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研究生：	張麗汶 Chang, Li-Wen
論文名稱：	基於集成學習之新店溪水環境韌性變化趨勢研究 Water Environment Resilience Variation in Hsintien River by Using Ensemble Learning
指導教授：	陳憲宗 Chen, Shien‐Tsung
學位類別：	碩士 Master
系所名稱：	工學院 - 自然災害減災及管理國際碩士學位學程 International Master Program on Natural Hazards Mitigation and Management
論文出版年：	2026
畢業學年度：	114
語文別：	英文
論文頁數：	118
中文關鍵詞：	新店溪、水環境韌性、CatBoost 、氣候變遷
外文關鍵詞：	Hsintien River, Water Environment Resilience, CatBoost, Climate Change
相關次數：	點閱：15 下載：0
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本研究旨在透過整合機器學習演算法 CatBoost 與 IPCC AR6 氣候情境，針對新店溪流域（包含秀朗與華中大橋集水區）建構一套「水質—水量—社會」水環境韌性評估框架，並推估未來二十年（2024–2043）之動態變遷趨勢。研究整合多維度指標作為模型輸入，涵蓋水量（雨量、流量）、水質（DO、BOD、SS、NH3-N）及社會（人口密度、不透水地表比例、污水接管率等）面向。研究結果證實，CatBoost降雨逕流模型具備優異的非線性擬合能力，能有效模擬流域水文反映。研究發現新店溪存在顯著的空間韌性差異：上游呈現「流量驅動」特徵，具備良好的自然稀釋與調節機制；下游則因高度都市化而呈現「結構性脆弱」，在極端事件中易因稀釋失效導致功能崩潰。未來推估顯示，在高排放情境（SSP5-8.5）下，極端降雨將改變環境壓力結構，使懸浮固體成為主導總體韌性變動的關鍵因子。整體而言，雖然基礎設施提升了水質表現，但極端氣候引發的流量極端變異仍是主要威脅。本研究建議流域管理策略應從傳統「污染防治」轉向動態「氣候調適」，並推動低衝擊開發以強化系統復原力。

This study aims to integrate the CatBoost machine learning algorithm with IPCC AR6 climate scenarios to construct a "Water Quality–Water Quantity–Social" nexus for water environment resilience assessment framework for the Hsintien River watershed, and to project dynamic trend changes over the next two decades (2024–2043). The study incorporates multi-dimensional indicators as model inputs, covering water quantity (rainfall and flow), water quality (DO, BOD, SS, NH₃-N), and social factors (population density, impervious surface ratio, sewer coverage, etc.). The study results confirm that the CatBoost rainfall–runoff model exhibits excellent non-linear fitting ability, effectively capturing the watershed's hydrological response. The study found significant spatial resilience differences in the Hsintien River: the upstream exhibits a "flow-driven" characteristic, with good natural dilution and regulation mechanisms; the downstream, however, is highly urbanized and exhibits "structural vulnerability," making it prone to functional collapse due to dilution failure during extreme events. Future projections show that under the high-emission scenario (SSP5-8.5), extreme rainfall will exacerbate "risk coupling," significantly increasing the correlation between suspended solids and overall resilience. Overall, although infrastructure improvements have enhanced water quality performance, extreme climate-induced water quantity fluctuations remain the primary threat. This study recommends that watershed management strategies shift from traditional "pollution control" to dynamic "climate adaptation," and promote Low Impact Development to strengthen the system's resilience.

Abstract i
中文摘要 ii
Acknowledgment iii
Contents iv
List of Table vii
List of Figure viii
Chapter 1  Introduction 1
1  Research Motivation 1
2  Research Objective 2
3  Research Framework 3
Chapter 2  Literature Review 5
1  Water Environment Resilience 5
1.1  Climate Change Trends 6
1.2  Human Disturbance 14
2  Establishing the Resilience Assessment Framework 16
2.1  Selection of Resilience Indicators 16
2.2  Machine Learning (ML) 19
Chapter 3  Study Area and Data 22
1  Study Area 22
1.1  Hsiulung Watershed (Upstream) 23
1.2  Huajhong Watershed (Downstream) 24
2  Data and Preprocessing 25
2.1  Resilience Assessment Baseline Period (2014–2023) 25
2.2  CatBoost Rainfall–runoff Model Input Variables (1970–2023) 28
2.3  TCCIP Gridded Observational Data 28
3  Climate Change Scenario Setting 31
3.1  Scenario Selection and Scientific Implications 31
3.2  Future Resilience Assessment Period (2024–2043) 32
3.3  TCCIP Gridded Climate Change Scenario Data 37
Chapter 4  Methodology 41
1  Resilience Assessment Framework 41
1.1  Multi-dimensional Indicator Analysis 42
1.2  Global Normalization Scoring Mechanism 43
1.3  Weighted Resilience Index Synthesis 44
2  CatBoost Algorithm 48
2.1  Ordered Boosting 48
2.2  Symmetric Trees 50
3  CatBoost Rainfall–runoff Model Construction and Setup 51
3.1  Feature Engineering 51
3.2  Algorithm Theory and Optimization 53
3.3  Data Splitting 54
3.4  Recursive Rolling Forecasting Mechanism 55
4  Model Evaluation Indicators 56
4.1  Loss Function 56
4.2  Coefficient of Determination and Hydrological Indicators 57
4.3  Cross-Disciplinary Integration and Use of Combined Indicators 58
5  Rainfall–runoff Model Calibration and Validation Performance 58
6  CatBoost Water Quality Prediction Model 63
6.1  Input Data Settings 64
6.2  Prediction Results 64
Chapter 5  Water Environment Resilience Assessment of Hsintien River basin 67
1  Analysis of Spatial Resilience Differences during the Baseline Period 68
1.1  Evaluation of Specific Indicators 71
1.2  Spatial Differences in Driving Patterns 76
1.3  Verification of Model Sensitivity 77
1.4  Weight Sensitivity and System Instability Analysis 79
1.5  Analysis of Collapse Mechanism under Extreme Climate Impacts 80
2  Impact and Comparison under Future Scenarios 85
2.1  System Stability and Environmental Risk Drivers Analysis 86
2.2  Weight Sensitivity Analysis 91
3  Comprehensive Discussion of Historical and Future Scenario Changes 93
Chapter 6  Conclusion and Recommendations 100
1  Conclusion 100
2  Recommendations 101
References 103
                                    

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