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Hourly rainfall-runoff modelling by combining the conceptual model with machine learning models in mostly karst Ljubljanica River catchment in Slovenia
ID Sezen, Cenk (Author), ID Šraj, Mojca (Author)

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Abstract
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-runoff modelling in the mostly karst Ljubljanica River catchment, Slovenia. Wavelet-based Extreme Learning Machine (WELM) and Wavelet-based Regression Tree (WRT) machine learning models were integrated into the conceptual CemaNeige Génie Rural à 4 paramètres Horaires (CemaNeige GR4H). In this regard, the performance of the hybrid models was compared with stand-alone conceptual and machine learning models. The stand-alone WELM and WRT models using only meteorological variables performed poorly for hourly runoff forecasting. The CemaNeige GR4H model as stand-alone model yielded good performance; however, it overestimated low flows. The hybrid CemaNeige GR4H-WELM and CemaNeige-WRT models provided better simulation results than the stand-alone models, especially regarding the extreme flows. The results of the study demonstrated that using different variables from the conceptual model, including the snow module, in the machine learning models as input data can significantly affect the performance of rainfall-runoff modelling. The hybrid modelling approach can potentially improve runoff simulation performance in karst catchments with diversified geological formations where the rainfall-runoff process is more complex.

Language:English
Keywords:conceptual model with snow module, hourly data, hybrid modelling, karst, Ljubljanica river catchment, machine learning
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FGG - Faculty of Civil and Geodetic Engineering
Publication status:Published
Publication version:Version of Record
Year:2024
Number of pages:Str. 937–961
Numbering:Vol. 38, iss. 3
PID:20.500.12556/RUL-154832 This link opens in a new window
UDC:556.165
ISSN on article:1436-3240
DOI:10.1007/s00477-023-02607-w This link opens in a new window
COBISS.SI-ID:174114563 This link opens in a new window
Publication date in RUL:05.03.2024
Views:168
Downloads:16
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Record is a part of a journal

Title:Stochastic environmental research and risk assessment
Shortened title:Stoch. environ. res. risk assess.
Publisher:Springer Nature
ISSN:1436-3240
COBISS.SI-ID:512334873 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:konceptualni model s snežnim modulom, urni podatki, hibridno modeliranje, kras, porečje reke Ljubljanice, strojno učenje

Projects

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P2-0180
Name:Vodarstvo in geotehnika: orodja in metode za analize in simulacije procesov ter razvoj tehnologij

Funder:Other - Other funder or multiple funders
Funding programme:UNESCO, IHP, Slovenian national committee
Project number:C3330-20–456010

Funder:Other - Other funder or multiple funders
Funding programme:UNESCO, Chair on Waterrelated Disaster Risk Reduction

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