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Št. zadetkov: 3
Izvirni znanstveni članek
Oznake: konceptualni model s snežnim modulom;urni podatki;hibridno modeliranje;kras;porečje reke Ljubljanice;strojno učenje;conceptual model with snow module;hourly data;hybrid modelling;karst;Ljubljanica river catchment;machine learning;
Hydrological modelling, essential for water resources management, can be very complex in karst catchments with different climatic and geologic characteristics. In this study, three combined conceptual models incorporating the snow module with machine learning models were used for hourly rainfall-run ...
Leto: 2023 Vir: Fakulteta za gradbeništvo in geodezijo (UL FGG)
Izvirni znanstveni članek
Oznake: konceptualni model;hibridno modeliranje;strojno učenje;kraško porečje;reka Ljubljanica;conceptual model;hybrid modelling;machine learning;Karst catchment;Ljubljanica River;
Hydrological modelling can be complex in nonhomogeneous catchments with diverse geological, climatic, and topographic conditions. In this study, an integrated conceptual model including the snow module with machine learning modelling approaches was implemented for daily rainfall-runoff modelling in ...
Leto: 2024 Vir: Fakulteta za gradbeništvo in geodezijo (UL FGG)
Izvirni znanstveni članek
Oznake: hidrologija;poplave;klimatske spremembe;hidrološko modeliranje;konice pretokov;enovit modeli;sestavljeni dogodki;rain-on-snow floods;climate change;hydrological modelling;peak discharges;lumped model;compound events;
Rain-on-snow (ROS) floods can cause economic damage and endanger human lives due to the compound effect of rainfall and snowmelt, especially under climate change. In this study, possible future changes of seasonality, magnitude and frequency characteristics of ROS floods were investigated for the se ...
Leto: 2020 Vir: Fakulteta za gradbeništvo in geodezijo (UL FGG)
Št. zadetkov: 3
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