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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 游景雲 | zh_TW |
dc.contributor.advisor | Gene J-Y You | en |
dc.contributor.author | 温浚達 | zh_TW |
dc.contributor.author | Chun-Ta Wen | en |
dc.date.accessioned | 2024-01-28T16:10:33Z | - |
dc.date.available | 2024-01-29 | - |
dc.date.copyright | 2024-01-27 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-29 | - |
dc.identifier.citation | 1.Abebe, A., Solomatine, D., & Venneker, R. (2000). Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events. Hydrological sciences journal, 45(3), 425-436.
2.Agency, W. R. (2009). Hydrological Year Book of Taiwan. In: Water Resources Agency Ministry of Economic Affairs Taipei. 3.Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-series clustering–a decade review. Information systems, 53, 16-38. 4.Agrawal, R., Faloutsos, C., & Swami, A. (1993). Efficient similarity search in sequence databases. Foundations of Data Organization and Algorithms: 4th International Conference, FODO'93 Chicago, Illinois, USA, October 13–15, 1993 Proceedings 4, 5.Ahmad, I., Zhang, F., Tayyab, M., Anjum, M. N., Zaman, M., Liu, J., Farid, H. U., & Saddique, Q. (2018). Spatiotemporal analysis of precipitation variability in annual, seasonal and extreme values over upper Indus River basin. Atmospheric Research, 213, 346-360. 6.Alfieri, L., Feyen, L., Dottori, F., & Bianchi, A. (2015). Ensemble flood risk assessment in Europe under high end climate scenarios. Global environmental change, 35, 199-212. 7.Aminikhanghahi, S., & Cook, D. J. (2017). A survey of methods for time series change point detection. Knowledge and information systems, 51(2), 339-367. 8.Arefinia, A., Bozorg-Haddad, O., & Chang, H. (2021). The role of data mining in water resources management. In Essential Tools for Water Resources Analysis, Planning, and Management (pp. 85-99). Springer. 9.Arnell, N. W. (1999). Climate change and global water resources. Global environmental change, 9, S31-S49. 10.Arslan, Y., Birturk, A., & Eren, S. (2015). Basin clustering of Turkey by use of monthly stream-flow data. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 11.Ashraf, M. S., Ahmad, I., Khan, N. M., Zhang, F., Bilal, A., & Guo, J. (2021). Streamflow variations in monthly, seasonal, annual and extreme values using Mann-Kendall, Spearmen’s Rho and innovative trend analysis. Water resources management, 35, 243-261. 12.Babovic, V. (2005). Data mining in hydrology. Hydrological Processes: An International Journal, 19(7), 1511-1515. 13.Babovic, V., & Keijzer, M. (2000). Forecasting of river discharges in the presence of chaos and noise. Flood issues in contemporary water management, 405-419. 14.Biederman, J. A., Somor, A. J., Harpold, A. A., Gutmann, E. D., Breshears, D. D., Troch, P. A., Gochis, D. J., Scott, R. L., Meddens, A. J., & Brooks, P. D. (2015). Recent tree die‐off has little effect on streamflow in contrast to expected increases from historical studies. Water Resources Research, 51(12), 9775-9789. 15.Chen, P.-C., Wang, Y.-H., You, G. J.-Y., & Wei, C.-C. (2017). Comparison of methods for non-stationary hydrologic frequency analysis: case study using annual maximum daily precipitation in Taiwan. Journal of hydrology, 545, 197-211. 16.Chow, V. (1971). Applied hydrology. McGraw-hill. 17.Chu, S., Keogh, E., Hart, D., & Pazzani, M. (2002). Iterative deepening dynamic time warping for time series. Proceedings of the 2002 SIAM International Conference on Data Mining, 18.Corduas, M. (2011). Clustering streamflow time series for regional classification. Journal of hydrology, 407(1-4), 73-80. 19.Curran, J. H., & Biles, F. E. (2021). Identification of seasonal streamflow regimes and streamflow drivers for daily and peak flows in Alaska. Water Resources Research, 57(2), e2020WR028425. 