Historic flood reconstructions for a safer future: The use of three types of surrogate models

A. Bomers; S.J.M.H. Hulscher; B. van der Meulen; R.M.J. Schielen

Historic flood reconstructions for a safer future: The use of three types of surrogate models

A. Bomers
, S.J.M.H. Hulscher
, B. van der Meulen
, R.M.J. Schielen

Marine and Fluvial Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Cite this

Bomers, A., Hulscher, S. J. M. H., van der Meulen, B., & Schielen, R. M. J. (2020). Historic flood reconstructions for a safer future: The use of three types of surrogate models. In W. Uijttewaal, M. J. Franca, D. Valero, V. Chavarrias, C. Ylla Arbos, R. Schielen, & A. Crosato (Eds.), River Flow 2020: Proceedings of the 10th Conference on Fluvial Hydraulics (Delft, Netherlands, 7-10 July 2020) (pp. 1873-1880). CRC Press/Balkema.

Bomers, A. ; Hulscher, S.J.M.H. ; van der Meulen, B. et al. / Historic flood reconstructions for a safer future : The use of three types of surrogate models. River Flow 2020: Proceedings of the 10th Conference on Fluvial Hydraulics (Delft, Netherlands, 7-10 July 2020). editor / Wim Uijttewaal ; Mario J. Franca ; Daniel Valero ; Victor Chavarrias ; Claudia Ylla Arbos ; Ralph Schielen ; Alessandra Crosato. London : CRC Press/Balkema, 2020. pp. 1873-1880

@inproceedings{78f619680df74e01b9bb3877a2db6ac9,

title = "Historic flood reconstructions for a safer future: The use of three types of surrogate models",

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. ",

author = "A. Bomers and S.J.M.H. Hulscher and \{van der Meulen\}, B. and R.M.J. Schielen",

year = "2020",

language = "English",

isbn = "978-0-367-62773-7",

pages = "1873--1880",

editor = "Wim Uijttewaal and Franca, \{Mario J.\} and Daniel Valero and Chavarrias, \{Victor \} and \{Ylla Arbos\}, \{Claudia \} and Ralph Schielen and Alessandra Crosato",

booktitle = "River Flow 2020",

publisher = "CRC Press/Balkema",

note = "10th Conference on Fluvial Hydraulics, River Flow 2020, River Flow ; Conference date: 07-07-2020 Through 10-07-2020",

}

Bomers, A , Hulscher, SJMH, van der Meulen, B & Schielen, RMJ 2020, Historic flood reconstructions for a safer future: The use of three types of surrogate models. in W Uijttewaal, MJ Franca, D Valero, V Chavarrias, C Ylla Arbos, R Schielen & A Crosato (eds), River Flow 2020: Proceedings of the 10th Conference on Fluvial Hydraulics (Delft, Netherlands, 7-10 July 2020). CRC Press/Balkema, London, pp. 1873-1880, 10th Conference on Fluvial Hydraulics, River Flow 2020, Delft, Netherlands, 7/07/20.

Historic flood reconstructions for a safer future: The use of three types of surrogate models. / Bomers, A.; Hulscher, S.J.M.H.; van der Meulen, B. et al.
River Flow 2020: Proceedings of the 10th Conference on Fluvial Hydraulics (Delft, Netherlands, 7-10 July 2020). ed. / Wim Uijttewaal; Mario J. Franca; Daniel Valero; Victor Chavarrias; Claudia Ylla Arbos; Ralph Schielen; Alessandra Crosato. London: CRC Press/Balkema, 2020. p. 1873-1880.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Historic flood reconstructions for a safer future

T2 - 10th Conference on Fluvial Hydraulics, River Flow 2020

AU - Bomers, A.

AU - Hulscher, S.J.M.H.

AU - van der Meulen, B.

AU - Schielen, R.M.J.

N1 - Conference code: 10

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

M3 - Conference contribution

SN - 978-0-367-62773-7

SP - 1873

EP - 1880

BT - River Flow 2020

A2 - Uijttewaal, Wim

A2 - Franca, Mario J.

A2 - Valero, Daniel

A2 - Chavarrias, Victor

A2 - Ylla Arbos, Claudia

A2 - Schielen, Ralph

A2 - Crosato, Alessandra

PB - CRC Press/Balkema

CY - London

Y2 - 7 July 2020 through 10 July 2020

ER -

Bomers A , Hulscher SJMH, van der Meulen B, Schielen RMJ. Historic flood reconstructions for a safer future: The use of three types of surrogate models. In Uijttewaal W, Franca MJ, Valero D, Chavarrias V, Ylla Arbos C, Schielen R, Crosato A, editors, River Flow 2020: Proceedings of the 10th Conference on Fluvial Hydraulics (Delft, Netherlands, 7-10 July 2020). London: CRC Press/Balkema. 2020. p. 1873-1880

Historic flood reconstructions for a safer future: The use of three types of surrogate models

Abstract

Conference

UN SDGs

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