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Št. zadetkov: 4
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: hidrologija;konceptualni model;model na podlagi podatkov;večhibridni model;kraško porečje;Ljubljanica;hydrology;conceptual model;data-driven model;multiple hybrid modelling;karst catchment;Ljubljanica river;
Rainfall-runoff modelling in karst catchments is challenging due to complex hydrogeological and climatic conditions. Conventional hydrological models can struggle to simulate the nonlinear dynamics. To address this challenge, this study proposes a multiple hybrid modelling approach to enhance daily ...
Leto: 2026 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)
Izvirni znanstveni članek
Oznake: kraška hidrologija;napovedovanje pretočnosti izvira;strojno učenje;procesni model;taljenje snega;hidrološka spremenljivost;karst hydrology;spring flow forecasting;machine learning;process-based model;snowmelt-driven system;hydrological variability;
Hydrological modeling of karst systems is difficult due to their unique recharge, drainage and discharge behavior, which is often highly dynamic and nonlinear. It becomes even more challenging for elevated karst catchments, where the recharge process is additionally influenced by snow accumulation a ...
Leto: 2026 Vir: Znanstvenoraziskovalni center Slovenske akademije znanosti in umetnosti (ZRC-SAZU)
Št. zadetkov: 4
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