest. A combined network involving an input and output of all the three parameters, viz., wave height, period and wind speed instead of separate networks for each one of these variables was found to be necessary in order to train the network with sufficient flexibility. It was also found that network training based on statistical homogeneity of data sets is essential to obtain accurate results. The problem of modeling wind speeds that are always associated with very high variations in their magnitudes was tackled in this study by imparting training in an innovated manner.     

Modeling of tropical cyclone winds and waves for emergency management

This paper compares three commonly used parametric models of tropical cyclone winds and evaluates their application in the wave model WAM. The parametric models provide surface wind fields based on best tracks of tropical cyclones and WAM simulates wave growth based on the wind energy input. The model package is applied to hindcast the wind and wave conditions of Hurricane Iniki, which directly hit the Hawaiian Island of Kauai in 1992. The parametric wind fields are evaluated against buoy and aircraft measurements made during the storm. A sensitivity analysis determines the spatial and spectral resolution needed to model the wave field of Hurricane Iniki. Comparisons of the modeled waves with buoy measurements indicate good agreement within the core of the storm and demonstrate the capability of the model package as a forecasting tool for emergency management.     

Analysis of wave transmission behind low-crested breakwaters using neural networks

This paper aims at improving the prediction of wave transmission behind low-crested breakwaters by means of a numerical model based on Artificial Neural Networks (ANNs). The data here used are those gathered within the European research project DELOS.Firstly, the motivations that lead to employ an ANN numerical model to forecast the wave transmission behind low-crested structures are discussed. Then, the ANN model is tested and its architecture is optimized with a test targeted on assessing both the accuracy and the robustness of the method. A study is devoted to investigate the ANN model capability in reproducing some physical relationships among the involved parameters. Finally, comparisons of ANN results with those from experimental formulations based on the classic regression approach demonstrate a considerable improvement in the forecast accuracy.The ANN forecasting tool is available as a user-friendly Internet applet at: http://w3.uniroma1.it/cmar/wave_transm_kt.htm.     

基于多层感知器的风涌浪分离方法

风涌浪分离是研究风浪、涌浪各自特性的基础,但受限于海浪谱数据的匮乏,基于海浪谱的风涌浪分离方法难以普及应用,有效的解决办法是采用波浪观测中容易获取的基本波要素进行风涌浪分离。现有方法无法利用基本波要素全面计算出风浪、涌浪的比例及其特征参数,为此本文将机器学习引入到风涌浪分离中,以多层感知器模型为基础,提出了一种利用基本波要素、风要素准确计算出风涌浪参数的方法。该方法需要每个测站提供至少466笔、建议766笔及以上的实测波浪数据作为训练样本,适用于台湾海峡3个测站,在计算精度上显著优于基于海浪频谱的传统风涌浪分离方法,可为本海域缺乏海浪谱的测站提供替代性的风涌浪计算方案,有助于扩大实测风涌浪资料的来源,进而加强风涌浪分布特性以及预警预报研究。     

Estimation of scour around submarine pipelines with Artificial Neural Network

The process of scour around submarine pipelines laid on mobile beds is complicated due to physical processes arising from the triple interaction of waves/currents, beds and pipelines. This paper presents Artificial Neural Network (ANN) models for predicting the scour depth beneath submarine pipelines for different storm conditions. The storm conditions are considered for both regular and irregular wave attacks. The developed models use the Feed Forward Back Propagation (FFBP) Artificial Neural Network (ANN) technique. The training, validation and testing data are selected from appropriate experimental data collected in this study. Various estimation models were developed using both deep water wave parameters and local wave parameters. Alternative ANN models with different inputs and neuron numbers were evaluated by determining the best models using a trial and error approach. The estimation results show good agreement with measurements.     

