An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia
Authors
Keywords
-
Journal
Remote Sensing
Volume 13, Issue 8, Pages 1456
Publisher
MDPI AG
Online
2021-04-12
DOI
10.3390/rs13081456
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios
- (2021) A. A. Masrur Ahmed et al. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
- Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting
- (2021) Jihong Qu et al. WATER RESOURCES MANAGEMENT
- Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting
- (2019) Lulu Wen et al. ENERGY
- Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach
- (2019) Ramendra Prasad et al. CATENA
- Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms
- (2019) Sujan Ghimire et al. APPLIED ENERGY
- Behavioral Modeling and Linearization of Wideband RF Power Amplifiers Using BiLSTM Networks for 5G Wireless Systems
- (2019) Jinlong Sun et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
- (2018) Guoqian Jiang et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- The optimized deep belief networks with improved logistic Sigmoid units and their application in fault diagnosis for planetary gearboxes of wind turbines
- (2018) Yi Qin et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications
- (2018) Luigi Troiano et al. IEEE Transactions on Industrial Informatics
- Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries
- (2018) Yongzhi Zhang et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities
- (2018) Sujan Ghimire et al. REMOTE SENSING OF ENVIRONMENT
- Empirical Algorithm for Significant Wave Height Retrieval from Wave Mode Data Provided by the Chinese Satellite Gaofen-3
- (2018) He Wang et al. Remote Sensing
- Deep belief network based k-means cluster approach for short-term wind power forecasting
- (2018) Kejun Wang et al. ENERGY
- New approach for solar tracking systems based on computer vision, low cost hardware and deep learning
- (2018) Jose A. Carballo et al. RENEWABLE ENERGY
- Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM
- (2018) Yuansheng Huang et al. Sustainability
- Improved EEMD-based crude oil price forecasting using LSTM networks
- (2018) Yu-Xi Wu et al. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
- Regional ocean wave height prediction using sequential learning neural networks
- (2017) N. Krishna kumar et al. OCEAN ENGINEERING
- LSTM: A Search Space Odyssey
- (2017) Klaus Greff et al. IEEE Transactions on Neural Networks and Learning Systems
- Deep belief network based deterministic and probabilistic wind speed forecasting approach
- (2016) H.Z. Wang et al. APPLIED ENERGY
- Study on network traffic forecast model of SVR optimized by GAFSA
- (2016) Yuan Liu et al. CHAOS SOLITONS & FRACTALS
- A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing
- (2016) Huimin Zhao et al. Entropy
- Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach
- (2016) L. Cornejo-Bueno et al. RENEWABLE ENERGY
- Transfer learning for short-term wind speed prediction with deep neural networks
- (2016) Qinghua Hu et al. RENEWABLE ENERGY
- Ocean wave energy harvesting with a piezoelectric coupled buoy structure
- (2015) Nan Wu et al. APPLIED OCEAN RESEARCH
- Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition
- (2015) Wen-chuan Wang et al. ENVIRONMENTAL RESEARCH
- Short-term forecasting of the wave energy flux: Analogues, random forests, and physics-based models
- (2015) Gabriel Ibarra-Berastegi et al. OCEAN ENGINEERING
- Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting
- (2015) Chun-Yang Zhang et al. IEEE Transactions on Sustainable Energy
- Improved annual rainfall-runoff forecasting using PSO–SVM model based on EEMD
- (2015) Wen-chuan Wang et al. JOURNAL OF HYDROINFORMATICS
- Feature Selection with theBorutaPackage
- (2015) Miron B. Kursa et al. Journal of Statistical Software
- Coastal Vulnerability Assessment for Orissa State, East Coast of India
- (2010) T. Srinivasa Kumar et al. JOURNAL OF COASTAL RESEARCH
- Episodic circulation and exchange in a wave-driven coral reef and lagoon system
- (2010) James L. Hench et al. LIMNOLOGY AND OCEANOGRAPHY
- Fitting of Hydrologic Models: A Close Look at the Nash–Sutcliffe Index
- (2008) Sharad K. Jain et al. JOURNAL OF HYDROLOGIC ENGINEERING
- Application of the EEMD method to rotor fault diagnosis of rotating machinery
- (2008) Yaguo Lei et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started