Related references
Note: Only part of the references are listed.Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine
Moslem Imani et al.
GLOBAL AND PLANETARY CHANGE (2018)
A Machine Learning Approach to Evaluating the Damage Level of Tooth-Shape Spur Dikes
Xianglong Wei et al.
WATER (2018)
Least Squares Support Vector Mechanics to Predict the Stability Number of Rubble-Mound Breakwaters
Nuray Gedik
WATER (2018)
Real-time forecasting of near-field tsunami waveforms at coastal areas using a regularized extreme learning machine
Iyan E. Mulia et al.
COASTAL ENGINEERING (2016)
Stability assessment of rubble-mound breakwaters using genetic programming
Mehmet Levent Koc et al.
OCEAN ENGINEERING (2016)
Surface roughness prediction by extreme learning machine constructed with abrasive water jet
Zarko Cojbasic et al.
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY (2016)
Particle Swarm Optimization based support vector machine for damage level prediction of non-reshaped berm breakwater
Narayana Harish et al.
APPLIED SOFT COMPUTING (2015)
Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia
Ravinesh C. Deo et al.
ATMOSPHERIC RESEARCH (2015)
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification
Wei Li et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2015)
Extreme Learning Machines: A new approach for prediction of reference evapotranspiration
Shafika Sultan Abdullah et al.
JOURNAL OF HYDROLOGY (2015)
Trends in extreme learning machines: A review
Gao Huang et al.
NEURAL NETWORKS (2015)
A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction
E. Alexandre et al.
OCEAN MODELLING (2015)
Rock toe stability of rubble mound breakwaters
Marcel R. A. van Gent et al.
COASTAL ENGINEERING (2014)
Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis
Cheng Lian et al.
NEURAL COMPUTING & APPLICATIONS (2014)
Artificial neural network based breakwater damage estimation considering tidal level variation
Dong Hyawn Kim et al.
OCEAN ENGINEERING (2014)
Kernel-based extreme learning machine for remote-sensing image classification
Mahesh Pal et al.
REMOTE SENSING LETTERS (2013)
Genetic algorithms based logic-driven fuzzy neural networks for stability assessment of rubble-mound breakwaters
Mehmet Levent Koc et al.
APPLIED OCEAN RESEARCH (2012)
Stability of rubble-mound breakwater using H50 wave height parameter
Amir Etemad-Shahidi et al.
COASTAL ENGINEERING (2012)
Stability number prediction for breakwater armor blocks using Support Vector Regression
Dookie Kim et al.
KSCE JOURNAL OF CIVIL ENGINEERING (2011)
Artificial neural networks based on principal component analysis, fuzzy systems and fuzzy neural networks for preliminary design of rubble mound breakwaters
Can Elmar Balas et al.
APPLIED OCEAN RESEARCH (2010)
Optimization method based extreme learning machine for classification
Guang-Bin Huang et al.
NEUROCOMPUTING (2010)
Design of rubble-mound breakwaters using M5′ machine learning method
Amir Etemad-Shahidi et al.
APPLIED OCEAN RESEARCH (2009)
Fuzzy logic approach to conventional rubble mound structures design
Tarkan Erdik
EXPERT SYSTEMS WITH APPLICATIONS (2009)
A fast pruned-extreme learning machine for classification problem
Hai-Jun Rong et al.
NEUROCOMPUTING (2008)
Application of probabilistic neural network to design breakwater armor blocks
Dookie Kim et al.
OCEAN ENGINEERING (2008)
Extreme learning machine: Theory and applications
Guang-Bin Huang et al.
NEUROCOMPUTING (2006)
Wave height parameter for damage description of rubble-mound breakwaters
C. Vidal et al.
COASTAL ENGINEERING (2006)
Neural network for design and reliability analysis of rubble mound breakwaters
DH Kim et al.
OCEAN ENGINEERING (2005)
Artificial neural networks: fundamentals, computing, design, and application
IA Basheer et al.
JOURNAL OF MICROBIOLOGICAL METHODS (2000)
Share Paper
