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Identification and representation of state dependent non-linearities in flood forecasting using the DBM methodology

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

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Identification and representation of state dependent non-linearities in flood forecasting using the DBM methodology. / Beven, Keith; Leedal, David; Smith, Paul; Young, Peter.

System Identification, Environmental Modelling and Control Systems Design. ed. / Liuping Wang; Hugues Garnier. London : Springer Verlag, 2012. p. 341-366.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Harvard

Beven, K, Leedal, D, Smith, P & Young, P 2012, Identification and representation of state dependent non-linearities in flood forecasting using the DBM methodology. in L Wang & H Garnier (eds), System Identification, Environmental Modelling and Control Systems Design. Springer Verlag, London, pp. 341-366. https://doi.org/10.1007/978-0-85729-974-1_17

APA

Beven, K., Leedal, D., Smith, P., & Young, P. (2012). Identification and representation of state dependent non-linearities in flood forecasting using the DBM methodology. In L. Wang, & H. Garnier (Eds.), System Identification, Environmental Modelling and Control Systems Design (pp. 341-366). Springer Verlag. https://doi.org/10.1007/978-0-85729-974-1_17

Vancouver

Beven K, Leedal D, Smith P, Young P. Identification and representation of state dependent non-linearities in flood forecasting using the DBM methodology. In Wang L, Garnier H, editors, System Identification, Environmental Modelling and Control Systems Design. London: Springer Verlag. 2012. p. 341-366 https://doi.org/10.1007/978-0-85729-974-1_17

Author

Beven, Keith ; Leedal, David ; Smith, Paul ; Young, Peter. / Identification and representation of state dependent non-linearities in flood forecasting using the DBM methodology. System Identification, Environmental Modelling and Control Systems Design. editor / Liuping Wang ; Hugues Garnier. London : Springer Verlag, 2012. pp. 341-366

Bibtex

@inbook{820d46a6ae594fc7a0386f3e2374a8c2,
title = "Identification and representation of state dependent non-linearities in flood forecasting using the DBM methodology",
abstract = "This paper addresses the issue of identifying a state dependent input nonlinearity in a Data Based Mechanistic (DBM) flood forecasting model based on the data rather than some prior conceptualisation of nonlinearity in the system response. Four forms of nonlinear function are presented. A power law may be useful when the input non-linearity is simple. The Radial Basis Function (RBF) network method is appropriate for systems that exhibit well defined but complex input non-linearities. The Piecewise Cubic Hermite Data Interpolation (PCHIP) method also provides the flexibility to map complex input non-linearity shapes while providing the ability to maintain a natural curve. Overfit to the calibration data is a risk in both RBF and PCHIP methods when a large number of knots are used. The Takagi-Sugeno Fuzzy Inference method, together with interactive tuning, provides an alternative approach that allows human-in-the-loop interaction during the parameter estimation process but is not optimal in any statistical sense. Future work will explore the use of these methods with continuous time transfer functions and optimisation of the nonlinear function at the same time as the transfer function.",
author = "Keith Beven and David Leedal and Paul Smith and Peter Young",
year = "2012",
doi = "10.1007/978-0-85729-974-1_17",
language = "English",
isbn = "978-0-85729-973-4",
pages = "341--366",
editor = "Liuping Wang and Hugues Garnier",
booktitle = "System Identification, Environmental Modelling and Control Systems Design",
publisher = "Springer Verlag",

}

RIS

TY - CHAP

T1 - Identification and representation of state dependent non-linearities in flood forecasting using the DBM methodology

AU - Beven, Keith

AU - Leedal, David

AU - Smith, Paul

AU - Young, Peter

PY - 2012

Y1 - 2012

N2 - This paper addresses the issue of identifying a state dependent input nonlinearity in a Data Based Mechanistic (DBM) flood forecasting model based on the data rather than some prior conceptualisation of nonlinearity in the system response. Four forms of nonlinear function are presented. A power law may be useful when the input non-linearity is simple. The Radial Basis Function (RBF) network method is appropriate for systems that exhibit well defined but complex input non-linearities. The Piecewise Cubic Hermite Data Interpolation (PCHIP) method also provides the flexibility to map complex input non-linearity shapes while providing the ability to maintain a natural curve. Overfit to the calibration data is a risk in both RBF and PCHIP methods when a large number of knots are used. The Takagi-Sugeno Fuzzy Inference method, together with interactive tuning, provides an alternative approach that allows human-in-the-loop interaction during the parameter estimation process but is not optimal in any statistical sense. Future work will explore the use of these methods with continuous time transfer functions and optimisation of the nonlinear function at the same time as the transfer function.

AB - This paper addresses the issue of identifying a state dependent input nonlinearity in a Data Based Mechanistic (DBM) flood forecasting model based on the data rather than some prior conceptualisation of nonlinearity in the system response. Four forms of nonlinear function are presented. A power law may be useful when the input non-linearity is simple. The Radial Basis Function (RBF) network method is appropriate for systems that exhibit well defined but complex input non-linearities. The Piecewise Cubic Hermite Data Interpolation (PCHIP) method also provides the flexibility to map complex input non-linearity shapes while providing the ability to maintain a natural curve. Overfit to the calibration data is a risk in both RBF and PCHIP methods when a large number of knots are used. The Takagi-Sugeno Fuzzy Inference method, together with interactive tuning, provides an alternative approach that allows human-in-the-loop interaction during the parameter estimation process but is not optimal in any statistical sense. Future work will explore the use of these methods with continuous time transfer functions and optimisation of the nonlinear function at the same time as the transfer function.

U2 - 10.1007/978-0-85729-974-1_17

DO - 10.1007/978-0-85729-974-1_17

M3 - Chapter

SN - 978-0-85729-973-4

SP - 341

EP - 366

BT - System Identification, Environmental Modelling and Control Systems Design

A2 - Wang, Liuping

A2 - Garnier, Hugues

PB - Springer Verlag

CY - London

ER -