American Society of Civil Engineers


Improving Accuracy of IDF Curves Using Long- and Short-Duration Separation and Multi-Objective Genetic Algorithm


by Taesoon Kim, Ph.D., (BK21 Lecturer, School of Civil and Environmental Engineering, Yonsei University, Seoul 120-749, South Korea ( E-mail: chaucer@yonsei.ac.kr)), Ju-Young Shin, (Graduate student, School of Civil and Environmental Engineering, Yonsei University, Seoul 120-749, South Korea ( E-mail: ausran@yonsei.ac.kr)), Kewtae Kim, (Graduate student, School of Civil and Environmental Engineering, Yonsei University, Seoul 120-749, South Korea ( E-mail: oruback@yonsei.ac.kr)), and Jun-Haeng Heo, (Professor, School of Civil and Environmental Engineering, Yonsei University, Seoul 120-749, South Korea ( E-mail: jhheo@yonsei.ac.kr))

pp. 1-9, (doi:  http://dx.doi.org/10.1061/40976(316)128)

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Document type: Conference Proceeding Paper
Part of: World Environmental and Water Resources Congress 2008: Ahupua’A
Abstract: Multi-objective genetic algorithm (MOGA) and cumulative distribution function (CDF) are used to improve the accuracy of IDF curve. Rainfall durations are divided into short- and long-duration using root mean squared error (RMSE) and relative root mean squared error (RRMSE) between rainfall quantiles by IDF curve and at-site frequency analysis. RMSE could be used for estimating parameters of relatively long-duration, and RRMSE for short-duration. The compromised solutions could be ahieved through MOGA with two multi-objective functions. The duration separating technique called C0MBI_1 is suggested and the comparison with the five different parameter estimation methods provides COMBI_1 is superior to the other methods.


ASCE Subject Headings:
Accuracy
Algorithms
Rainfall
Errors