skip to main content


Title: Freak Wave Forecasting: A Data-Driven Approach
Freak waves, waves significantly higher than neighboring waves, are a serious threat to ships and marine infrastructure. Despite significant refinement of operational wave models and recent progress in studying the theoretical foundations of such extreme events, the emergence of these events remains unpredictable. In this work, the authors propose a data-driven wave forecasting approach by combining the essence of common wave models, rapid oscillations, and slowly changing spectrum with data-driven techniques such as recurrent neural networks. A judicious minimization procedure is developed, wherein the sea surface elevation is first decomposed into harmonic functions with varying amplitudes. Then, the amplitude variations are forecasted by fitting universal, black-box models. This approach, which can be used to forecast wave crests and troughs in real time, is tested on available buoy data. Overall, the developed models and fitting strategies outperform simple benchmarks indicating the approach’s potential for operational, real-time wave forecasting.  more » « less
Award ID(s):
1854532
PAR ID:
10334975
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the ASME 202 2022 41st International Conference on Ocean, Offshore and Arctic Engineering
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Background

    Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox,SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US.

    Results

    This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score.

    Conclusions

    We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

     
    more » « less
  2. Abstract

    This study introduces an ensemble learning model for the prediction of significant wave height and average wave period in stations along the U.S. Atlantic coast. The model utilizes the stacking method, combining three base learner models - Lasso regression, support vector machine, and Multi-layer Perceptron - to achieve more precise and robust predictions. To train and evaluate the models, a twenty-year dataset comprising meteorological and wave data was used, enabling forecasts for significant wave height and average wave period at 1, 3, 6, and 12 hour intervals. The data collection involved two NOAA buoy stations situated on the U.S. Atlantic coast. The findings demonstrate that the ensemble learning model constructed through the stacking method yields significantly higher accuracy in predicting significant wave height within the specified time intervals.

    Moreover, the study investigates the influence of swell waves on forecasting significant wave height and average wave period. Notably, the inclusion of swell waves improves the accuracy of the 12-hour forecast. Consequently, the developed ensemble model effectively estimates both significant wave height and average wave period. The ensemble model outperforms the individual models in forecasting significant wave height and average wave period. This ensemble learning model serves as a viable alternative to conventional coastal models for predicting wave parameters.

     
    more » « less
  3. Recent advances in instruments are transforming our capabilities to better understand, monitor, and model river systems. The present paper illustrates such capabilities by providing new insights into unsteady flows captured with a Horizontal Acoustic Current Profiler (HADCP) integrated at an operational index-velocity gaging station. The illustrations demonstrate that the high-resolution stage and velocity measurements directly acquired during flood wave propagation reveal the intricate interplay among flow variables that are essential for better supporting judicious decision making for river management, flooding, sediment transport, and stream ecology. The paper confirms that the index-velocity method better captures the unsteady flow dynamics in comparison with the stage-discharge monitoring approach. At a time when the intensity and frequency of floods is continuously increasing, a better understanding of the critical features of flood waves during extreme events and the possibility of capturing more accurately their dynamics in real time is of special socio-economic significance. 
    more » « less
  4. The forecasting of significant societal events such as civil unrest and economic crisis is an interesting and challenging problem which requires both timeliness, precision, and comprehensiveness. Significant societal events are influenced and indicated jointly by multiple aspects of a society, including its economics, politics, and culture. Traditional forecasting methods based on a single data source find it hard to cover all these aspects comprehensively, thus limiting model performance. Multi-source event forecasting has proven promising but still suffers from several challenges, including (1) geographical hierarchies in multi-source data features, (2) hierarchical missing values, (3) characterization of structured feature sparsity, and (4) difficulty in model’s online update with incomplete multiple sources. This article proposes a novel feature learning model that concurrently addresses all the above challenges. Specifically, given multi-source data from different geographical levels, we design a new forecasting model by characterizing the lower-level features’ dependence on higher-level features. To handle the correlations amidst structured feature sets and deal with missing values among the coupled features, we propose a novel feature learning model based on an N th-order strong hierarchy and fused-overlapping group Lasso. An efficient algorithm is developed to optimize model parameters and ensure global optima. More importantly, to enable the model update in real time, the online learning algorithm is formulated and active set techniques are leveraged to resolve the crucial challenge when new patterns of missing features appear in real time. Extensive experiments on 10 datasets in different domains demonstrate the effectiveness and efficiency of the proposed models. 
    more » « less
  5. null (Ed.)
    Prompt surveillance and forecasting of COVID-19 spread are of critical importance for slowing down the pandemic and for the success of any public mitigation efforts. However, as with any infectious disease with rapid transmission and high virulence, lack of COVID-19 observations for near-real-time forecasting is still the key challenge obstructing operational disease prediction and control. In this context, we can follow the two approaches to forecasting COVID-19 dynamics: based on mechanistic models and based on machine learning. Mechanistic models are better in capturing an epidemiological curve, using a low amount of data, and describing the overall trajectory of the disease dynamics, hence, providing long-term insights into where the disease might go. Machine learning, in turn, can provide more precise data-driven forecasts especially in the short-term horizons, while suffering from limited interpretability and usually requiring backlog history on the infectious disease. We propose a unified reinforcement learning framework that combines the two approaches. That is, long-term trajectory forecasts are used in machine learning techniques to forecast local variability which is not captured by the mechanistic model. 
    more » « less