Rafi et al. (2026) Development of Quantile Regression Technique for Queensland for Regional Flood Estimation

Identification

• Journal: Lecture notes in civil engineering
• Year: 2026
• Date: 2026-01-01
• Authors: Ridwan S. M. H. Rafi, Sadia T. Mim, Hasan Abidur Rahaman, Nadia Afrin, Monisha Anindita, Ataur Rahman
• DOI: 10.1007/978-3-032-18708-6_13

Research Groups

• Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh
• Department of Civil Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
• CARE Bangladesh, Ukhiya Field Office, Paschim Morichya, Morichya Palong-4750, Ukhiya, Cox’s Bazar, Bangladesh
• Independent Researcher, Dhaka, Bangladesh
• School of Engineering, Design and Built Environment, Western Sydney University, Sydney, New South Wales, Australia

Short Summary

This study evaluates the Quantile Regression Technique (QRT) for regional flood estimation in Queensland, Australia, for 10-year and 50-year return periods, finding that the QRT model performs more reliably for shorter return periods (Q10).

Objective

• To evaluate the Quantile Regression Technique (QRT) for regional flood frequency analysis (RFFA) in Queensland, Australia, specifically for 10-year (Q10) and 50-year (Q50) flood return periods to aid in designing flood-safe infrastructure at ungauged catchments.

Study Configuration

• Spatial Scale: Queensland state, Australia.
• Temporal Scale: Flood estimation for 10-year and 50-year return periods, based on historical flood characteristics from gauged catchments.

Methodology and Data

• Models used: Quantile Regression Technique (QRT). The models were assessed against assumptions of Ordinary Least Squares (OLS).
• Data sources: Regional Flood Frequency Analysis (RFFA) method, which transfers flood characteristics from gauged catchments to ungauged sites. Implied use of historical flood data from gauged catchments in Queensland.

Main Results

• Both QRT models for Q10 and Q50 satisfied the assumptions of Ordinary Least Squares, with residuals randomly distributed and over 90% within ±2, indicating stable variance and approximate normality.
• The Q10 model demonstrated stronger predictive skill with an R² of 0.71.
• The Q50 model showed an R² of 0.61.
• Error analysis for Q10 revealed lower mean absolute relative error (39.41%) and median absolute relative error (26.00%).
• Error analysis for Q50 showed higher mean absolute relative error (54.93%) and median absolute relative error (30.92%).
• The QRT demonstrated greater reliability for shorter return periods (Q10) due to lower errors and reduced variability.

Contributions

• This study provides a specific evaluation of the Quantile Regression Technique (QRT) for regional flood estimation in Queensland, Australia, a region frequently affected by devastating floods.
• It quantitatively assesses the performance of QRT for different flood return periods (Q10 and Q50), highlighting its differential reliability and suggesting its suitability for shorter return periods in the region.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Rafi2026Development,
  author = {Rafi, Ridwan S. M. H. and Mim, Sadia T. and Rahaman, Hasan Abidur and Afrin, Nadia and Anindita, Monisha and Rahman, Ataur},
  title = {Development of Quantile Regression Technique for Queensland for Regional Flood Estimation},
  journal = {Lecture notes in civil engineering},
  year = {2026},
  doi = {10.1007/978-3-032-18708-6_13},
  url = {https://doi.org/10.1007/978-3-032-18708-6_13}
}