- Tytuł:
- Data-driven discharge analysis: a case study for the Wernersbach catchment, Germany
- Autorzy:
-
Popat, Eklavyya
Kuleshov, Alexey
Kronenberg, Rico
Bernhofer, Christian - Powiązania:
- https://bibliotekanauki.pl/articles/108441.pdf
- Data publikacji:
- 2020
- Wydawca:
- Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
- Tematy:
-
artificial neural networks
data-driven modelling
event-based coefficient of rainfall-runoff
precipitation
multi-correlation analysis
soil moisture content - Opis:
- 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.
- Źródło:
-
Meteorology Hydrology and Water Management. Research and Operational Applications; 2020, 8, 1; 54-62
2299-3835
2353-5652 - Pojawia się w:
- Meteorology Hydrology and Water Management. Research and Operational Applications
- Dostawca treści:
- Biblioteka Nauki
Artykuł