Short-term probabilistic prediction of significant wave height using bayesian model averaging: Case study of chabahar port, Iran
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Short-term probabilistic prediction of significant wave height using bayesian model averaging: Case study of chabahar port, Iran
Authors
Keywords
-
Journal
OCEAN ENGINEERING
Volume 272, Issue -, Pages 113887
Publisher
Elsevier BV
Online
2023-02-10
DOI
10.1016/j.oceaneng.2023.113887
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Hybrid systems using residual modeling for sea surface temperature forecasting
- (2022) Paulo S. G. de Mattos Neto et al. Scientific Reports
- Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach
- (2022) Mehdi Jamei et al. APPLIED ENERGY
- Deep Learning of Sea Surface Temperature Patterns to Identify Ocean Extremes
- (2021) J. Xavier Prochaska et al. Remote Sensing
- Deep Learning Based Approach to Classify Saline Particles in Sea Water
- (2021) Mohammed Alshehri et al. Water
- Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models
- (2021) Fabio Di Nunno et al. Atmosphere
- Machine learning methods applied to sea level predictions in the upper part of a tidal estuary
- (2021) Nicolas Guillou et al. OCEANOLOGIA
- Forecasting of Typhoon-Induced Wind-Wave by Using Convolutional Deep Learning on Fused Data of Remote Sensing and Ground Measurements
- (2021) Chih-Chiang Wei et al. SENSORS
- Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning
- (2021) Jun-Whan Lee et al. COASTAL ENGINEERING
- Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia
- (2021) Mumtaz Ali et al. RENEWABLE ENERGY
- Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
- (2021) Kuo Chen et al. Applied Sciences-Basel
- A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting
- (2020) Xinxin He et al. WATER RESOURCES MANAGEMENT
- Improving Coastal Ocean Wave Height Forecasting during Typhoons by using Local Meteorological and Neighboring Wave Data in Support Vector Regression Models
- (2020) ShienTsung Chen et al. Journal of Marine Science and Engineering
- A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions
- (2020) Meysam Alizamir et al. ENERGY
- A Novel Method for Sea Surface Temperature Prediction Based on Deep Learning
- (2020) Xuan Yu et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Forecasting of wave energy in Canary Islands based on Artificial Intelligence
- (2020) Deivis Avila et al. APPLIED OCEAN RESEARCH
- Statistical models for improving significant wave height predictions in offshore operations
- (2020) Stergios Emmanouil 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
- Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow
- (2020) Ming Li et al. Water
- Machine Learning in Tropical Cyclone Forecast Modeling: A Review
- (2020) Rui Chen et al. Atmosphere
- Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model
- (2020) Meysam Alizamir et al. ARTIFICIAL INTELLIGENCE REVIEW
- Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN
- (2020) Salim Heddam et al. JOURNAL OF HYDROLOGY
- Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods
- (2020) Ozgur Kisi et al. NEURAL PROCESSING LETTERS
- A wavelet - Particle swarm optimization - Extreme learning machine hybrid modeling for significant wave height prediction
- (2020) Mosbeh R. Kaloop et al. OCEAN ENGINEERING
- Kernel Extreme Learning Machine: An Efficient Model for Estimating Daily Dew Point Temperature Using Weather Data
- (2020) Meysam Alizamir et al. Water
- A Deep Learning Approach to Spatiotemporal Sea Surface Height Interpolation and Estimation of Deep Currents in Geostrophic Ocean Turbulence
- (2020) Georgy E. Manucharyan et al. Journal of Advances in Modeling Earth Systems
- On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: Case studies of river and lake in USA
- (2020) Meysam Alizamir et al. JOURNAL OF CLEANER PRODUCTION
- Applications of Deep Learning to Ocean Data Inference and Sub-Grid Parameterisation
- (2019) Thomas Bolton et al. Journal of Advances in Modeling Earth Systems
- Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States
- (2019) Ziyao Xu et al. Water
- Probabilistic forecasting of coastal wave height during typhoon warning period using machine learning methods
