prikaz prve stranice dokumenta Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements
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Scientific paper - Original scientific paper
Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements
Journal of Marine Science and Engineering, 9(8) (2021); 786. https://doi.org/10.3390/jmse9080786
Bujak, Damjan; Bogovac, Tonko; Carević, Dalibor; Ilic, Suzana; Lončar, Goran

Institutional repository: Repository of the Faculty of Civil Engineering, University of Zagreb

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Bujak, D., Bogovac, T., Carević, D., Ilic, S. & Lončar, G. (2021). Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements. Journal of Marine Science and Engineering, 9(8).. doi: 10.3390/jmse9080786

Bujak, Damjan, et al. "Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements." Journal of Marine Science and Engineering, vol. 9(8), 2021. https://doi.org/10.3390/jmse9080786

Bujak, Damjan, Tonko Bogovac, Dalibor Carević, Suzana Ilic and Goran Lončar. "Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements." Journal of Marine Science and Engineering 9(8) (2021). https://doi.org/10.3390/jmse9080786

Bujak, D., et al. (2021) 'Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements', Journal of Marine Science and Engineering, 9(8). doi: 10.3390/jmse9080786

Bujak D, Bogovac T, Carević D, Ilic S, Lončar G. Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements. Journal of Marine Science and Engineering [Internet]. 2021 [cited 2024 September 07];9(8). doi: 10.3390/jmse9080786

D. Bujak, T. Bogovac, D. Carević, S. Ilic and G. Lončar, "Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements", Journal of Marine Science and Engineering, vol. 9(8), 2021. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:237:798666. [Accessed: 07 September 2024]

Metadata
Title (english)Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements
AuthorDamjan Bujak
AuthorTonko Bogovac
AuthorDalibor Carević
AuthorSuzana Ilic
AuthorGoran Lončar
Author's institutionUniversity of Zagreb
Faculty of Civil Engineering
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
The volume of material required for the construction of new and expansion of existing beach sites is an important parameter for coastal management. This information may play a crucial role when deciding which beach sites to develop. This work examines whether artificial neural networks (ANNs) can predict the spatial variability of nourishment requirements on the Croatian coast. We use survey data of the nourishment volume requirements and gravel diameter from 2016 to 2020, fetch length, beach area and orientation derived from national maps which vary from location to location due to a complex coastal configuration on the East Adriatic coast, and wind, tide, and rainfall data from nearby meteorological/oceanographic stations to train and test ANNs. The results reported here confirm that an ANN can adequately predict the spatial variability of observed nourishment volumes (R and MSE for the test set equal 0.87 and 2.24 × 104, respectively). The contributions of different parameters to the ANN’s predictive ability were examined. Apart from the most obvious parameters like the beach length and the beach areas, the fetch length proved to be the most important input contribution to ANN’s predictive ability, followed by the beach orientation. Fetch length and beach orientation are parameters governing the wind wave height and direction and hence are proxies for forcing.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleJournal of Marine Science and Engineering
Numberingvol. 9(8), 786
p-ISSN2077-1312
DOIhttps://doi.org/10.3390/jmse9080786
URN:NBNurn:nbn:hr:237:798666
Publication2021
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-09-04 12:10:13