4.7 Article

Enhancing the accuracy of metocean hindcasts with machine learning models

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Ocean

Learning wave fields evolution in North West Pacific with deep neural networks

Zhiyi Gao et al.

Summary: In this paper, we establish a fast prediction model DFF-ConvLSTM based on deep learning, which can provide high resolution wave prediction on a basin-scale. The prediction parameters include significant wave height, mean period, and average wavelength. The model's 5-day wave height predictions are highly similar to the numerical results, with a root mean square error of 0.439 m, and the calculation time is just 2.7s.

APPLIED OCEAN RESEARCH (2023)

Article Engineering, Ocean

Development of a 2-D deep learning regional wave field forecast model based on convolutional neural network and the application in South China Sea

Gen Bai et al.

Summary: Currently, most methods of wave prediction based on deep learning theory focus on single-point prediction, but 2-D wave field prediction can provide a better understanding of the overall wave situation in a certain area. In this study, a 2-D deep learning regional wave field forecast model using convolutional neural network (CNN) was proposed to forecast the significant wave height (SWH) in the South China Sea. The model achieved accurate predictions of wave height changes along the timeline and provided good estimation of spatial wave height distribution in the 2-D wave field. The mean absolute percentage errors for different lead time periods demonstrated the model's ability to perform long-term forecasts.

APPLIED OCEAN RESEARCH (2022)

Article Engineering, Marine

Spatial wave assimilation by integration of artificial neural network and numerical wave model

Ye Htet Oo et al.

Summary: This study developed a spatial wave assimilation algorithm using artificial neural network to improve the accuracy of wave information in nearshore regions, and the technique developed showed transferability.

OCEAN ENGINEERING (2022)

Article Engineering, Marine

Numerical study of a CNN-based model for regional wave prediction

Yu Jing et al.

Summary: This paper introduces a CNN-based regional wave prediction model that achieves high-precision wave prediction by constructing the mapping relationship between wind data and wave data. Compared to the traditional SWAN model, this model has higher computational efficiency.

OCEAN ENGINEERING (2022)

Article Engineering, Marine

Wave forecasting within a port using WAVEWATCH III and artificial neural networks

Zhenjun Zheng et al.

Summary: An artificial neural network (ANN) model is developed to estimate wave heights inside a harbor using offshore bulk wave parameters. The model combines with a spectral wave model to achieve fast and accurate forecasting, which is proven to be reliable. The study also evaluates the performance accuracy of the ANN model with different input variables and analyzes the sensitivity of the model to these variables.

OCEAN ENGINEERING (2022)

Article Engineering, Ocean

A regional wind wave prediction surrogate model based on CNN deep learning network

Limin Huang et al.

Summary: This paper proposes a regional wind wave prediction surrogate model based on a convolutional neural network, which improves the prediction accuracy and computational efficiency by using historical wind and wave data.

APPLIED OCEAN RESEARCH (2022)

Article Engineering, Marine

Systematization of short-term forecasts of regional wave heights using a machine learning technique and long-term wave hindcast

Seongho Ahn et al.

Summary: This study presents a novel approach utilizing machine-learning techniques to forecast regional wave climates using long-term wave hindcast data. The study reveals that the model's performance in global wave forecasts is spatially heterogeneous, emphasizing the need for validation of wave forecasting models in different wave sites and sea states. As access to high-resolution regional wave hindcast data improves, the accuracy of the forecasts is expected to increase.

OCEAN ENGINEERING (2022)

Article Engineering, Marine

Phase-resolved wave prediction for short crest wave fields using deep learning

Xuewen Ma et al.

Summary: This study proposes a long short-term memory wave prediction model (LSTM-WP model) based on deep learning, which can accurately predict the surface of short crest waves. The effects of predicted distance and lead steps on the prediction error are also discussed.

OCEAN ENGINEERING (2022)

Article Engineering, Marine

Forecasting wind waves in the US Atlantic Coast using an artificial neural network model: Towards an AI-based storm forecast system

Zhangping Wei

Summary: This study introduces an artificial intelligence model for predicting time-series wind waves on the US Atlantic Coast, using Long Short-Term Memory model to learn patterns from historical data and achieve high accuracy. Short-term forecasts exhibit better performance than long-term forecasts.

OCEAN ENGINEERING (2021)

Article Engineering, Marine

Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests

Ricardo M. Campos et al.

Summary: Ensemble forecasts have better performances at mid-to-long ranges, but random forest models could only select the best wave forecast in the very short range. Evaluation of wind and wave variables on model accuracy was crucial for feature selection and determining the best forecast.

JOURNAL OF MARINE SCIENCE AND ENGINEERING (2021)

Article Biodiversity Conservation

Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques

Zohre Ebrahimi-Khusfi et al.

Summary: This study predicts the number of dusty days around a major source of dust production in southeastern Iran using machine learning models and different feature selection techniques, with SGB-MARS, SGB-RFE, and SGB-Boruta models outperforming others in terms of performance.

ECOLOGICAL INDICATORS (2021)

Article Engineering, Marine

Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks

Christoph Joerges et al.

Summary: This paper aims to develop a machine learning model based on LSTM neural networks for the reconstruction and prediction of nearshore significant wave height. By integrating bathymetric data for the first time, the model's performance was significantly improved, achieving higher accuracy in SWH reconstruction and predictions.

