인공신경망 모델을 이용한 항만 다운타임 예측

Title
인공신경망 모델을 이용한 항만 다운타임 예측
Alternative Title
Downtime Forecasting Technique in Harbour Using Artificial Neural Netwroks Model
Author(s)
이진학; 류경호; 정원무; 백원대
KIOST Author(s)
Yi, Jin Hak(이진학)Ryu, Kyong Ho(류경호)Jeong, Weon Mu(정원무)Baek, Won Dae(백원대)
Publication Year
2014-06-18
Abstract
In this study, a downtime forecasting technique for Pohang New Harbour, which is located near the inmost part of pocket-type bay in the east coast of Korea and where downtime due to swell-like waves frequently occur, was developed using the artificial neural networks model as the non-parametric estimation model. For this purpose, a neural networks model was constructed using wide-area wave observational data from ocean wave buoys and observational data from bay entrance and several points in harbour from September 2008 onward. The back-propagation neural networks model, having excellent generalization capability and being widely employed in various engineering problems including weather forecasting, was used as the neural networks model. Wide-area observational data was used as input data, while in-harbour observational data was used as output data. Currently, results are being compared using various neural network structures, and it is expected that the optimum neural networks model would be constructed through this comparative study. Moreover, using such an optimally constructed artificial neural networks model, it is also expected that the forecasting system can be constructed and operated more efficiently. artificial neural networks model as the non-parametric estimation model. For this purpose, a neural networks model was constructed using wide-area wave observational data from ocean wave buoys and observational data from bay entrance and several points in harbour from September 2008 onward. The back-propagation neural networks model, having excellent generalization capability and being widely employed in various engineering problems including weather forecasting, was used as the neural networks model. Wide-area observational data was used as input data, while in-harbour observational data was used as output data. Currently, results are being compared using various neural network structures, and it is expected that the optimum neural networks model would be constructed through this comparative study. Moreover, using such an optimally constructed artificial neural networks model, it is also expected that the forecasting system can be constructed and operated more efficiently.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/26165
Bibliographic Citation
Proceedings of the 34th International Conference on Coastal Engineering (ICCE 2014), pp.1, 2014
Publisher
ASCE
Type
Conference
Language
English
Publisher
ASCE
Related Researcher
Research Interests

Ocean Energy-Tidal Current Energy Converter System,Infrastructure Management-Structural Health Monitoring,Offshore Wind,해양에너지-조류발전시스템,시설물 유지관리-구조건전성 평가,해상풍력

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