Naslov
Development of ANN model for wind speed prediction as a support for early warning system

Autori
Marović, Ivan ; Sušanj, Ivana ; Ožanić, Nevenka

Izvornik
Complexity (1076-2787) 2017 (2017); 3418145, 10

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Decision support ; early warning systems ; prediction models ; wind speed ; ANN

Sažetak
The impact of natural disasters increases every year with more casualties and damages to property and the environment. Therefore, it is important to prevent consequences by implementation of the early warning system (EWS) in order to announce the possibility of the harmful phenomena occurrence. In this paper focus is placed on the implementation of the EWS on the micro location in order to announce possible harmful phenomena occurrence caused by wind. In order to predict such phenomena (wind speed), an artificial neural network (ANN) prediction model is developed. The model is developed on the basis of the input data obtained by local meteorological station on the University of Rijeka campus area in the Republic of Croatia. The prediction model is validated and evaluated by visual and common calculation approaches after which, it was found that it is possible to do very good wind speed prediction for time steps Δt=1h, Δt=3h, and Δt=8h. The developed model is implemented in the EWS as a decision support for improvement of the existing “Procedure plan in a case of the emergency caused by stormy wind or hurricane, snow and occurrence of the ice on the University of Rijeka campus”.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo, Interdisciplinarne tehničke znanosti, Projektni menadžment

Napomena
The research for this paper was conducted within the project “Research Infrastructure for Campus-Based Laboratories at the University of Rijeka, ” which is cofunded by the European Union under the European Regional Development Fund (RC.2.2.06-0001), as well as a part of the scientific project “Water Resources Hydrology and Floods and Mud Flow Risks Identification in the Karstic Area” financed by the University of Rijeka (13.05.1.1.03).

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