OCEAN ENGINEERING (2021)

Article Environmental Sciences

ConvLSTM-Based Wave Forecasts in the South and East China Seas

Shuyi Zhou et al.

Summary: This paper establishes a 2D SWH prediction model for the South and East China Seas based on the ConvLSTM algorithm, achieving high accuracy and efficiency in wave forecasting under both normal and extreme conditions. The model shows improved performance compared to traditional numerical wave models.

FRONTIERS IN MARINE SCIENCE (2021)

Article Meteorology & Atmospheric Sciences

The ERA5 global reanalysis

Hans Hersbach et al.

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2020)

Article Meteorology & Atmospheric Sciences

Improving NCEP's global-scale wave ensemble averages using neural networks

Ricardo Martins Campos et al.

OCEAN MODELLING (2020)

Article Engineering, Marine

A novel model to predict significant wave height based on long short-term memory network

Shuntao Fan et al.

OCEAN ENGINEERING (2020)

Article Engineering, Marine

Forecasting a water-surface wave train with artificial intelligence- A case study

Hiroshi Kagemoto

OCEAN ENGINEERING (2020)

Article Engineering, Marine

A multi-layer perceptron approach for accelerated wave forecasting in Lake Michigan

Xi Feng et al.

OCEAN ENGINEERING (2020)

Article Computer Science, Artificial Intelligence

Unsupervised online detection and prediction of outliers in streams of sensor data

Niko Reunanen et al.

INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS (2020)

Article Engineering, Ocean

Nonlinear Wave Ensemble Averaging in the Gulf of Mexico Using Neural Networks

Ricardo Martins Campos et al.

JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY (2019)

Article Engineering, Civil

A machine learning framework to forecast wave conditions

Scott C. James et al.

COASTAL ENGINEERING (2018)

Article Computer Science, Artificial Intelligence

Comprehensive study of feature selection methods to solve multicollinearity problem according to evaluation criteria

Alexandr Katrutsa et al.

EXPERT SYSTEMS WITH APPLICATIONS (2017)

Review Computer Science, Artificial Intelligence

Recent advances in feature selection and its applications

Yun Li et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2017)

Article Meteorology & Atmospheric Sciences

Data assimilation with the ensemble Kalman filter in a high-resolution wave forecasting model for coastal areas

Sofia Almeida et al.

JOURNAL OF OPERATIONAL OCEANOGRAPHY (2016)

Article Engineering, Marine

Comparison of HIPOCAS and ERA wind and wave reanalyses in the North Atlantic Ocean

R. M. Campos et al.

OCEAN ENGINEERING (2016)

Article Meteorology & Atmospheric Sciences

Data assimilation of ocean wind waves using Neural Networks. A case study for the German Bight

Kathrin Wahle et al.

OCEAN MODELLING (2015)

Article Meteorology & Atmospheric Sciences

Impact of assimilating altimeter data on wave predictions in the western Iberian coast

Liliana Rusu et al.

OCEAN MODELLING (2015)

Article Meteorology & Atmospheric Sciences

Local data assimilation scheme for wave predictions close to the Portuguese ports

Liliana Rusu et al.

JOURNAL OF OPERATIONAL OCEANOGRAPHY (2014)

Article Green & Sustainable Science & Technology

Wind forecasting using Principal Component Analysis

Christina Skittides et al.

RENEWABLE ENERGY (2014)

Article Engineering, Manufacturing

Principal component analysis for feature extraction and NN pattern recognition in sensor monitoring of chip form during turning

T. Segreto et al.

CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY (2014)

Article Meteorology & Atmospheric Sciences

Problems in RMSE-based wave model validations

L. Mentaschi et al.

OCEAN MODELLING (2013)

Review Computer Science, Hardware & Architecture

A Few Useful Things to Know About Machine Learning

Pedro Domingos

COMMUNICATIONS OF THE ACM (2012)

Article Geosciences, Multidisciplinary

Observational changes and trends in northeast Pacific wave records

Johannes Gemmrich et al.

GEOPHYSICAL RESEARCH LETTERS (2011)

Article Engineering, Multidisciplinary

Non-linear wave data assimilation with an ANN-type wind-wave model and Ensemble Kalman Filter (EnKF)

Ahmadreza Zamani et al.

APPLIED MATHEMATICAL MODELLING (2010)

Article Geosciences, Multidisciplinary

Variability of extreme wave heights in the northeast Pacific Ocean based on buoy measurements

Melisa Menendez et al.

GEOPHYSICAL RESEARCH LETTERS (2008)

Article Engineering, Marine

Ocean wave forecasting using recurrent neural networks

S. Mandal et al.

OCEAN ENGINEERING (2006)

Article Engineering, Marine

Hindcasting of storm waves using neural networks

SB Rao et al.

OCEAN ENGINEERING (2005)

Article Materials Science, Characterization & Testing

A feature extraction technique based on principal component analysis for pulsed Eddy current NDT

A Sophian et al.

NDT & E INTERNATIONAL (2003)

Article Computer Science, Artificial Intelligence

Gene selection for cancer classification using support vector machines

I Guyon et al.

MACHINE LEARNING (2002)