Spatial ocean wave height prediction with CNN mixed-data deep neural networks using random field simulated bathymetry
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Spatial ocean wave height prediction with CNN mixed-data deep neural networks using random field simulated bathymetry
Authors
Keywords
-
Journal
OCEAN ENGINEERING
Volume 271, Issue -, Pages 113699
Publisher
Elsevier BV
Online
2023-02-09
DOI
10.1016/j.oceaneng.2023.113699
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Using machine learning to derive spatial wave data: A case study for a marine energy site
- (2021) Jiaxin Chen et al. ENVIRONMENTAL MODELLING & SOFTWARE
- Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks
- (2021) Christoph Jörges et al. OCEAN ENGINEERING
- Predicting Lake Erie wave heights and periods using XGBoost and LSTM
- (2021) Haoguo Hu et al. OCEAN MODELLING
- ConvLSTM-Based Wave Forecasts in the South and East China Seas
- (2021) Shuyi Zhou et al. Frontiers in Marine Science
- Development of a 2-D deep learning regional wave field forecast model based on convolutional neural network and the application in South China Sea
- (2021) Gen Bai et al. APPLIED OCEAN RESEARCH
- A convolutional neural network based model to predict nearshore waves and hydrodynamics
- (2021) Zhangping Wei et al. COASTAL ENGINEERING
- A survey of the recent architectures of deep convolutional neural networks
- (2020) Asifullah Khan et al. ARTIFICIAL INTELLIGENCE REVIEW
- Real-time significant wave height estimation from raw ocean images based on 2D and 3D deep neural networks
- (2020) Heejeong Choi et al. OCEAN ENGINEERING
- Forecasting, hindcasting and feature selection of ocean waves via recurrent and sequence-to-sequence networks
- (2020) Mohammad Pirhooshyaran et al. OCEAN ENGINEERING
- A novel model to predict significant wave height based on long short-term memory network
- (2020) Shuntao Fan et al. OCEAN ENGINEERING
- Quantification of morphodynamic variability and sea state damping of plates at the nearshore area in the East Frisian North Sea
- (2020) Christoph Jörges et al. COASTAL ENGINEERING
- A multi-layer perceptron approach for accelerated wave forecasting in Lake Michigan
- (2020) Xi Feng et al. OCEAN ENGINEERING
- Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition
- (2019) Mumtaz Ali et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor
- (2019) Diego Cabrera et al. NEUROCOMPUTING
- Deterministic wave prediction for unidirectional sea-states in real-time using Artificial Neural Network
- (2019) Y.Z. Law et al. OCEAN ENGINEERING
- A machine learning framework to forecast wave conditions
- (2018) Scott C. James et al. COASTAL ENGINEERING
- An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts
- (2018) Fearghal O’Donncha et al. JOURNAL OF MARINE SYSTEMS
- Bayesian optimization of a hybrid system for robust ocean wave features prediction
- (2018) L. Cornejo-Bueno et al. NEUROCOMPUTING
- Estimation of the significant wave height in the nearshore using prediction equations based on the Response Surface Method
- (2018) Zilong Ti et al. OCEAN ENGINEERING
- Climate change and safe design of ship structures
- (2018) Elzbieta M. Bitner-Gregersen et al. OCEAN ENGINEERING
- Long-term statistics of potentially hazardous sea states in the North Sea 1958–2014
- (2018) Tobias Teich et al. OCEAN DYNAMICS
- Applying dynamically updated nearshore bathymetry estimates to operational nearshore wave modeling
- (2018) A. Spicer Bak et al. COASTAL ENGINEERING
- Regional ocean wave height prediction using sequential learning neural networks
- (2017) N. Krishna kumar et al. OCEAN ENGINEERING
- Prediction of extreme wave heights using neuro wavelet technique
- (2016) Pradnya Dixit et al. APPLIED OCEAN RESEARCH
- Massive missing data reconstruction in ocean buoys with evolutionary product unit neural networks
- (2016) A.M. Durán-Rosal et al. OCEAN ENGINEERING
- Modeling depth-induced wave breaking over complex coastal bathymetries
- (2015) J.E. Salmon et al. COASTAL ENGINEERING
- Significant wave height record extension by neural networks and reanalysis wind data
- (2015) D.J. Peres et al. OCEAN MODELLING
- Scaling depth-induced wave-breaking in two-dimensional spectral wave models
- (2015) J.E. Salmon et al. OCEAN MODELLING
- Multi-model climate projections of ocean surface variables under different climate scenarios—Future change of waves, sea level and wind
- (2013) Nobuhito Mori et al. OCEAN ENGINEERING
- Morphodynamics of the Wadden Sea and its barrier island system
- (2012) Z.B. Wang et al. OCEAN & COASTAL MANAGEMENT
- Wave height prediction using the rough set theory
- (2012) Armaghan Abed-Elmdoust et al. OCEAN ENGINEERING
- Anthropogenic influences on shoreline and nearshore evolution in the San Francisco Bay coastal system
- (2011) Kate L. Dallas et al. ESTUARINE COASTAL AND SHELF SCIENCE
- Changing North Sea storm surge climate: An increasing hazard?
- (2011) Ralf Weisse et al. OCEAN & COASTAL MANAGEMENT
- Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction
- (2011) Iman Malekmohamadi et al. OCEAN ENGINEERING
- Hindcasting of wave parameters using different soft computing methods
- (2008) J. Mahjoobi et al. APPLIED OCEAN RESEARCH
- Climate change impact on extreme wave conditions in the North Sea: an ensemble study
- (2008) Iris Grabemann et al. OCEAN DYNAMICS
- Soft computing approach for real-time estimation of missing wave heights
- (2008) S.N. Londhe OCEAN ENGINEERING
- Learning from data for wind–wave forecasting
- (2008) Ahmadreza Zamani et al. OCEAN ENGINEERING
- Wave hindcasting by coupling numerical model and artificial neural networks
- (2007) I. Malekmohamadi et al. OCEAN ENGINEERING
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started