Scaling Issues in Design Flood Estimation for Ungauged Catchments: A Case Study for Eastern Australia

Zaman, Mohammad A; Rahman, Ishita; Haddad, Khaled; Rahman, Ataur

doi:10.1061/41114(371)295

Cited by 3 publications

(7 citation statements)

References 17 publications

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“…AREA is the main scaling factor and has widely been used in RFFA [52,53]. The design rainfall intensity is the main input to the flood generation process and has been…”

Section: Datamentioning

confidence: 99%

“…It should be noted that all these eight predictor variables were included in the developed ANN and SVM-based RFFA models presented in this study. AREA is the main scaling factor and has widely been used in RFFA [52,53]. The design rainfall intensity is the main input to the flood generation process and has been adopted in many RFFA studies [50,54].…”

Section: Datamentioning

confidence: 99%

See 1 more Smart Citation

Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia

Zalnezhad

Rahman

Nasiri³

et al. 2022

Water

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Design flood estimations at ungauged catchments are a challenging task in hydrology. Regional flood frequency analysis (RFFA) is widely used for this purpose. This paper develops artificial intelligence (AI)-based RFFA models (artificial neural networks (ANN) and support vector machine (SVM)) using data from 181 gauged catchments in South-East Australia. Based on an independent testing, it is found that the ANN method outperforms the SVM (the relative error values for the ANN model range 33–54% as compared to 37–64% for the SVM). The ANN and SVM models generate more accurate flood quantiles for smaller return periods; however, for higher return periods, both the methods present a higher estimation error. The results of this study will help to recommend new AI-based RFFA methods in Australia.

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“…AREA is the main scaling factor and has widely been used in RFFA [52,53]. The design rainfall intensity is the main input to the flood generation process and has been…”

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia

Zalnezhad

Rahman

Nasiri³

et al. 2022

Water

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“…We use a Bayesian model to let the data define the point where the change between phases occurs. Such models have been applied successfully to study inflation (Koop & Potter, 2004), water flooding (Zaman, Rahman, & Haddad, 2012), Alzheimer’s disease (Li, Dowling, & Chappell, 2015), menstrual cycle (Huang, Elliott, & Harlow, 2014), and climate variations (Beaulieu, Chen, & Sarmiento, 2012). In this article we will discuss how the Bayesian unknown change-point model can be used to provide inferential statistical evidence of immediacy to supplement visual analysis and identify delayed effects.…”

Section: Problems With Commonly Used Statistical Analyses In Scdsmentioning

confidence: 99%

Bayesian unknown change-point models to investigate immediacy in single case designs.

Natesan

Hedges²

2017

Psychological Methods

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Although immediacy is one of the necessary criteria to show strong evidence of a causal relation in single case designs (SCDs), no inferential statistical tool is currently used to demonstrate it. We propose a Bayesian unknown change-point model to investigate and quantify immediacy in SCD analysis. Unlike visual analysis that considers only 3-5 observations in consecutive phases to investigate immediacy, this model considers all data points. Immediacy is indicated when the posterior distribution of the unknown change-point is narrow around the true value of the change-point. This model can accommodate delayed effects. Monte Carlo simulation for a 2-phase design shows that the posterior standard deviations of the change-points decrease with increase in standardized mean difference between phases and decrease in test length. This method is illustrated with real data. (PsycINFO Database Record

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“…The reason for selecting these variables are that in previous Australian studies, these have been found to be significant in RFFA (e.g. Haddad and Rahman 2012;Rahman 1997). To estimate flood quantiles of annual exceedance probabilities (AEPs) of 1 in 2 (Q 2 ), 1 in 5 (Q 5 ), 1 in 10 (Q 10 ), 1 in 20 (Q 20 ), 1 in 50 (Q 50 ) and 1 in 100 (Q 100 ), a Log Pearson Type 3 distribution was adopted as it has been found to be the bestfitting probability distribution in Australia (Rahman et al 2013).…”

Section: Study Area and Datamentioning

confidence: 99%

“…In almost all previous RFFA studies, AREA has been used. Many other catchment characteristics such as slope, stream order and stream length are closely related to AREA (Anderson 1957;Rahman 1997). The area is the most used morphometric characteristic amongst the other characteristics, and it is known to be the main scaling factor in statistical hydrology.…”

Section: Catchment Areamentioning

confidence: 99%

Design flood estimation at ungauged catchments using index flood method and quantile regression technique: a case study for South East Australia

Zalnezhad,

Rahman,

Ahamed

et al. 2023

Nat Hazards

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Flood is one of the worst natural disasters, which causes the damage of billions of dollars each year globally. To reduce the flood damage, we need to estimate design floods accurately, which are used in the design and operation of water infrastructure. For gauged catchments, flood frequency analysis can be used to estimate design floods; however, for ungauged catchments, regional flood frequency analysis (RFFA) is used. This paper compares two popular RFFA techniques, namely the quantile regression technique (QRT) and the index flood method (IFM). A total of 181 catchments are selected for this study from south-east Australia. Eight predictor variables are used to develop prediction equations. It has been found that IFM outperforms QRT in general. For higher annual exceedance probabilities (AEPs), IFM generally demonstrates a smaller estimation error than QRT; however, for smaller AEPs (e.g. 1 in 100), QRT provides more accurate quantile estimates. The IFM provides comparable design flood estimates with the Australian Rainfall and Runoff—the national guide for design flood estimation in Australia.

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Scaling Issues in Design Flood Estimation for Ungauged Catchments: A Case Study for Eastern Australia

Cited by 3 publications

References 17 publications

Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia

Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia

Bayesian unknown change-point models to investigate immediacy in single case designs.

Design flood estimation at ungauged catchments using index flood method and quantile regression technique: a case study for South East Australia

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