Application of Non-Dominated Sorting Genetic Algorithm in Calibration of HBV Rainfall-runoff Model: A Case Study of Tsengwen Reservoir Catchment in Southern Taiwan

  • 黎 長灣

Student thesis: Master's Thesis


The objective of this study is to apply a multi-objective optimization algorithm for tuning parameters of the HBV rainfall-runoff model This study selected the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) as optimization algorithm and examined various objective functions for investigating the performance of the HBV model in different flow situations (e g low flow and high flow) Two objective functions were chosen in this study: root mean squared error (RMSE) and mean absolute percentage error (MPE) Previous studies (e g Getahun and Van Laned 2015) showed that the HBV might give bias estimates for low and high flow situations Thus the study proposed a season-dependent calibration strategy for further improving the biased estimates in different flow situations The strategy is composed of two parts: (1) the RMSE-based objective function is used for wet seasons only (i e high flow situations); (2) the MPE-based objective function is used for dry seasons only (i e low flow situations) The preliminary results suggest that the proposed season-dependent strategy can improve results
Date of Award2016 Jul 27
Original languageEnglish
SupervisorPao-Shan Yu (Supervisor)

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