Kavramsal Hidrolojik Modellerin Farklı Optimizasyon Algoritmaları İle Kalibrasyonu - Calibration of Conceptual Hydrological Model by Different Optimization Algorithms

Yıl 2015, Cilt: 11 Sayı: 2, 0 - , 19.12.2015

Mustafa Turan , Erkan Doğan

https://doi.org/10.18466/cbujos.04100

Cited By: 1

Öz

Kavramsal Hidrolojik Modellerin Farklı Optimizasyon Algoritmaları İle Kalibrasyonu

 Mevcut çalışmada kavramsal hidrolojik modellerin optimizasyon metotları yardımıyla kalibrasyonu ele alınmıştır. Sezgiye dayalı yenilikçi optimizasyon algoritmaları doğada var olan olayların matematiksel olarak taklit edildiği çözüm yöntemleridir. Bu tip yöntemler, optimum çözümü araştırırken rastgele ve olasıksal parametreler kullanırlar. Bu yöntemlerden av arama, yapay arı kolonisi ve ateş böceği algoritmaları literatürde yer alan GR4J, GR2M kavramsal hidrolojik modellerinin kalibrasyonu için kullanılmış, farklı gözlem istasyonlarından alınan veriler değerlendirilerek yöntemlerin optimizasyon problemi üzerindeki etkinlikleri araştırılmıştır. 

Calibration of Conceptual Hydrological Model by Different Optimization Algorithms

In this study, calibration of conceptual hydrological models is carried out by means of optimization methods. Meta-Heuristic inonative optimization algorithms are the methods in which the natural events have been imitated mathematically. These type of methods use random and probabilistic parameters to investigate optimal solutions. Hunting search, artificial bee colony and firefly algorithms are used for calibration of GR4J, GR2M conceptual hydrological models and the efficiency of the methods on the optimization problems is investigated by evaluating the data from the different gauging stations. 

Anahtar Kelimeler

GR4J, GR2M, Kavramsal Hidrolojik Modeller, Kalibrasyon, Av Arama Algoritması, Ateş Böceği Algoritması, Yapay Arı Kolonisi Algoritması / GR4J, GR2M, Conceptual Hidrological Model, Calibration, Hunting Search Algorithm, Firefly Algorithm, Artificial Bee Co

Kaynakça

• Hasançebi, O.; Çarbaş, S.; Doğan, E., Erdal; F., Saka, M.P., Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Computers and Structures, 2009; 87(5-6), 284-302.
• Yang, X.S., Firefly Algorithms for Multimodal Optimization. Stochastic Algorithms: Foundations and Applications. Lecture Notes in Computer Science, 2009; 5792, 169-178.
• Franchini, M.; Galeati, G.; Saverio, B., Global optimization techniques for the calibration of conceptual rainfallrunoff models. Hydrological Sciences Journal, 1998; 43(3), 443-458.
• Blasone, R.S.; Madsen, H.; Rosbjerg, D., Parameter estimation in distributed hydrological modelling: Comparison of global and local optimisation techniques. Nordic Hydrology, 2007; 38(4-5), 451–476.
• Arsenault, R.; Poulin, A.; Côté, P.; Brissette, F., Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration. Journal of Hydrologic Engineering, 2014; 19(7), 1374-1384.
• Perrin, C., Vers une amélioration d’un modéle global pluie-débit au travers d’une approche comparative, France: INPG (Grenoble)/Cemagref (Antony), 2000.
• Mouelhi, S.; Michel, C.; Perrin, C.; Andréassian, V., Stepwise Development of a Two-Parameter Monthly Water Balance Model. Journal of Hydrology, 2006; 318, 200-214.
• Nash, J.E.; Sutcliffe, J.V., River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 1970; 10(3), 282-290.
• Oftadeh, R.; Mahjoob, M.J.; Shariatpanahi, M., A novel meta-heuristic optimization algorithm inspired by group hunting of animals:Hunting search. Computers Mathematics with Applications, 2010; 60,2087-2098.
• Yang, X.S., Nature-Inspired Metaheuristic Algorithms, London: Luniver Press, 2008; 116 pp.
• Karaboğa, D., An Idea Based on Honey Bee Swarm For Numerical Optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Kayseri, 2005.
• Karaboğa, D.; Baştürk, B., A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization, 2007; 39(3), 459-471.
• Marinakis, Y.; Marinaki, M.; Matsatsinis, N., A Hybrid Discrete Artificial Bee Colony-GRASP Algorithm for Clustering, Computers & Industrial Engineering, International Conference on, Troyes, 2009.
• Akay, B., Nümerik optimizasyon problemlerinde yapay arı kolonisi (artificial bee colony) algoritmasının performans analizi. Kayseri: Erciyes Üniversitesi Fen Bilimleri Enstitüsü. Doktora Tezi, 2009.
• Karaboğa, D., Yapay Zekâ Optimizasyon Algoritmaları. İstanbul: Atlas Yayın Dağıtım, 2004; 231pp.
• DSİ, 2011 Yılı Faaliyet Rapor. DSİ, Ankara, 2012.
• Oudin, L.; Hervieu, F.; Michel, C.; Perrin, C.; Andréassian, V.; Anctil F.; Loumagne, C., Which potential evapotranspiration input for a lumped rainfall–runoff model?: Part 2—Towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling. Journal of hydrology, 2005; 303(1), 290-306.
• Perrin, C.; Michel, C.; Andréassian, V., Improvement of a parsimonious model for streamflow simulation. Journal of Hydrology, 2003; 279(1-4), 275-289.
• Apostolopoulos, T.; Vlachos, A., Application of the firefly algorithm for solving the economic emissions load dispatch problem. Journal of Combinatorics, 2011.
• Farahani, S. M.; Nasiri, B.; Abshouri, A. A.; Meybodi, M. R., An improved firefly algorithm with directed movement, Proceedings of 4th IEEE International Conference on Computer Science and Information Technology, Chengdu, 2011.
• Yang, X. S., Firefly algorithm, Stochastic Test Functions and Design Optimization. International Journal of Bio-Inspired Computation, 2010; 2(2), 78-84.
• Karaboğa, D., A New Design Method Based on Artificial Bee Colony Algorithm for Digital IIR Filters. Journal of the Franklin Institute, 2009; 346(4), 328–348.