
系統識別號 
U00261408202011320400 
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 HydroMeteorological 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 hydrometeorological 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 hydrometeorological 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 rainfallstate classification and the regression for rainfallamount prediction. Predictors of classification and regression methods were selected from the largescale 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 rainfallstate classification and their performances were compared. After rainfallstate classification, the LSSVM was used for rainfallamount prediction for different rainfall states. Two rainfall states (i.e., dry day and wet day) and three rainfall states (dry day, nonextremerainfall day, and extremerainfall day) were defined and compared for judging their downscaling performances. The results show that RF outperforms LDA and SVC for rainfallstate classification. Using RF for threerainfallstates classification and LSSVM for rainfallamount 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 SSALSSVM, 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 SSALSSVM with the input of antecedent SPI6 values (SSALSSVM2). For the second aim, a coupled SSALSSVM with the input of antecedent monthly aggregated rainfall (SSALSSVM3) was proposed and compared to SSALSSVM2. 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 SSALSSVM2. In addition, the comparison of performance of SSALSSVM2 and SSALSSVM3 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 SSALSSVM3 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 RainfallState 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 (SSALSSVM) 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 SSALSSVM2. 48
4.2.2 Performance Comparison of SSALSSVM2 and SSALSSVM3 Models 51
4.2.3 Discussion 54
CHAPTER FIVE: CONCLUSION AND SUGGESTION 58
REFERENCES 62
APPENDICES 75

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