Pregled bibliografske jedinice broj: 821581
Mošćenička Draga Early Warning Systems Development Using Machine Learning
Mošćenička Draga Early Warning Systems Development Using Machine Learning // Landslide and Flood Hazard Assessment / Mihalić Arbanas, Snježana ; Arbanas, Željko (ur.).
Zagreb: Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb ; Faculty of Civil Engineering, University of Rijeka, 2014. str. 117-120 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 821581 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Mošćenička Draga Early Warning Systems Development Using Machine Learning
Autori
Ružić, Igor ; Ožanić, Nevenka ; Benac, Čedomir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Landslide and Flood Hazard Assessment
/ Mihalić Arbanas, Snježana ; Arbanas, Željko - Zagreb : Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb ; Faculty of Civil Engineering, University of Rijeka, 2014, 117-120
ISBN
978-953-6953-43-1
Skup
3rd Workshop of the Croatian-Japanese Project ‘Risk Identification and Land-Use Planning for Disaster Mitigation of Landslides and Floods in Croatia’
Mjesto i datum
Zagreb, Hrvatska, 07.03.2013. - 09.03.2013
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
early warning system; machine learning; flood; Mošćenička Draga
Sažetak
The paper presents a machine learning model for predicting Mošćenička Draga torrential stream water levels and discharges based on meteorological and hydrological data. Possible use of machine learning algorithms for early warning system development is presented as well. One of the most important goals of Croatian – Japanese Project Risk Identification and Land- Use Planning for Disaster Mitigation of Landslides and Floods is development of the Early Warning Systems (EWS). The most important task of the EWS in Mošćenička Draga is identification of the critical torrential discharges and precipitations that can cause floods, early enough to inform the authorities and public. The paper describes an artificial intelligence model that has been developed by integrating meteorological and hydrological data. The WEKA Data Mining Software was used for a model development. Model has been validated on measured hydrological and meteorological data from May 2011 till December 2012. Water levels predicted by the model are in accordance with the measured data. Model validation showed that the mac
Izvorni jezik
Engleski
Znanstvena područja
Geologija, Građevinarstvo
POVEZANOST RADA
Ustanove:
Građevinski fakultet, Rijeka
