Multi-Step-Ahead Forecasting of Wave Conditions Based on a Physics-Based Machine Learning (PBML) Model for Marine Operations
Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., significant wave height <i>H<sub>s</su...
Main Authors: | Mengning Wu, Christos Stefanakos, Zhen Gao |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-12-01
|
Series: | Journal of Marine Science and Engineering |
Subjects: | |
Online Access: | https://www.mdpi.com/2077-1312/8/12/992 |
Similar Items
-
Uncertainty Quantification in Machine Learning Modeling for Multi-Step Time Series Forecasting: Example of Recurrent Neural Networks in Discharge Simulations
by: Tianyu Song, et al.
Published: (2020-03-01) -
Machine learning strategies for multi-step-ahead time series forecasting
by: Ben Taieb, Souhaib
Published: (2014) -
The Quixotic Task of Forecasting Peaks of COVID-19: Rather Focus on Forward and Backward Projections
by: Ruy Freitas Reis, et al.
Published: (2021-03-01) -
Impacts of an Altimetric Wave Data Assimilation Scheme and Currents-Wave Coupling in an Operational Wave System: The New Copernicus Marine IBI Wave Forecast Service
by: Aouf, L., et al.
Published: (2022) -
Factor-Based Framework for Multivariate and Multi-step-ahead Forecasting of Large Scale Time Series
by: Jacopo De Stefani, et al.
Published: (2021-09-01)