Design of rubble-mound breakwaters using M5 ′ machine learning method

Applied Ocean Research

Volumn 31, Issue 3, 2009, Pages 197-201

  Etemad Shahidi, Amir ^a   Bonakdar, Lisham ^a  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

Author keywords

Breakwater armor blocks; Data learning; M5 model tree; Soft computing methods; Stability number

Indexed keywords

ARMOR BLOCKS; ARTIFICIAL NEURAL NETWORK; BREAKWATER ARMORS; DEVELOPED MODEL; EMPIRICAL FORMULAS; EXPERIMENTAL TEST; GOVERNING PARAMETERS; INPUT VARIABLES; LEARNING TOOL; MACHINE LEARNING METHODS; MODEL TREES; RUBBLE MOUND BREAKWATERS; SIMPLE RULES; SOFT COMPUTING METHODS; SOFT COMPUTING MODELS; SOFT COMPUTING TOOLS; STABILITY NUMBER; STATISTICAL MEASURES;

ARMOR; BREAKWATERS; COMPUTER SCIENCE; EDUCATION; FUZZY LOGIC; FUZZY NEURAL NETWORKS; MACHINE DESIGN; MILITARY EQUIPMENT; OCEAN ENGINEERING; OCEANOGRAPHY; STABILITY;

SOFT COMPUTING;

ARTIFICIAL NEURAL NETWORK; BREAKWATER; COASTAL ENGINEERING; EMPIRICAL ANALYSIS; FUZZY MATHEMATICS; NUMERICAL MODEL; PREDICTION; REVETMENT; STABILITY ANALYSIS; STATISTICAL ANALYSIS;

EID: 71549141265     PISSN: 01411187     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apor.2009.08.003     Document Type: Article

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