20.Dudley, R. W., Hirsch, R. M., Archfield, S. A., Blum, A. G., & Renard, B. (2020). Low streamflow trends at human-impacted and reference basins in the United States. Journal of hydrology, 580, 124254. 21.Dupas, R., Tavenard, R., Fovet, O., Gilliet, N., Grimaldi, C., & Gascuel‐Odoux, C. (2015). Identifying seasonal patterns of phosphorus storm dynamics with dynamic time warping. Water Resources Research, 51(11), 8868-8882. 22.Eliasson, J. (2015). The rising pressure of global water shortages. Nature, 517(7532), 6-6. 23.Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-37. 24.Forbes, W. L., Mao, J., Ricciuto, D. M., Kao, S. C., Shi, X., Tavakoly, A. A., Jin, M., Guo, W., Zhao, T., & Wang, Y. (2019). Streamflow in the Columbia River Basin: Quantifying changes over the period 1951‐2008 and determining the drivers of those changes. Water Resources Research, 55(8), 6640-6652. 25.Ghimire, G. R., Hansen, C., Gangrade, S., Kao, S. C., Thornton, P. E., & Singh, D. (2023). Insights From Dayflow: A Historical Streamflow Reanalysis Dataset for the Conterminous United States. Water Resources Research, 59(2), e2022WR032312. 26.Gido, K. B., Dodds, W. K., & Eberle, M. E. (2010). Retrospective analysis of fish community change during a half-century of landuse and streamflow changes. Journal of the North American Benthological Society, 29(3), 970-987. 27.Giorgino, T. (2009). Computing and visualizing dynamic time warping alignments in R: the dtw package. Journal of Statistical Software, 31, 1-24. 28.Govindaraju, R. S., & Rao, A. R. (2013). Artificial neural networks in hydrology (Vol. 36). Springer Science & Business Media. 29.Griffiths, A., Robinson, L. A., & Willett, P. (1984). Hierarchic agglomerative clustering methods for automatic document classification. Journal of Documentation. 30.Guo, Y., Li, Z., Amo-Boateng, M., Deng, P., & Huang, P. (2014). Quantitative assessment of the impact of climate variability and human activities on runoff changes for the upper reaches of Weihe River. Stochastic Environmental Research and Risk Assessment, 28, 333-346. 31.Hannah, D. M., Smith, B. P., Gurnell, A. M., & McGregor, G. R. (2000). An approach to hydrograph classification. Hydrological processes, 14(2), 317-338. 32.Hennig, C., Meila, M., Murtagh, F., & Rocci, R. (2015). Handbook of cluster analysis. CRC press. 33.Ibrahim, K. S. M. H., Huang, Y. F., Ahmed, A. N., Koo, C. H., & El-Shafie, A. (2022). A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting. Alexandria Engineering Journal, 61(1), 279-303. 34.Islam, A., Sikka, A. K., Saha, B., & Singh, A. (2012). Streamflow response to climate change in the Brahmani River Basin, India. Water resources management, 26(6), 1409-1424. 35.Jie, C., & Gupta, A. (2000). Parametric statistical change point analysis. Birkh User. 36.Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons. 37.Killick, R., & Eckley, I. (2014). changepoint: An R package for changepoint analysis. Journal of Statistical Software, 58(3), 1-19. 38.Kundzewicz, Z. W., Mata, L., Arnell, N. W., Döll, P., Jimenez, B., Miller, K., Oki, T., Şen, Z., & Shiklomanov, I. (2008). The implications of projected climate change for freshwater resources and their management. Hydrological sciences journal, 53(1), 3-10. 39.Kuo, C.-C., Gan, T. Y., & Yu, P.-S. (2010). Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan. Journal of hydrology, 387(3-4), 292-303. 40.Kustu, M. D., Fan, Y., & Robock, A. (2010). Large-scale water cycle perturbation due to irrigation pumping in the US High Plains: A synthesis of observed streamflow changes. Journal of hydrology, 390(3-4), 222-244. 41.Lee, T.-Y., Chiu, C.-C., Chen, C.-J., Lin, C.-Y., & Shiah, F.