کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4383710 1617847 2015 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting of Time Series Significant Wave Height Using Wavelet Decomposed Neural Network
ترجمه فارسی عنوان
پیش بینی ارتفاع موج قابل توجهی در طول موج با استفاده از شبکه عصبی تجزیه شده موج
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک علوم آبزیان
چکیده انگلیسی

In this current study, a hybrid model of wavelet and Artificial Neural Network (WLNN) has been developed to forecast time series significant wave height for lead times up to 48 h. The data used in the hybrid model are significant wave heights (Hs) belongs to two stations, one near to New Mangalore port, Indian ocean and another near to west of Eureka, Canada in North Pacific ocean. The three hourly significant wave height data for a period of one year was first decomposed through discrete wavelet transformation in order to obtain frequencies of different bands in the form of wavelet coefficients. Later these coefficients are used as inputs into Levenberg Marquardt artificial neural network models to forecast time series significant wave heights at multistep lead time. Two different methods WLNN-1 &WLNN-2 employed for the first station data to forecast significant wave heights at higher lead times. From the result it is found that the second method (WLNN-2) in wavelet-ANN model performed better than first method (WLNN-1).Model results obtained for two stations showed good predictions at lower lead times but slight deviation observed at higher lead times. As compared to first station results, the second station results are slightly poor because of more statistical variations in the data set.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Aquatic Procedia - Volume 4, 2015, Pages 540-547