- (2019) Shien-Tsung Chen JOURNAL OF HYDROINFORMATICS
- 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
- Hydrodynamics of river-channel confluence: toward modeling separation zone using GEP, MARS, M5 Tree and DENFIS techniques
- (2019) Ozgur Kisi et al. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
- Combination of Multiple Data-Driven Models for Long-Term Monthly Runoff Predictions Based on Bayesian Model Averaging
- (2019) Huaping Huang et al. WATER RESOURCES MANAGEMENT
- A random forest model for inflow prediction at wastewater treatment plants
- (2019) Pengxiao Zhou et al. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
- Inter-Comparison of Different Bayesian Model Averaging Modifications in Streamflow Simulation
- (2019) Pedram Darbandsari et al. Water
- Dynamic Streamflow Simulation via Online Gradient-Boosted Regression Tree
- (2019) Heng Zhang et al. JOURNAL OF HYDROLOGIC ENGINEERING
- Combining random forests and physics-based models to forecast the electricity generated by ocean waves: A case study of the Mutriku wave farm
- (2019) Paula Serras et al. OCEAN ENGINEERING
- A Novel Dual Path Gated Recurrent Unit Model for Sea Surface Salinity Prediction
- (2019) Tao Song et al. JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
- An Adaptive Scale Sea Surface Temperature Predicting Method Based on Deep Learning With Attention Mechanism
- (2019) Jiang Xie et al. IEEE Geoscience and Remote Sensing Letters
- Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine
- (2018) Moslem Imani et al. GLOBAL AND PLANETARY CHANGE
- Improving Streamflow Prediction Using Uncertainty Analysis and Bayesian Model Averaging
- (2018) Antonio A. Meira Neto et al. JOURNAL OF HYDROLOGIC ENGINEERING
- Ocean wave height prediction using ensemble of Extreme Learning Machine
- (2018) N. Krishna Kumar et al. NEUROCOMPUTING
- A fuzzy KNN-based model for significant wave height prediction in large lakes
- (2018) Mohammad Reza Nikoo et al. OCEANOLOGIA
- Accounting for model structure, parameter and input forcing uncertainty in flood inundation modeling using Bayesian model averaging
- (2018) Zhu Liu et al. JOURNAL OF HYDROLOGY
- Prediction vs. estimation of dewpoint temperature: assessing GEP, MARS and RF models
- (2018) Jalal Shiri HYDROLOGY RESEARCH
- Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast
- (2017) Chih-Chiang Wei Energies
- Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea
- (2017) Tayeb Sadeghifar et al. MARINE GEODESY
- Application of neural networks and support vector machine for significant wave height prediction
- (2017) Jadran Berbić et al. OCEANOLOGIA
- Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging
- (2016) Gaofeng Zhu et al. HYDROLOGICAL PROCESSES
- A real-time forecast model using artificial neural network for after-runner storm surges on the Tottori coast, Japan
- (2016) Sooyoul Kim et al. OCEAN ENGINEERING
- Significant wave height estimation using SVR algorithms and shadowing information from simulated and real measured X-band radar images of the sea surface
- (2015) S. Salcedo-Sanz et al. OCEAN ENGINEERING
- Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia
- (2012) Sepideh Karimi et al. COMPUTERS & GEOSCIENCES
- CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization
- (2011) Gerhard-Wilhelm Weber et al. INVERSE PROBLEMS IN SCIENCE AND ENGINEERING
- Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction
- (2011) Iman Malekmohamadi et al. OCEAN ENGINEERING
- Wave height forecasting in Dayyer, the Persian Gulf
- (2010) B. Kamranzad et al. OCEAN ENGINEERING
- Prediction of significant wave height using regressive support vector machines
- (2009) J. Mahjoobi et al. OCEAN ENGINEERING
- Ensemble Bayesian model averaging using Markov Chain Monte Carlo sampling
- (2008) Jasper A. Vrugt et al. ENVIRONMENTAL FLUID MECHANICS
- Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data / Performances de régressions par splines multiples et adaptives (MARS) pour la prévision d'écoulement au sein de micro-bassins versants Himalayens d'altitudes intermédiaires avec peu de données
- (2008) V. N. SHARDA et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now