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    Wyświetlanie 1-3 z 3
    Tytuł:
    Local Wave Energy Dissipation and Morphological Beach Characteristics along a Northernmost Segment of the Polish Coast
    Autorzy:
    Różyński, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/241376.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Instytut Budownictwa Wodnego PAN
    Tematy:
    coastal morphology
    wave energy dissipation
    equilibrium profiles
    data-driven modelling
    signal processing
    Opis:
    This paper analyses cross-shore bathymetric profiles between Władysławowo (km 125 of the Polish coastal chainage) and Lake Sarbsko (km 174) done in 2005 and 2011. Spaced every 500 m, they cover beach topography from dune/cliff crests to a seabed depth of about 15 m. They were decomposed by signal processing techniques to extract the monotonic component of beach topography and to perform a straightforward assessment of wave energy dissipation rates. Three characteristic dissipation patterns were identified: one associated with large nearshore bars and 2–3 zones of wave breaking; a second, to which the equilibrium beach profile concept can be applied; and a third, characterized by mixed behaviour. An attempt was then made to interpret these types of wave energy dissipation in terms of local coastal morphological features and the underlying sedimentary characteristics.
    Źródło:
    Archives of Hydro-Engineering and Environmental Mechanics; 2018, 65, 2; 91-108
    1231-3726
    Pojawia się w:
    Archives of Hydro-Engineering and Environmental Mechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Data-driven discharge analysis: a case study for the Wernersbach catchment, Germany
    Autorzy:
    Popat, Eklavyya
    Kuleshov, Alexey
    Kronenberg, Rico
    Bernhofer, Christian
    Powiązania:
    https://bibliotekanauki.pl/articles/108441.pdf
    Data publikacji:
    2020
    Wydawca:
    Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
    Tematy:
    artificial neural networks
    data-driven modelling
    event-based coefficient of rainfall-runoff
    precipitation
    multi-correlation analysis
    soil moisture content
    Opis:
    This study focuses on precipitationdischarge data-driven models, with regression analysis between the weighted maximum rainfall and maximum discharge of flood events. It is also the first of its kind investigation for the Wernersbach catchment, which incorporates data-driven models in order to evaluate the suitability of the model in simulating the discharge from the catchment and provide good insights for future studies. The input parameters are hydrological and climate data collected from 2001 to 2009, including precipitation, rainfall-runoff and soil moisture. The statistical regression and artificial neural network models used are based on a data-driven multiple linear regression technique, and the same input parameters are applied for validation and calibration. The artificial neural network model has one hidden layer with a sigmoidal activation function and uses a linear activation function in the output layer. The artificial neural network is observed to model 0.7% and 0.5% of values, with and without extreme values respectively. With less than 1% error, the artificial neural network is observed to predict extreme events better compared to the conventional statistical regression model and is also better suited to the tasks of rainfall-runoff and flood forecasting. It is presumed that in the future this study’s conclusions would form the basis for more complex and detailed studies for the same catchment area.
    Źródło:
    Meteorology Hydrology and Water Management. Research and Operational Applications; 2020, 8, 1; 54-62
    2299-3835
    2353-5652
    Pojawia się w:
    Meteorology Hydrology and Water Management. Research and Operational Applications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Data-driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine
    Autorzy:
    Liu, Shida
    Ji, Honghai
    Hou, Zhongsheng
    Zuo, Jiashuo
    Fan, Lingling
    Powiązania:
    https://bibliotekanauki.pl/articles/1838200.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    data-driven modelling
    UGI gasification process
    relevance vector machine
    modified lazy learning
    modelowanie oparte na danych
    proces gazyfikacji
    maszyna wektorów istotnych
    Opis:
    A modified lazy learning algorithm combined with a relevance vector machine (MLL-RVM) is presented to address a data-driven modelling problem for a gasification process inside a united gas improvement (UGI) gasifier. During the UGI gasification process, the measured online temperature of the produced crude gas is a crucial aspect. However, the gasification process complexities, especially severe changes in the temperature versus infrequent manipulation of the gasifier and the unknown noise in collected data, pose difficulties in dynamics process descriptions via conventional first principles. In the MLL-RVM, a novel weighted neighbour selection method is adopted based on the proposed dynamic cost functions. Moreover, the RVM is utilized in the implementation and design of the proposed online local modelling owing to its short test time and sparseness. Furthermore, the leave-one-out cross-validation technique is used for local model validation, by which the modelling performance is further improved. The MLL-RVM is applied to a series of real data collected from a pragmatic UGI gasifier, and its effectiveness is verified.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 2; 321-335
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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