Comparison of conventional and differential evolution-based parameter estimation methods on the flood frequency analysis

Accurate estimation of flood frequency is an important task for water resources management. This starts with appropriate selection of probability distribution to flood samples (annual maximum flows) that is of great importance for flood frequency analysis (FFA). In order to reach the most precise es...

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Bibliographic Details
Published in:Acta geophysica 2021-10, Vol.69 (5), p.1887-1900
Main Authors: Yilmaz, Muhammet, Tosunoglu, Fatih, Demirel, Mehmet Cüneyd
Format: Article
Language:English
Subjects:
Distribution functions
Earth and Environmental Science
Earth Sciences
Evolution
Evolutionary computation
Flood frequency
Flood frequency analysis
Flood management
Flood plain management
Flood predictions
Floodplains
Floods
Frequency analysis
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Hydraulic structures
Maximum likelihood method
Maximum probable flood
Parameter estimation
Probability distribution
Probability distribution functions
Research Article - Hydrology
Risk analysis
Structural Geology
Water resources
Water resources management
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Summary:Accurate estimation of flood frequency is an important task for water resources management. This starts with appropriate selection of probability distribution to flood samples (annual maximum flows) that is of great importance for flood frequency analysis (FFA). In order to reach the most precise estimation, the probability distribution of the considered time series should be well defined and its parameters should be more accurately estimated. First time in the FFA literature, a differential evolution-based parameter estimation method is applied to obtain the parameters of probability distribution functions and is compared with the traditional maximum likelihood method (MLM) in the present study. For this purpose, eleven distributions have been used to describe the annual maximum flood series of nine gauging sites, with the performance of each distribution being investigated based on six criteria. The results revealed that a single distribution cannot be specified as the best-fit distribution for the study area. Moreover, it has been found that the applied approach improves the probability prediction of floods better than MLM method for efficient design of hydraulic structures, risk analysis and floodplain management.
ISSN:1895-6572
1895-7455
DOI:10.1007/s11600-021-00645-y