-K. (2023). Assessing future availability of water resources in Taiwan based on the Budyko framework. Ecological Indicators, 146, 109808. 42.Li, H., Zhang, Q., Singh, V. P., Shi, P., & Sun, P. (2017). Hydrological effects of cropland and climatic changes in arid and semi-arid river basins: a case study from the Yellow River basin, China. Journal of hydrology, 549, 547-557. 43.Lindsey, R., & Dahlman, L. (2020). Climate change: Global temperature. Climate. gov, 16. 44.Liu, J., Zhang, Q., Singh, V. P., & Shi, P. (2017). Contribution of multiple climatic variables and human activities to streamflow changes across China. Journal of hydrology, 545, 145-162. 45.Liu, Y., Zhang, X., Xia, D., You, J., Rong, Y., & Bakir, M. (2013). Impacts of land-use and climate changes on hydrologic processes in the Qingyi River watershed, China. Journal of Hydrologic Engineering, 18(11), 1495-1512. 46.Lu, M., Xu, Y., Shan, N., Wang, Q., Yuan, J., & Wang, J. (2019). Effect of urbanisation on extreme precipitation based on nonstationary models in the Yangtze River Delta metropolitan region. Science of the Total Environment, 673, 64-73. 47.Mei, H. (2010). Advances in study on water resources carrying capacity in China. Procedia Environmental Sciences, 2, 1894-1903. 48.Mihailović, D. T., Nikolić-Đorić, E., Malinović-Milićević, S., Singh, V. P., Mihailović, A., Stošić, T., Stošić, B., & Drešković, N. (2019). The choice of an appropriate information dissimilarity measure for hierarchical clustering of river streamflow time series, based on calculated Lyapunov exponent and Kolmogorov measures. Entropy, 21(2), 215. 49.Montero, P., & Vilar, J. A. (2015). TSclust: An R package for time series clustering. Journal of Statistical Software, 62, 1-43. 50.Murtagh, F., & Contreras, P. (2011). Methods of hierarchical clustering. arXiv preprint arXiv:1105.0121. 51.Murtagh, F., & Contreras, P. (2012). Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1), 86-97. 52.Olson, C. F. (1995). Parallel algorithms for hierarchical clustering. Parallel computing, 21(8), 1313-1325. 53.Ouyang, R., Ren, L., Cheng, W., & Zhou, C. (2010). Similarity search and pattern discovery in hydrological time series data mining. Hydrological Processes: An International Journal, 24(9), 1198-1210. 54.Pan, V. Y. (1997). Solving a polynomial equation: some history and recent progress. SIAM review, 39(2), 187-220. 55.Rai, P., & Singh, S. (2010). A survey of clustering techniques. International Journal of Computer Applications, 7(12), 1-5. 56.Rumsey, C. A., Miller, M. P., & Sexstone, G. A. (2020). Relating hydroclimatic change to streamflow, baseflow, and hydrologic partitioning in the Upper Rio Grande Basin, 1980 to 2015. Journal of hydrology, 584, 124715. 57.Ryberg, K. R., Hodgkins, G. A., & Dudley, R. W. (2020). Change points in annual peak streamflows: Method comparisons and historical change points in the United States. Journal of hydrology, 583, 124307. 58.Sabzevari, A. A., Zarenistanak, M., Tabari, H., & Moghimi, S. (2015). Evaluation of precipitation and river discharge variations over southwestern Iran during recent decades. Journal of Earth System Science, 124, 335-352. 59.Sadeghi, S., Tootle, G., Elliott, E., Lakshmi, V., Therrell, M., Kam, J., & Bearden, B. (2019). Atlantic Ocean Sea Surface Temperatures and Southeast United States streamflow variability: Associations with the recent multi-decadal decline. Journal of hydrology, 576, 422-429. 60.Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE transactions on acoustics, speech, and signal processing, 26(1), 43-49. 61.Sardá-Espinosa, A. (2017). Comparing time-series clustering algorithms in r using the dtwclust package. R package vignette, 12, 41. 62.Sivakumar, B., Liong, S.-Y., Liaw, C.-Y., & Phoon, K.-K. (1999). Singapore rainfall behavior: chaotic? Journal of Hydrologic Engineering, 4(1), 38-48. 