Data-driven discharge analysis: a case study for the Wernersbach catchment, Germany

• Autorzy: Popat Eklavyya , Kuleshov Alexey , Kronenberg Rico , Bernhofer Christian

Identyfikatory

DOI

10.26491/mhwm/112284

• Języki publikacji: EN

Abstrakty

EN

This study focuses on precipitationdischarge data-driven models, with regression analysis between the weighted maximum rainfall and maximum discharge of flood events. It is also the first of its kind investigation for the Wernersbach catchment, which incorporates data-driven models in order to evaluate the suitability of the model in simulating the discharge from the catchment and provide good insights for future studies. The input parameters are hydrological and climate data collected from 2001 to 2009, including precipitation, rainfall-runoff and soil moisture. The statistical regression and artificial neural network models used are based on a data-driven multiple linear regression technique, and the same input parameters are applied for validation and calibration. The artificial neural network model has one hidden layer with a sigmoidal activation function and uses a linear activation function in the output layer. The artificial neural network is observed to model 0.7% and 0.5% of values, with and without extreme values respectively. With less than 1% error, the artificial neural network is observed to predict extreme events better compared to the conventional statistical regression model and is also better suited to the tasks of rainfall-runoff and flood forecasting. It is presumed that in the future this study’s conclusions would form the basis for more complex and detailed studies for the same catchment area.

Słowa kluczowe

EN

artificial neural networks; data-driven modelling; event-based coefficient of rainfall-runoff; precipitation; multi-correlation analysis; soil moisture content

• Wydawca: Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
• Czasopismo: Meteorology Hydrology and Water Management. Research and Operational Applications
• Rocznik: 2020
• Tom: Vol. 8, Iss. 1
• Strony: 54--62
• Opis fizyczny: Bibliogr. 39 poz., rys., tab.

Twórcy

autor

Popat Eklavyya

• Technische Universität Dresden, Institute of Hydrology and Meteorology, Dresden, Germany Goethe University, Institute of Physical Geography, Frankfurt am Main, Germany

autor

Kuleshov Alexey

• Technische Universität Dresden, Institute of Hydrology and Meteorology, Dresden, Germany

autor

Kronenberg Rico

• Technische Universität Dresden, Institute of Hydrology and Meteorology, Dresden, Germany

autor

Bernhofer Christian

• Technische Universität Dresden, Institute of Hydrology and Meteorology, Dresden, Germany

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