Interpolation of wave heights

被引:27
作者
Deo, MC [1 ]
Kumar, NK [1 ]
机构
[1] Indian Inst Technol, Dept Civil Engn, Bombay 400076, Maharashtra, India
关键词
wave heights; neural networks; satellite data; ocean waves; disaggregation models;
D O I
10.1016/S0029-8018(99)00023-2
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
Remote sensing of waves often necessitates presentation of data in the form of wave height values grouped over large time intervals. This restricts their use to long-term applications only. This paper describes how such data can be made suitable for short-term usage in the field. Weekly mean significant wave heights were derived from their monthly mean observations with the help of different alternative techniques. These include model-free neural network schemes as well as model-based statistical and numerical methods. Superiority of neural networks was noted when the estimations were compared with corresponding observations. The network was trained using three different training algorithms, viz., error back propagation, conjugate gradient and cascade correlation. The technique of cascade correlation took minimum training time and showed better coefficient of correlation between observations and network output. (C) 2000 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:907 / 919
页数:13
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