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系統識別號 U0026-1708202022345900
論文名稱(中文) 基因遺傳演算法應用於土砂收支模型參數最佳化之研究
論文名稱(英文) Applied genetic algorithm on parameters optimization of the sediment budget model
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 108
學期 2
出版年 109
研究生(中文) 王上銘
研究生(英文) Shang-Ming Wang
學號 NC6071065
學位類別 碩士
語文別 英文
論文頁數 157頁
口試委員 指導教授-謝正倫
口試委員-蔡元芳
口試委員-曾志民
口試委員-蔡元融
中文關鍵字 類神經網絡  堆疊式稀疏自動編碼器  流域土砂收支模型  基因遺傳算法  參數最佳化  多站 
英文關鍵字 artificial neural network  stacked sparse autoencoders  sediment budget model  genetic algorithm  optimization  multi-sites 
學科別分類
中文摘要 近年來,氣候變遷導致極端降雨事件頻繁的發生,並且加上降雨事件的短延時強降雨的特性下,導致集水區流域產生嚴重的土壤沖蝕與坡面崩塌的發生。降雨產生之逕流搬運行為觸發坡面不安定的土體造成崩塌、土石流與土壤沖蝕的土壤量沿著坡面-河道並最後進入水庫中。這導致水庫庫底之淤積量增加,降低水庫蓄水能力,並增加水庫大壩結構的不穩定性。
過去,流域土砂收支模型最常用於表示與推估整體流域內空間上的水文與土砂生產的行為。然而,對於模擬參數設定的合理性與準確性對於物理性的土砂收支模型是困難的過程。通常會使用最佳化的演算法進行模型參數的率定校正。本研究透過衛星影像分類與類神經推估模型並搭配前人研究推估之經驗轉換迴歸公式,對於未觀測或未實地調查的地區進行參數的推估。
過往的研究主要將整體流域視為單個計算單元,並透過參數最佳化來表示整個流域的水文行為,但是基於空間的變異性的影響,較不能完整的表示流域的水文行為,因此單一集水區參數最佳化仍然受到限制。因此本研究主要針對多個子集水區的參數最佳化的分析。透過基因遺傳演算法為每個獨立的子集水區進行參數的最佳化計算後,並討論了參數設定的不確定性分析。
對於降雨逕流參數最佳化過程,透過大數據的概念將多場降雨件資料進行組合建立模擬資料庫。過程中主要將降雨資料庫主要分為率定與驗證等二組,使用率定資料集透過基因遺傳演算法進行參數最佳化資料集的建構,過程中主要使用了兩種不同的目標函數設定為參數選擇標準。經過率定建構最佳化參數資料後,將使用二場降雨事件進行資料驗證。
而對於土砂生產參數最佳化過程,則根據觀測數據選擇三個不同時間的水庫庫區地形量測結果,並分別計算時間周期內的庫區土砂體積變化情況作為最佳化計算的目標。透過此三個時間的土砂變化量進行參數土砂生產最佳化的計算。最後透過2020/05/22降雨事件之上游兩處觀測資料進行驗證。
經土砂收支模型參數最佳化計算結果顯示,對於降雨逕流參數的表現優異,而土砂生產的評估則顯示,觀測數據與模擬數據的分佈趨勢一致。
通過現地觀測數據的整合,本研究中使用的多期降雨模擬數據被用於流域土砂收支模型最佳化參數。這項研究估計了土砂產生和入庫的徑流量,可作為未來評估土砂危害影響的參考。
英文摘要 In recent years, climate change has led to an increase in the frequency of extreme rainfall events, coupled with the short and rapid concentration of rainfall events, have led to severe erosion and landslides of soils and sediment in the catchment, producing sediments that are transported to the reservoir by water transportation, causing increased deposition, reducing the reservoir water storage capacity, and increasing the instability of the reservoir dam structure.
In the past, sediment budget models were most often used to spatially distribute runoff and sediment discharge throughout the watershed, however, the setting of parameters for these simulations is difficult. Therefore, the optimization algorithm is used to help the sediment budget model for parameter optimization. For parameter data pre-processing stage, image classification, predictive model and experience transformation equations are used to obtain the unobserved area data respectively. However, previous studies rarely discuss independent parameter optimization for multiple sub-catchments, so this study uses a genetic algorithm to calculate independent parameter optimizations for each sub-catchment and creates an uncertainty analysis to discuss the uncertainty of parameter settings for each sub-catchment.
For the runoff aspect, the parameter optimization is mainly based on the concept of large data, where multiple rainfall events are combined to create simulated data for parameter search. The rainfall data are divided into two main groups: calibration and validation. After creating the optimal set of runoff discharge parameters from the calibration data set, the applicability of the optimized parameter set is discussed using the validation data. The results show that the most effective parameter solution for the runoff discharge is to use the Nash-Sutcliffe coefficient as the parameter set for the fitting function. For sediment discharge, three reservoir topography measurements at different times were selected based on the observed data and the estimated sediment volume change over time was used as a target for calibration calculations to calculate the optimal parameters for sediment discharge, which were finally validated by the 2020/05/22 rainfall event.
The results of the optimization calculations show that the evaluation performance for rainfall runoff discharge is excellent, while the results for sediment discharge show that the observed data is consistent with the distribution trend of the simulated data.
