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系統識別號 U0026-2607201607313600
論文名稱(中文) 應用非支配排序遺傳演算法於HBV水文模式率定:以南臺灣曾文水庫集水區為例
論文名稱(英文) Application of Non-Dominated Sorting Genetic Algorithm in Calibration of HBV Rainfall-runoff Model: A Case Study of Tsengwen Reservoir Catchment in Southern Taiwan
校院名稱 成功大學
系所名稱(中) 自然災害減災及管理國際碩士學位學程
系所名稱(英) International Master Program on Natural Hazards Mitigation and Management
學年度 104
學期 2
出版年 105
研究生(中文) 黎長灣
研究生(英文) Le Truong Vinh
學號 NC6037037
學位類別 碩士
語文別 英文
論文頁數 67頁
口試委員 指導教授-游保杉
召集委員-陳憲宗
口試委員-林妤蓁
口試委員-楊道昌
口試委員-郭振民
中文關鍵字 None 
英文關鍵字 multi-objective optimization algorithm  the HBV rainfall-runoff model  calibration strategy 
學科別分類
中文摘要 None
英文摘要 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.
論文目次 ACKNOWLEDGEMENTS II
TABLE OF CONTENTS III
LIST OF TABLES V
LIST OF FIGURES VII
CHAPTER ONE: INTRODUCTION 1
1.1 General introduction 1
1.1.1 Motivation 1
1.1.2 Objectives of this study 1
1.2 Literature review 2
1.2.1 Hydrological models and the HBV model 2
1.2.2 Model calibration 3
1.2.3 NSGA-II application in hydrological model 7
1.3 Structure of the thesis 9
CHAPTER TWO: STUDY AREA AND DATA SETS 11
2.1 Study area 11
2.2 Data sets 11
CHAPTER THREE: METHODOLOGIES 13
3.1 MHBV model 13
3.2 Multi-objective function and Pareto-optimal solutions 18
3.3 NSGA-II Algorithm 21
3.3.1 Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II 21
3.3.2 Testing cases for NSGA-II 24
3.4 Calibration strategies for MHBV model 27
3.4.1 Two single objective functions 29
3.4.2 Multi-objective function 29
3.4.3 Improvement of multi-objective function 29
3.5 Framework for linking MHBV with NSGA-II and simulation 29
CHAPTER FOUR: RESULTS AND DISCUSSIONS 34
4.1 Two single objective functions 34
4.2 Multi-objective function 37
4.3 A comparison between the results by using two single objective functions and multi-objective function 40
4.4 Improvement of multi-objective function 41
4.5 Discussions 55
CHAPTER FIVE: CONCLUSION AND SUGGESTIONS 56
5.1 Conclusions 56
5.2 Suggestions 56
REFERENCES 58
APPENDICES 61
Appendix A 61
Appendix B 64

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