Development of wave buoy network using soft computing techniques

被引:0
作者
Londhe, Shreenivas N. [1 ]
机构
[1] Vishwakarma Inst Informat Technol, Pune 411048, Maharashtra, India
来源
OCEANS 2008 - MTS/IEEE KOBE TECHNO-OCEAN, VOLS 1-3 | 2008年
关键词
wave buoy; buoy network; soft computing techniques; artificial neural networks; genetic programming;
D O I
暂无
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Wave buoys are perhaps the only reliable source measuring waves continuously for years. This is perhaps the most vital reason for establishment of data buoy programs by various countries like USA (NDBC), Australia, Canada, UK, Germany, India (NDBP) etc. The wave data measurements not only provide real time wave information for Coastal and Ocean related activities but also form wave data base useful for predicting future events using statistical or stochastic techniques. However some times these wave buoys stop functioning either due to malfunctioning instruments or maintenance-related reasons resulting into loss of data. This paper presents use of soft computing techniques like Artificial Neural Networks (ANN) and Genetic Programmin (GP) to retrieve this lost data by forming a network of wave buoys in a region. For developing the buoy network common data of hourly significant wave heights at six buoys in the Gulf of Mexico namely 42001, 42003, 42007, 42036, 42039 and 42040 for the years 2002 and 2004 is used. A separate network for each buoy is developed as the 'target buoy' with other 5 buoys as 'input buoys' which can be operated to retrieve lost data at a location. The testing results of both approaches when compared showed superiority of Genetic Programming over Artificial Neural Network as evident by higher correlation coefficient between observed and predicted wave heights in all cases. The wave height plots also pointed out that GP estimates wave heights in extreme events (peaks) more accurately than ANN.
引用
收藏
页码:90 / 97
页数:8
相关论文
共 18 条
  • [1] The reconstruction of significant wave height time series by using a neural network approach
    Arena, F
    Puca, S
    [J]. JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME, 2004, 126 (03): : 213 - 219
  • [2] BOSE NK, 1998, NEURAL NETWORK FUNDA
  • [3] CHARHATE SB, 2007, INT WORKSH ADV HYDR
  • [4] CHARHATE SB, 2007, J ENG MARIT IN PRESS
  • [5] Hydrological modelling using artificial neural networks
    Dawson, CW
    Wilby, RL
    [J]. PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT, 2001, 25 (01): : 80 - 108
  • [6] Hagan M., 1996, Neural network design
  • [7] Haykin S., 1998, NEURAL NETWORKS COMP
  • [8] Jain P., 2006, Ships and Offshore Structures, V1, P25, DOI 10.1533/saos.2004.0005
  • [9] RBF network for spatial mapping of wave heights
    Kalra, R
    Deo, MC
    Kumar, R
    Agarwal, VK
    [J]. MARINE STRUCTURES, 2005, 18 (03) : 289 - 300
  • [10] Artificial neural network to translate offshore satellite wave data to coastal locations
    Kalra, R
    Deo, MC
    Kumar, R
    Agarwal, VK
    [J]. OCEAN ENGINEERING, 2005, 32 (16) : 1917 - 1932