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Spatial variability of long-term trends in significant wave height over the Gulf of Gdańsk using System Identification techniques

Badur J., Cieślikiewicz W.

Oceanological and Hydrobiological Studies

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2018

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Vol. 47, No. 2

190--201

EN

The significant wave height field over the Gulf of Gdańsk in the Baltic Sea is simulated back to the late 19th century using selected data-driven System Identification techniques (Takagi-Sugeno-Kang neuro-fuzzy system and non-linear optimization methods) and the NOAA/OAR/ESRL PSD Reanalysis 2 wind fields. Spatial variability of trends in the simulated dataset is briefly presented to show a cumulative “storminess” increase in the open, eastern part of the Gulf of Gdańsk and a decrease in the sheltered, western part of the Gulf.

2

Application of neural networks and support vector machine for significant wave height prediction

Berbić J., Ocvirk E., Carević D., Lončar G.

Oceanologia

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2017

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No. 59 (3)

331--349

EN

For the purposes of planning and operation of maritime activities, information about wave height dynamics is of great importance. In the paper, real-time prediction of significant wave heights for the following 0.5–5.5 h is provided, using information from 3 or more time points. In the first stage, predictions are made by varying the quantity of significant wave heights from previous time points and various ways of using data are discussed. Afterwards, in the best model, according to the criteria of practicality and accuracy, the influence of wind is taken into account. Predictions are made using two machine learning methods – artificial neural networks (ANN) and support vector machine (SVM). The models were built using the built-in functions of software Weka, developed by Waikato University, New Zealand.

3

Sea spray aerosol flux estimation based on long-term variation of wave statistics

Myrhaug D., Wang H., Holmedal L. E.

Oceanologia

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2015

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No. 57 (3)

288--292

EN

This note provides estimates of the mean sea spray aerosol flux based on long-term wave statistics using the whitecap method based on the limiting steepness and threshold vertical acceleration criteria. The aim is to present a procedure demonstrating how global wave statistics can be used to give estimates of the long-term aerosol flux. These estimates are obtained by using bivariate distributions of significant wave height and characteristic wave period, representing open ocean deep water waves in the Northern North Sea and the North Atlantic.

4

Analysis of the influence of waves in the occurrence of accidents in the Portuguese coast using bayesian belief networks

Antao P., Soares C. G.

Journal of KONBiN

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2010

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No. 1 (13)

105-116

EN

Sea and weather conditions are the second most frequent cause of accidents in Portuguese waters accounting for 23% of the occurrences. However due to lack of information in the Portuguese maritime accident database it is difficult to assess what this cause consists specifically, i.e., fog, large wave heights or other events. In the present study significant wave height data was introduced in a Bayesian Belief Network (BBN) model in order assess the correlation between their amplitude and certain accident typologies and related consequences (human injuries or fatalities). The results of different inferences of the BBN model show that through a simple modification of an accident model the influence of weather pattern can be assessed and specific risk factors can be identified.

PL

Stan morza i warunki atmosferyczne to druga z najczęściej występujących przyczyn wypadków na wodach terytorialnych Portugalii, odpowiedzialna za 23% zdarzeń. Ze względu na brak informacji w bazie danych o wypadkach morskich w Portugalii trudno jednak ocenić, co konkretnie stanowi o zaistnieniu wypadku: mgła, duża wysokość fal, czy też inne czynniki. W niniejszej pracy dokonano następującego zabiegu: do modelu opartego na sieci bayesowskiej (Bayesian Belief Network - BBN) wprowadzono dane o znaczącej wysokości fal, a to celem dokonania oceny korelacji między ich amplitudą, typologią wypadków określonego rodzaju, oraz wynikającymi konsekwencjami w postaci odniesionych przez ludzi obrażeń czy też wypadków śmiertelnych. Wyniki wnioskowania na bazie modelu BBN dowodzą, że dzięki prostej modyfikacji modelu wypadku można ocenić wpływ określonych warunków atmosferycznych na zaistnienie wypadku oraz zidentyfikować konkretne czynniki ryzyka.

5

Wave Climate in the Gulf of Gdańsk vs. Open Baltic Sea near Lubiatowo, Poland

Różyński G.

Archives of Hydro-Engineering and Environmental Mechanics

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2010

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Vol. 57, nr 2

167-176

EN

The paper analyses long-term variability in the wave climate near Lubiatowo, ca. 15 km east of Łeba harbor, and in the Gulf of Gdańsk, near the Vistula river mouth. The wave climate was reconstructed for the 1958-2001 period by the German Institute for Coastal Research (GKSS). Using basic statistical tools - empirical mean values of significant wave height, estimation of the number of threshold crossings above a prescribed value of that height and conditional empirical probability density functions of wave approach directions - a comparison of wave height at the two locations was executed. A substantial reduction in wave height inside the Gulf (sheltering effect) was measured. Further, the increased storminess over the winter season was estimated for the open sea location. Finally, the analysis of wave approach direction in the open sea location revealed substantial growth in extreme waves from the western sector. Given the geographic configuration of the Gulf and the combined rise in storminess and evolution in extreme wave direction, it can be inferred that the sheltering effect of the Gulf can vary depending on locations within the Gulf. Identification of sheltering patterns in the Gulf emerges as obvious follow-up research. This study could also prove useful in analyses aiming at integrated management of coastal zones in the Gulf, mainly in the implementation of Coastal Protection Law (Apr. 2003), which postulates maintenance of the 2001 shoreline configuration along the entire Polish coast.