Down-scaled regional ocean modeling system (ROMS) for high-resolution coastal hydrodynamics in Korea

A down-scaled operational oceanographic system is developed for the coastal waters of Korea using a regional ocean modeling system(ROMS).The operational oceanographic modeling system consists of atmospheric and hydrodynamic models.The hydrodynamic model,ROMS,is coupled with wave,sediment transport,and water quality modules.The system forecasts the predicted results twice a day on a 72 h basis,including sea surface elevation,currents,temperature,salinity,storm surge height,and wave information for the coastal waters of Korea.The predicted results are exported to the web-GIS-based coastal information system for real-time dissemination to the public and validation with real-time monitoring data using visualization technologies.The ROMS is two-way coupled with a simulating waves nearshore model,SWAN,for the hydrodynamics and waves,nested with the meteorological model,WRF,for the atmospheric surface forcing,and externally nested with the eutrophication model,CE-QUAL-ICM,for the water quality.The operational model,ROMS,was calibrated with the tidal surface observed with a tide-gage and verified with current data observed by bottom-mounted ADCP or AWAC near the coastal waters of Korea.To validate the predicted results,we used real-time monitoring data derived from remote buoy system,HF-radar,and geostationary ocean color imager(GOCI).This down-scaled operational coastal forecasting system will be used as a part of the Korea operational oceanographic system(KOOS) with other operational oceanographic systems.     

Calibration and verification of a spectral wind–wave model for Lake Okeechobee

A spectral wind wave model SWAN (Simulation WAves Nearshore) that represents the generation, propagation and dissipation of waves was applied to Lake Okeechobee. This model includes the effects of refraction, shoaling, and blocking in wave propagation. It accounts for wave dissipation by whitecapping, bottom friction, and depth-induced wave breaking. The wave–wave interaction effect also is included in this model. Measurements of wind and wave heights were made at different stations and different time periods in Lake Okeechobee. Significant wave height values were computed from the recorded data. The correlation between wind stress and significant wave height also was analyzed. A 6-day simulation using 1989 data was conducted for model calibration. Another 6-day simulation using 1996 data was conducted for model verification. The simulated significant wave heights were found to agree reasonably well with measured significant wave heights for calibration and verification periods. Agreement between observed and simulated values was based on graphical comparisons, mean, absolute and root mean square errors, and correlation coefficient. Comparisons showed that the model reproduced both general observed trends and short term fluctuations.     

An evaluation of model estimates of ocean wave directions

The potential accuracy of local models is investigated to determine the mean direction of waves from the time history of locally observed significant wave height (or peak frequency) and locally observed wind. This is done by comparing results of such models with observations at a location in the southern North Sea for a period of six weeks. The model results are also compared with results of two synoptic models which require large scale wind information to estimate the local mean wave direction.For significant wave heights larger than 1.5 m the rms-error of the estimated mean wave direction was about 30° for the best performing local model and about 15° for the best performing synoptic model.     

Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea

In multi-resolution analysis (MRA) by wavelet function Daubechies (db), we decompose the signal in two parts, the low and high-frequency contents. We remove the high-frequency content and reconstruct a new “de-noise” signal by using inverse wavelet transform. The calculation of tidal constituent phase-lags was made to determine the input and output data patterns used in building network structure of Artificial Neuron-Network (ANN) model. The “de-noise” signal was, then, used as the input data to improve the forecasting accuracy of the ANN model. The wavelet spectrum, conventional energy spectrum (fast Fourier transform, FFT), and harmonic analysis were used to analyze the characteristics of tidal data.Using only a very short-period data as a training data set in Artificial Neuron-Network Back-Propagate (ANN-BP) model, the developed ANN+Wavelet model can accurately predict or supply the missing tide data for a long period (1–5 years). The results also show that the concept of tidal constituent phase-lags can improve ANN model of tidal forecasting and data supplement. The addition of the wavelet analysis to ANN method can prominently improve the prediction quality.     

Soft computing approach for real-time estimation of missing wave heights

This paper presents soft computing approach for estimation of missing wave heights at a particular location on a real-time basis using wave heights at other locations. Six such buoy networks are developed in Eastern Gulf of Mexico using soft computing techniques of Artificial Neural Networks (ANN) and Genetic Programming (GP). Wave heights at five stations are used to estimate wave height at the sixth station. Though ANN is now an established tool in time series analysis, use of GP in the field of time series forecasting/analysis particularly in the area of Ocean Engineering is relatively new and needs to be explored further. Both ANN and GP approach perform well in terms of accuracy of estimation as evident from values of various statistical parameters employed. The GP models work better in case of extreme events. Results of both approaches are also compared with the performance of large-scale continuous wave modeling/forecasting system WAVEWATCH III. The models are also applied on real time basis for 3 months in the year 2007. A software is developed using evolved GP codes (C++) as back end with Visual Basic as the Front End tool for real-time application of wave estimation model.