Pregled bibliografske jedinice broj: 1181771
Forecasting water level and discharge in the Kupa river basin
Forecasting water level and discharge in the Kupa river basin // Book of Abstracts / Kučera, Zdeněk ; Siwek , Tadeusz ; Chromý, Pavel (ur.).
Prag: Association of Geographical Societies in Europe, 2021. str. 340-340 (predavanje, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 1181771 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Forecasting water level and discharge in the Kupa
river basin
Autori
Katušić, Damjan ; Pripužić, Mirjana ; Pripužić, Krešimir
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
Book of Abstracts
/ Kučera, Zdeněk ; Siwek , Tadeusz ; Chromý, Pavel - Prag : Association of Geographical Societies in Europe, 2021, 340-340
Skup
8th EUGEO International Congress on the Geography of Europe
Mjesto i datum
Prag, Češka Republika, 28.06.2021. - 01.07.2021
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
floods ; forecasting ; water level ; discharge ; Kupa river
Sažetak
Due to the global climate change and corresponding more severe and frequent floods, there is an emergent need for putting in operation early warning systems for floods. Forecasting river water level and discharge is the main component of such a system. In this paper we present our forecasting model, for which we used historical sensor data about water level, discharge, and precipitation from 24 hydrological and 20 meteorological stations within the Kupa river basin in Croatia. In the development of our model, we have evaluated the Vector Auto-Regression (VAR) and LSTM (Long Short-Term Memory) methods as representatives of statistical and machine learning approaches, respectively. The VAR and LSTM methods were evaluated on 4 downstream hydrological stations while forecasting water level and discharge for one to five days in the future. To achieve the best forecasting results we have combined data from all upstream hydrological and meteorological stations. Our evaluation has shown that LSTM is more complex to train and computationally intensive to run than a simple VAR method, but proved better than VAR method in almost all forecasting scenarios for all observed hydrological stations in the Kupa river basin. The only exception was in forecasting scenarios for several days into the future where both methods achieved similar forecasting performance.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Geografija
POVEZANOST RADA
Projekti:
HRZZ-UIP-2017-05-9066 - Učinkovita stvarnovremenska obrada brzih geoprostornih podataka (RETROFIT) (Pripužić, Krešimir, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Sveučilište VERN, Zagreb