Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Ensemble machine learning (EML) based regional flood frequency analysis model development and testing for south-east Australia
Study region: South-east Australia Study focus: This study develops two ensemble-based regional flood frequency analysis (RFFA) techniques, Random Forest Regression (RFR) and Gradient Boosting Regression (GBR)) with a standalone method (Artificial Neural Network (ANN), for south-east Australia. A dataset from 201 catchments across south-east Australia is used in this study. It includes six Annual Exceedance Probabilities (AEPs), 1 in 2, 1 in 5, 1 in 10, 1 in 20, 1 in 50, and 1 in 100 to estimate design floods, which are widely used in the planning and design of water infrastructure. An independent test is adopted to compare the performance of the selected RFFA techniques. New hydrological insights for the region: This study employs a random forest (RF) algorithm as a nonlinear feature selection method to select the important features/catchment characteristics (predictors) in the RFFA. Out of the eight candidate predictors, three are selected to develop and test the selected RFFA techniques. The findings indicate that ensemble methods (RFR and GBR) provide better performance than the standalone ANN technique. The median relative error values are found to be in the range of 33–44 % for the RFR, 34–46 % for the GBR, and 35–53 % for the ANN. The results of this study would be useful in upgrading RFFA methods in the Australian Rainfall and Runoff (national guideline).
Ensemble machine learning (EML) based regional flood frequency analysis model development and testing for south-east Australia
Study region: South-east Australia Study focus: This study develops two ensemble-based regional flood frequency analysis (RFFA) techniques, Random Forest Regression (RFR) and Gradient Boosting Regression (GBR)) with a standalone method (Artificial Neural Network (ANN), for south-east Australia. A dataset from 201 catchments across south-east Australia is used in this study. It includes six Annual Exceedance Probabilities (AEPs), 1 in 2, 1 in 5, 1 in 10, 1 in 20, 1 in 50, and 1 in 100 to estimate design floods, which are widely used in the planning and design of water infrastructure. An independent test is adopted to compare the performance of the selected RFFA techniques. New hydrological insights for the region: This study employs a random forest (RF) algorithm as a nonlinear feature selection method to select the important features/catchment characteristics (predictors) in the RFFA. Out of the eight candidate predictors, three are selected to develop and test the selected RFFA techniques. The findings indicate that ensemble methods (RFR and GBR) provide better performance than the standalone ANN technique. The median relative error values are found to be in the range of 33–44 % for the RFR, 34–46 % for the GBR, and 35–53 % for the ANN. The results of this study would be useful in upgrading RFFA methods in the Australian Rainfall and Runoff (national guideline).
Ensemble machine learning (EML) based regional flood frequency analysis model development and testing for south-east Australia
Nilufa Afrin (Autor:in) / Ataur Rahman (Autor:in) / Ahmad Sharafati (Autor:in) / Farhad Ahamed (Autor:in) / Khaled Haddad (Autor:in)
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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| Elsevier | 2025
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