Meta-Heuristic Algorithms for Hydrologic Frequency Analysis

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.

Ant Colony Optimization for Estimating Parameters of Flood Frequency Distributions

“…Results of the present algorithm are compared with some well-known parameter estimation methods; here, a brief review of these algorithms is presented (Hassanzadeh et al, 2011).…”

“…Results of the two models indicated good performance of the ACO model in terms of higher annual power production, satisfying irrigation demands, and flood control restrictions. Applying ACO to estimate parameters of flood frequency distributions was performed by Hassanzadeh et al (2011). They used the GA and ACO methodologies to estimate parameters of flood frequency distributions and compared them with conventional methods.…”

“…To evaluate the efficiency of the new algorithm, a region in Iran was considered (Hassanzadeh et al, 2011). The present study was carried out for the catchments of East Azerbaijan, which lie between latitude 36 to 39 North and longitude 45 to 48 East.…”

“…Although there are many probability distributions to choose among, selecting a suitable model is still one of the major problems in frequency analysis. In addition, there are several methods of parameter estimation, among which the most popular are the methods of maximum likelihood, moments, and probability-weighted moments (PWMs) (Hassanzadeh et al, 2011).…”

Aquifers Management through Evolutionary Bayesian Networks: The Altiplano Case Study (SE Spain)

“…has been illustrated (Molina-Cristobal et al 2005;Hanne and Nickel 2005;Osman et al 2005;Farmani et al 2005bFarmani et al , 2006Farmani et al and 2007Murugan et al 2009), there have been limited applications in policy analysis of water resources management (Farmani et al 2009). There are recent applications of EMO algorithms related to other water resources research studies such as optimal design of water distribution systems or reservoirs (Cisty 2010;Nazif et al 2010;Haghighi et al 2011;Hınçal et al 2011;Louati et al 2011), or Conjunctive Use of Surface Water and Groundwater (Safavi et al 2010), or the control of Seawater Intrusion in Coastal Aquifers (Kourakos and Mantoglou 2011; Abd-Elhamid and Javadi 2011; Sedki and Ouazar 2011), or hydrological studies (Dumedah et al 2010;;Hassanzadeh et al 2011;Gorev et al 2011). In this work an evolutionary multi-objective optimization tool (GANetXL 2007) based on NSGAII (Deb et al 2000) is coupled with the OOBN model developed in HUGIN software (2007), and used to assist in the selection of the best compromise management option(s) for participatory decision making.…”