Abstract
Design discharges corresponding to large return periods are generally highly uncertain because of the relatively short data set of measured discharges. The uncertainty in these discharge predictions can be decreased by extending the data set of annual maximum discharges with historic flood events. However, efficient model approaches, in terms of com putational times and model accuracy, are required to reconstruct historic flood events. In this study, the suitability of three data-driven surrogate models are evaluated, namely: a linear regression model, an artificial neural network and a support vector machine. The Rhine river flood event of 1809 is used as a case study. Although all types of surrogate models are capable of reproducing the maximum discharge during the 1809 flood event, the use of an artificial neural network resulted in the smallest 95% uncertainty interval.
| Original language | English |
|---|---|
| Title of host publication | River Flow 2020 |
| Subtitle of host publication | Proceedings of the 10th Conference on Fluvial Hydraulics (Delft, Netherlands, 7-10 July 2020) |
| Editors | Wim Uijttewaal, Mario J. Franca, Daniel Valero, Victor Chavarrias, Claudia Ylla Arbos, Ralph Schielen, Alessandra Crosato |
| Place of Publication | London |
| Publisher | CRC Press/Balkema |
| Pages | 1873-1880 |
| ISBN (Electronic) | 978-1-003-11095-8 |
| ISBN (Print) | 978-0-367-62773-7 |
| Publication status | Published - 2020 |
| Event | 10th Conference on Fluvial Hydraulics, River Flow 2020 - Delft, Netherlands Duration: 7 Jul 2020 → 10 Jul 2020 Conference number: 10 |
Conference
| Conference | 10th Conference on Fluvial Hydraulics, River Flow 2020 |
|---|---|
| Abbreviated title | River Flow |
| Country/Territory | Netherlands |
| City | Delft |
| Period | 7/07/20 → 10/07/20 |
