Meta-Heuristic Algorithms for Hydrologic Frequency Analysis Academic Article

abstract

• Meta-heuristic algorithms, such as the genetic algorithm and ant colony optimization, have received considerable attention in recent years due to their higher ability for solving difficult engineering optimization problems. This paper employs these techniques for estimating parameters of commonly used flood frequency distributions, and compares them with some conventional methods such as maximum likelihood, moments and probability weighted moments using annual maximum discharge data of 14 rivers from East-Azarbaijan, Iran. The results indicate that both the genetic algorithm and ant colony optimization are suitable parameter estimation alternatives. Also, the results of Monte Carlo simulation for various sample sizes, ranging from 20 to 100, demonstrate that the meta-heuristic algorithms yield accurate quantile estimates. 2011 Springer Science+Business Media B.V.

authors

• Singh, Vijay

published proceedings

• WATER RESOURCES MANAGEMENT

author list (cited authors)

• Hassanzadeh, Y., Abdi, A., Talatahari, S., & Singh, V. P.

citation count

• 23

complete list of authors

• Hassanzadeh, Yousef||Abdi, Amin||Talatahari, Siamak||Singh, Vijay P

publication date

• May 2011

publisher

• Springer Nature  Publisher

published in

• WATER RESOURCES MANAGEMENT  Journal

keywords

• Conventional Methods
• Hydrologic Frequency Analysis
• Meta-heuristic Algorithms
• Monte Carlo Simulation
• Parameter Estimation

Digital Object Identifier (DOI)

• 10.1007/s11269-011-9778-1

start page

• 1855

end page

• 1879

volume

• 25

issue

• 7

URL

• http%3A%2F%2Fdx.doi.org%2F10.1007%2Fs11269-011-9778-1