Autor: Paplińska, B. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Specific features of sea waves in the Pomeranian Bay
Autorzy:: Paplińska, B.
Powiązania:: https://bibliotekanauki.pl/articles/241037.pdf
Data publikacji:: 2001
Wydawca:: Polska Akademia Nauk. Instytut Budownictwa Wodnego PAN
Tematy:: sea waves
Pomeranian Bay
numerical models
wave measurements
Opis:: The paper concerns investigations of regional variability of the wave climate in the Pomeranian Bay. Analysis is based on the wave measurements taken during a period of four months and numerical wave model WAM4 results. The calculations covered period of 3 months in 1997 (Oct., Nov., Dec.) and two years: 1998,1999. Analysis of wave modelling results shows that wave fields are not homogeneous in the whole area. The gradual increase (up to 50%) of yearly mean values of selected wave parameters can be observed going from the south-westerly of the Bay to north-easterly direction. Directional distribution of significant wave height is determined by wind climate and shape of the basin and prevails in easterly, south-easterly, south-westerly directions.
Źródło:: Archives of Hydro-Engineering and Environmental Mechanics; 2001, 48, 2; 55-72
1231-3726
Pojawia się w:: Archives of Hydro-Engineering and Environmental Mechanics
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Application of neural networks to the prediction of significant wave height at selected locations on the Baltic Sea
Autorzy:: Paplińska-Swerpel, B.
Powiązania:: https://bibliotekanauki.pl/articles/240964.pdf
Data publikacji:: 2006
Wydawca:: Polska Akademia Nauk. Instytut Budownictwa Wodnego PAN
Tematy:: sea wave forecast
neural networks
sea waves
Baltic Sea
Opis:: This paper describes the application of methodology based on the artificial neural network technique to make short-term wave forecasts. The neural network model is used to predict significant wave height at a selected location on the Baltic Sea based on wave and/or wind data at ten points scattered on the sea. High quality hindcast data were used in the process of developing the forecast methodology. The data originated from the WAM4 wave model. The results show that the neural network technique allowed significant wave height to be predicted accurately. The agreement obtained by a comparison with a testing data set was sufficiently good to confirm the effectiveness of this approach.
Źródło:: Archives of Hydro-Engineering and Environmental Mechanics; 2006, 53, 3; 183-201
1231-3726
Pojawia się w:: Archives of Hydro-Engineering and Environmental Mechanics
Dostawca treści:: Biblioteka Nauki

Artykuł

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