A Machine Learning Framework for Extending Wave Height Time Series Using Historical Wind Records

This study presents a novel machine learning-based framework that utilizes the ConvLSTM-1D model to extend the hindcast of wave height time series by leveraging historical wind records. This approach was applied to Lake Michigan by incorporating wind data from multiple Automatic Surface Observation Systems (ASOS) stations as input features. A wave height time series from the Wave Information System model (WIS) served as the training, validation, and testing dataset for the proposed model. Several models were developed, considering different numbers of wind stations, revealing the importance of incorporating stations with variable distances and orientations to enhance prediction accuracy. Notably, the improvement in the model performance plateaued after a certain number of stations, underscoring the importance of selecting an optimal number of wind stations. Additionally, an ensemble learning technique was employed to combine multiple models, resulting in further enhancements in prediction accuracy. The developed model added 30 years of wave height predictions to the existing time series, expanding it by 70% which allows insights into the long-term wave climatology of the Lake Michigan. This framework offers a promising avenue for utilizing historical wind records worldwide to extend wave height time series, in turn improving coastal resilience and coastal management plans

Improving accuracy on wave height estimation through machine learning techniques

Estimatabion of wave agitation plays a key role in predicting natural disasters, path optimization and secure harbor operation. The Spanish agency Puertos del Estado (PdE) has several oceanographic measure networks equipped with sensors for different physical variables, and manages forecast systems involving numerical models. In recent years, there is a growing interest in wave parameter estimation by using machine learning models due to the large amount of oceanographic data available for training, as well as its proven efficacy in estimating physical variables. In this study, we propose to use machine learning techniques to improve the accuracy of the current forecast system of PdE. We have focused on four physical wave variables: spectral significant height, mean spectral period, peak period and mean direction of origin. Two different machine learning models have been explored: multilayer perceptron and gradient boosting decision trees, as well as ensemble methods that combine both models. These models reduce the error of the predictions of the numerical model by 36% on average, demonstrating the potential gains of combining machine learning and numerical models

Hindcast of Significant Wave Heights in Sheltered Basins Using Machine Learning and the Copernicus Database

Long-term time series of wave parameters play a critical role in coastal structure design and maritime activities. At sites with limited buoy measurements, methods are used to extend the available time series data. To date, wave hindcasting research using machine learning methods has mainly focused on filling in missing buoy measurements or finding a mapping function between two nearshore buoy locations. This work aims to implement machine learning methods for hindcasting wave parameters using only publicly available Copernicus data. Ensemble regression and artificial neural networks were used as machine learning methods and the optimal hyperparameters were determined by the Bayesian optimization algorithm. As inputs, data from the MEDSEA reanalysis wave model were used for the wave parameters and data from the ERA5 atmospheric reanalysis model were used for the wind parameters. The results of this study show that the normalized RMSE of the test data improved by 29% for Rijeka and 12% for Split compared to the original MEDSEA wave hindcast at buoy locations. The proposed method was extremely efficient in removing bias in the original MEDSEA hindcasts (e.g., NBIAS = -0.35 for Rijeka) to negligible values for both Split and Rijeka (NBIAS < 0.03)

A Deep-learning Real-time Bias Correction Method for Significant Wave Height Forecasts in the Western North Pacific

Significant wave height is one of the most important parameters characterizing ocean waves, and accurate numerical ocean wave forecasting is crucial for coastal protection and shipping. However, due to the randomness and nonlinearity of the wind fields that generate ocean waves and the complex interaction between wave and wind fields, current forecasts of numerical ocean waves have biases. In this study, a spatiotemporal deep-learning method was employed to correct gridded SWH forecasts from the ECMWF-IFS. This method was built on the trajectory gated recurrent unit deep neural network,and it conducts real-time rolling correction for the 0-240h SWH forecasts from ECMWF-IFS. The correction model is co-driven by wave and wind fields, providing better results than those based on wave fields alone. A novel pixel-switch loss function was developed. The pixel-switch loss function can dynamically fine-tune the pre-trained correction model, focusing on pixels with large biases in SWH forecasts. According to the seasonal characteristics of SWH, four correction models were constructed separately, for spring, summer, autumn, and winter. The experimental results show that, compared with the original ECMWF SWH predictions, the correction was most effective in spring, when the mean absolute error decreased by 12.972~46.237%. Although winter had the worst performance, the mean absolute error decreased by 13.794~38.953%. The corrected results improved the original ECMWF SWH forecasts under both normal and extreme weather conditions, indicating that our SWH correction model is robust and generalizable.Comment: 21 page

A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems

We develop a method for the evaluation of extreme event statistics associated with nonlinear dynamical systems, using a small number of samples. From an initial dataset of design points, we formulate a sequential strategy that provides the 'next-best' data point (set of parameters) that when evaluated results in improved estimates of the probability density function (pdf) for a scalar quantity of interest. The approach utilizes Gaussian process regression to perform Bayesian inference on the parameter-to-observation map describing the quantity of interest. We then approximate the desired pdf along with uncertainty bounds utilizing the posterior distribution of the inferred map. The 'next-best' design point is sequentially determined through an optimization procedure that selects the point in parameter space that maximally reduces uncertainty between the estimated bounds of the pdf prediction. Since the optimization process utilizes only information from the inferred map it has minimal computational cost. Moreover, the special form of the metric emphasizes the tails of the pdf. The method is practical for systems where the dimensionality of the parameter space is of moderate size, i.e. order O(10). We apply the method to estimate the extreme event statistics for a very high-dimensional system with millions of degrees of freedom: an offshore platform subjected to three-dimensional irregular waves. It is demonstrated that the developed approach can accurately determine the extreme event statistics using limited number of samples

A review on Day-Ahead Solar Energy Prediction

Accurate day-ahead prediction of solar energy plays a vital role in the planning of supply and demand in a power grid system. The previous study shows predictions based on weather forecasts composed of numerical text data. They can reflect temporal factors therefore the data versus the result might not always give the most accurate and precise results. That is why incorporating different methods and techniques which enhance accuracy is an important topic. An in-depth review of current deep learning-based forecasting models for renewable energy is provided in this paper

Estimating the Impact of Weather-Sensitive Cargo Risk on Transport Cost

As a consequence of the global renewable energy transition, there is a rising demand for transportation of project cargo, such as wind turbine components. Transportation of this type of cargo requires special considerations as it is sensitive to adverse weather exposure. This thesis aims to determine what impact weather-sensitive cargo has on transportation cost, formulated as the expected incremental cost compared to vessels transporting "regular" cargo. The chosen methodology approach applies a ship weather routing model to identify the most cost-efficient route from Spain to Houston while accounting for the required weather considerations. The weather routing model comprises one of the literature's most prominent pathfinding algorithms combined with complex machine learning models to achieve realistic cost estimations. Our findings indicate that vessels carrying weather-sensitive deck cargo have a high tendency to deviate from the optimal route selected by vessels carrying regular cargo. This is particularly evident in the winter months, where our findings identify an incremental cost upwards of 13.80%. Conversely, the results reveal an upper limit on the incremental cost of 0.70% in the summer months, indicating a relatively modest disparity from the vessels transporting regular cargo. This asymmetry is found to be largely explained by the seasonal effect of adverse weather. Our findings suggest tha t vessels transporting weather-sensitive deck cargo are at a considerably higher risk during the winter months, where exposure to adverse weather effects is especially prominent.nhhma