Comparison between quantile regression technique and generalised additive model for regional flood frequency analysis : a case study for Victoria, Australia

Farhana Noor; Orpita U. Laz; Khaled Haddad; Mohammad A. Alim; Ataur Rahman

doi:10.3390/w14223627

Comparison between quantile regression technique and generalised additive model for regional flood frequency analysis : a case study for Victoria, Australia

Farhana Noor, Orpita U. Laz, Khaled Haddad, Mohammad A. Alim, Ataur Rahman

Western Sydney University

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relative error ranging 16–41%. For higher return periods (i.e., 20, 50 and 100 years), log-log linear regression model (QRT) outperforms the GAM model with a median relative error ranging 31–59%.

Original language	English
Article number	3627
Number of pages	15
Journal	Water
Volume	14
Issue number	22
DOIs	https://doi.org/10.3390/w14223627
Publication status	Published - 2022

Open Access - Access Right Statement

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Access to Document

10.3390/w14223627

Cite this

@article{fe2a806b99f04551b55a652c33999013,

title = "Comparison between quantile regression technique and generalised additive model for regional flood frequency analysis : a case study for Victoria, Australia",

abstract = "For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relative error ranging 16–41%. For higher return periods (i.e., 20, 50 and 100 years), log-log linear regression model (QRT) outperforms the GAM model with a median relative error ranging 31–59%.",

author = "Farhana Noor and Laz, {Orpita U.} and Khaled Haddad and Alim, {Mohammad A.} and Ataur Rahman",

year = "2022",

doi = "10.3390/w14223627",

language = "English",

volume = "14",

journal = "Water",

issn = "2073-4441",

publisher = "MDPI AG",

number = "22",

}

TY - JOUR

T1 - Comparison between quantile regression technique and generalised additive model for regional flood frequency analysis : a case study for Victoria, Australia

AU - Noor, Farhana

AU - Laz, Orpita U.

AU - Haddad, Khaled

AU - Alim, Mohammad A.

AU - Rahman, Ataur

PY - 2022

Y1 - 2022

N2 - For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relative error ranging 16–41%. For higher return periods (i.e., 20, 50 and 100 years), log-log linear regression model (QRT) outperforms the GAM model with a median relative error ranging 31–59%.

AB - For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relative error ranging 16–41%. For higher return periods (i.e., 20, 50 and 100 years), log-log linear regression model (QRT) outperforms the GAM model with a median relative error ranging 31–59%.

UR - https://hdl.handle.net/1959.7/uws:69228

U2 - 10.3390/w14223627

DO - 10.3390/w14223627

M3 - Article

SN - 2073-4441

VL - 14

JO - Water

JF - Water

IS - 22

M1 - 3627

ER -

Comparison between quantile regression technique and generalised additive model for regional flood frequency analysis : a case study for Victoria, Australia

Abstract

Open Access - Access Right Statement

Access to Document

Other files and links

Fingerprint

Cite this