論文目次 摘要 I
ABSTRACT II
致謝 III
ACKNOWLEDGEMENTS IV
TABLE OF CONTENTS V
LIST OF FIGURES VII
LIST OF TABLES X
LIST OF SYMBOLIC TABLES XII
CHAPTER 1 INTRODUCTION 1
1.1 Research Motivation 1
1.2 Research Objectives 4
1.3 Research Region 5
1.4 Research Process 10
CHAPTER 2 LITERATURE REVIEW 12
2.1 Autoencoders Based on Image Classification 12
2.2 Hydrological Models Optimization 15
2.3 Overview of the Sediment Budget Model 18
2.4 Overview of the Soil Erosion Model 23
CHAPTER 3 RESEARCH METHODOLOGY 25
3.1 Integrate Sediment Management Model (ISMM) 25
3.1.1 Rainfall-runoff process 26
3.1.2 Sediment production process 28
3.1.3 Sediment transportation process 31
3.2 Genetic Algorithms (GA) 34
3.3 Artificial Neural Network (ANN) 39
3.4 Stacked Autoencoders (SAEs) 44
CHAPTER 4 OPTIMIZATION OF SEDIMENT BUDGET MODEL 49
4.1 Construction of optimization Framework 49
4.2 Basic Database Collection and Creation 52
4.2.1 Selection natural environment factors 52
4.2.2 Selection of land cover factors affecting runoff 56
4.2.3 Selection sediment production factors 76
4.2.4 Selection hydrology factors 94
4.2.5 Summary of physiographic condition 99
4.3 Sediment Budget Parameters Optimization 101
4.3.1 Evaluation criteria of hydrology model 104
4.3.2 Observed runoff and sediment discharge 108
4.3.3 Selection parameters for optimization 117
4.3.4 The effect of genetic algorithm parameters on runoff 118
4.3.5 Optimization runoff discharge of single rainfall event 120
4.3.6 Optimization runoff of multiple rainfall events 123
4.3.7 Optimization sediment discharge 129
CHAPTER 5 CONCLUSIONS AND SUGGESTIONS 135
5.1 Conclusions 135
5.2 Suggestions 137
REFERENCES 138
Appendix A 148
Appendix B 150
Appendix C 154
Curriculum Vitae 157
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19. Shieh, M. C. & Huang, H. P., (2007). Estimation of Mixed-Grain Sediment Discharge (混合粒徑輸砂量估算之研究). Journal of Chinese Soil and Water Conservation, 38 (4), 349-371.
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23. Su, C. C. (2013), Analysis and Construction of Evaluation Model for Rainfall-induced Soil Erosion in Watersheds (集水區降雨誘發土壤沖蝕之探討及其評估模式之建置), National Cheng Kung University, Master Thesis.
24. Su, M. C. & Chang, X. D. (2007). Machine Learning: Classical Neural Networks. Fuzzy systems and genetic algorithms (機器學習 : 類神經網路、模糊系統以及基因演算法則). Taipei. San Min Book Inc.
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【Research Report References】
1. Ministry of Science and Technology (MOST) Project, (2017). Risk management model and system for slope hazards in catchment areas- A study of numerical catchment modelling (集水區坡地災害風險管理模式及系統建立-總計畫暨子計畫:數值集水區模型建立之研究). Research Report.
2. Water Resources Agency, Ministry of Economic Affairs (MOEA), (2012) Investigation of stream status of Agongdian River (阿公店及牡丹水庫抽泥入海方案初步規劃_成果摘要結論與建議) . Research Report.
3. Water Resources Agency, Ministry of Economic Affairs (MOEA), (2014). The observed empty flushing operation of Agondian Reservoir report research report (阿公店水庫空庫防淤泥砂觀測及抽泥放淤可行性評估) . Research Report.
4. Water Resources Agency, Ministry of Economic Affairs (MOEA), (2015). The observed empty flushing operation of Agondian Reservoir report research report (阿公店水庫空庫防淤泥砂觀測及抽泥放淤可行性評估) . Research Report.
5. Water Resources Agency, Ministry of Economic Affairs (MOEA), (2017). The observed empty flushing operation of Agondian Reservoir report research report (阿公店水庫空庫防淤泥砂觀測及抽泥放淤可行性評估) . Research Report.
6. Water Resources Agency, Ministry of Economic Affairs (MOEA), (2018). Hydrological year book of Taiwan republic of china 2018 (水文年報).
7. Water Resources Agency, Ministry of Economic Affairs (MOEA), (2019). The observed empty flushing operation of Agondian Reservoir report research report (阿公店水庫空庫防淤泥砂觀測及抽泥放淤可行性評估) . Research Report.
【Web References】
1. Central Weather Bureau (2010). Monthly Mean- Mean temperature. https://www.cwb.gov.tw/V8/E/C/Statistics/monthlymean.html.
2. Central Weather Bureau. (2017). Meteorology Encyclopedia. https://www.cwb.gov.tw/.
3. Chen, S. J. (2005). Foundations of Genetic Algorithm (遺傳演算法基礎). National United University.
4. MathWorks (Matlab). (2020). Train an autoencoder (2020a). The MathWorks, Inc. https://www.mathworks.com.
5. MathWorks (Matlab). (2020). Neural Network Toolbox (2020a). The MathWorks, Inc. https://www.mathworks.com.
6. Ministry of Science and Technology. (2017). Disaster Risk and Application. http://dmip.tw/Lthree/2017/.
7. Water resources agency, MOEA (2017). Agongdian Reservoir control center. https://www.wrasb.gov.tw/news/news01_detail.aspx?no=15&nno=2018052201.
8. Water resources agency, MOEA (2019). Agongdian Reservoir. https://www.wra.gov.tw/6950/7170/7356/14288/15541/.
9. WEPP. (2020). Watershed Erosion Prediction Project (WEPP). https://www.fs.usda.gov/ccrc/tools/watershed-erosion-prediction-project.
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