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系統識別號 U0026-1408202011320400
論文名稱(中文) 應用機器學習法於水文氣象極值事件降尺度與預報之研究
論文名稱(英文) Study on Hydro-Meteorological Extremes Downscaling and Forecasting by Using Machine Learning Methods
校院名稱 成功大學
系所名稱(中) 水利及海洋工程學系
系所名稱(英) Department of Hydraulics & Ocean Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 范保國
研究生(英文) Pham Bao Quoc
學號 N88057033
學位類別 博士
語文別 英文
論文頁數 86頁
口試委員 指導教授-游保杉
口試委員-郭振民
口試委員-楊道昌
召集委員-李錦育
口試委員-陳憲宗
口試委員-林妤蓁
中文關鍵字 none 
英文關鍵字 statistical downscaling  random forest  least square support vector regression  extreme rainfall  standardized precipitation index  drought forecasting  singular spectrum analysis 
學科別分類
中文摘要 none
英文摘要 Studying on hydro-meteorological extremes downscaling and forecasting has attracted much interest because it is important for water resources management. This study focuses on exploring the ability of machine learning in order to improve the accuracy of hydro-meteorological extremes downscaling and forecasting. To this end, this dissertation carried out two separate studies: (1) combining random forest (RF) and least square support vector regression (LSSVM) for improving extreme rainfall downscaling and (2) coupling singular spectrum analysis (SSA) with LSSVM to improve accuracy of SPI drought forecasting.
For the first study, a statistical downscaling approach for improving extreme rainfall simulation was proposed to predict the daily rainfalls at Shihmen Reservoir catchment in northern Taiwan. The structure of the proposed downscaling approach is composed of two parts: the rainfall-state classification and the regression for rainfall-amount prediction. Predictors of classification and regression methods were selected from the large-scale climate variables of the National Center for Environmental Prediction (NCEP) reanalysis data based on statistical tests. The data during 1964–1999 and 2000–2013 were used for calibration and validation, respectively. Three classification methods, including linear discriminant analysis (LDA), random forest (RF), and support vector classification (SVC), were adopted for rainfall-state classification and their performances were compared. After rainfall-state classification, the LSSVM was used for rainfall-amount prediction for different rainfall states. Two rainfall states (i.e., dry day and wet day) and three rainfall states (dry day, non-extreme-rainfall day, and extreme-rainfall day) were defined and compared for judging their downscaling performances. The results show that RF outperforms LDA and SVC for rainfall-state classification. Using RF for three-rainfall-states classification and LSSVM for rainfall-amount prediction can improve the extreme rainfall downscaling.
The second study of this dissertation is coupling SSA with LSSVM to improve accuracy of SPI drought forecasting. The aims of this study is twofold: (1) investigating and comparing the performance of the single LSSVM and the coupled SSA-LSSVM, and (2) comparison of the effectiveness of different between two types of model inputs (i.e., antecedent SPI and monthly aggregated rainfall) when it is preprocessed by using SSA on drought forecasting. For the first aim, two different models were proposed including the single LSSVM with the input of antecedent SPI values (LSSVM1) and a coupled SSA-LSSVM with the input of antecedent SPI6 values (SSA-LSSVM2). For the second aim, a coupled SSA-LSSVM with the input of antecedent monthly aggregated rainfall (SSA-LSSVM3) was proposed and compared to SSA-LSSVM2. SPI3 and SPI6 drought indexes in Tsengwen Reservoir catchment, Southern Taiwan, was selected as a case study. Three performance indices (i.e., root mean square error, correlation coefficient, and mean absolute error) were then used for evaluating the performance of three proposed models. The results indicate that SSA can significantly improve the performance of forecasting models when compare LSSVM1 and SSA-LSSVM2. In addition, the comparison of performance of SSA-LSSVM2 and SSA-LSSVM3 indicates that using raw monthly aggregated rainfall data as input of SSA is much better than that of using raw SPI data. The possible reasons for this were also briefly explained in the discussion part of this study. Along with Tsengwen Reservoir catchment, this study then used data in the reservoir catchments of Shihmen, Deji, and Kaoping to validate the efficient of the coupled model of SSA and LSSVM. Results also show that the SSA-LSSVM3 model is the most suitable method for drought forecasting for the study area according to the performance measures.
Through these two studies in this dissertation, it is apparent that the combination of LSSVM with appropriate models (i.e. RF and SSA) could obtain remarkable improvement in downscaling and forecasting of extreme values.
論文目次 TABLE OF CONTENTS
ABSTRACT III
TABLE OF CONTENTS VIII
LIST OF TABLES X
LIST OF FIGURES XI
LIST OF SYMBOLS XV
ABBREVIATIONS XVI
CHAPTER ONE: INTRODUCTION 1
1.1 Statement of the Problem 1
1.2 Research Objectives 3
1.3 Statistical Rainfall Downscaling 4
1.4 Drought Forecasting 6
1.5 Research Contribution 10
CHAPTER TWO: STUDY AREAS AND DATASETS 12
2.1 Shihmen Reservoir Catchment 12
2.2 Tsengwen Reservoir Catchment 15
CHAPTER THREE: STUDY ON STATISTICAL EXTREME RAINFALL DOWNSCALING 17
3.1 Methodology for Statistical Extreme Rainfall Downscaling 17
3.1.1. Approaches for Three Types of Statistical Rainfall Downscaling 18
3.1.2. Linear Discriminant Analysis 21
3.1.3. Random Forest 22
3.1.4. Least Square Support Vector Machine 22
3.2 Results and Discussion 25
3.2.1 Rainfall-State Classification 25
3.2.2 Regression of Rainfall Amount 29
3.2.3 Discussion 34
CHAPTER FOUR: STUDY ON DROUGHT FORCASTING 36
4.1 Methodology for SPI Drought Index Forecasting 36
4.1.1. The Standardized Precipitation Index (SPI) drought index 36
4.1.2. Singular Spectrum Analysis (SSA) 38
4.1.3. Coupling Singular Spectrum Analysis with Least Square Support Vector Machine (SSA-LSSVM) 41
4.1.4. Model performance assessment for SPI drought forecasting 45
4.2 Results and Discussion 47
4.2.1 Performance Comparison of single LSSVM1 and coupled SSA-LSSVM2. 48
4.2.2 Performance Comparison of SSA-LSSVM2 and SSA-LSSVM3 Models 51
4.2.3 Discussion 54
CHAPTER FIVE: CONCLUSION AND SUGGESTION 58
REFERENCES 62
APPENDICES 75

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