63.Sneath, P., & Sokal, R. (1973). Numerical taxonomy WH freeman and co. San Francisco, 1-573. 64.Sánchez-Gómez, A., Martínez-Pérez, S., Sylvain, L., Sastre-Merlín, A., & Molina-Navarro, E. (2023). Streamflow components and climate change: Lessons learnt and energy implications after hydrological modeling experiences in catchments with a Mediterranean climate. Energy Reports, 9, 277-291. 65.Solomatine, D. (2003). Applications of data-driven modelling and machine learning in control of water resources. In Computational intelligence in control (pp. 197-217). IGI Global. 66.Son, N. T. (2018). Pattern matching under dynamic time warping for time series prediction. Tạp chí Khoa học, 15(3), 148. 67.Stewart, J. (2011). Calculus. Cengage Learning. 68.Sun, G., McNulty, S. G., Lu, J., Amatya, D. M., Liang, Y., & Kolka, R. (2005). Regional annual water yield from forest lands and its response to potential deforestation across the southeastern United States. Journal of hydrology, 308(1-4), 258-268. 69.Tomer, M. D., & Schilling, K. E. (2009). A simple approach to distinguish land-use and climate-change effects on watershed hydrology. Journal of hydrology, 376(1-2), 24-33. 70.Tonkin, J. D., Death, R. G., & Barquín, J. (2014). Periphyton control on stream invertebrate diversity: is periphyton architecture more important than biomass? Marine and Freshwater Research, 65(9), 818-829. 71.Tonkin, J. D., Merritt, D. M., Olden, J. D., Reynolds, L. V., & Lytle, D. A. (2018). Flow regime alteration degrades ecological networks in riparian ecosystems. Nature ecology & evolution, 2(1), 86-93. 72.Truong, C., Oudre, L., & Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167, 107299. 73.Van Ginkel, K. C., Dottori, F., Alfieri, L., Feyen, L., & Koks, E. E. (2021). Flood risk assessment of the European road network. Natural Hazards and Earth System Sciences, 21(3), 1011-1027. 74.Walvoord, M. A., & Striegl, R. G. (2007). Increased groundwater to stream discharge from permafrost thawing in the Yukon River basin: Potential impacts on lateral export of carbon and nitrogen. Geophysical Research Letters, 34(12). 75.Whitfield, P. H., Shook, K., & Pomeroy, J. (2020). Spatial patterns of temporal changes in Canadian Prairie streamflow using an alternative trend assessment approach. Journal of hydrology, 582, 124541. 76.Wobus, C., Porter, J., Lorie, M., Martinich, J., & Bash, R. (2021). Climate change, riverine flood risk and adaptation for the conterminous United States. Environmental Research Letters, 16(9), 094034. 77.Ye, X., Zhang, Q., Liu, J., Li, X., & Xu, C.-y. (2013). Distinguishing the relative impacts of climate change and human activities on variation of streamflow in the Poyang Lake catchment, China. Journal of hydrology, 494, 83-95. 78.Zhang, L., Zhao, F., & Brown, A. (2012). Predicting effects of plantation expansion on streamflow regime for catchments in Australia. Hydrology and Earth System Sciences, 16(7), 2109-2121. 79.Zhang, Q., Gu, X., Singh, V. P., Sun, P., Chen, X., & Kong, D. (2016). Magnitude, frequency and timing of floods in the Tarim River basin, China: Changes, causes and implications. Global and Planetary Change, 139, 44-55. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91477 | - |
dc.description.abstract | 逕流模式流量特性的改變是水資源管理的重要影響因素。近年來,氣候變化不僅導致極端水文事件的強度和頻率增加,也導致人類社會受到負面影響和經濟損失,然而,多數研究著重於極值的趨勢,本研究著重於水文時序特徵的變化。由於降雨的時空分佈不均,台灣的水資源問題更為嚴重。這項研究旨在發現台灣逕流模式的變化和逕流中乾濕時間點變化。本研究提出一個由層次聚類、動態時間扭曲、變化點檢測和總體經驗模態分解法組成的 Hierarchical clustering, Dynamic time warping, Change-point detection, Ensemble empirical mode decomposition (HDCE) 框架 。在此框架下,本研究會先比較不同距離矩陣的層次聚類,透過凝聚指數可得到最優聚類數和最佳層次聚類方法。而動態時間規整在研究中被用來計算時間序列的相似度,此相似度即為距離矩陣的輸入值。在確定此歷史資料集的最優聚類數和最佳層次聚類方法後,因為基於概念假設每年的乾濕變化會造成最大的流量差距,所以選擇使用AMOC(最多一個變化)來分析歷史水文逕流時間序列的結構。對每個水文時間序列使用AMOC檢查最大變化的時間點。最後採用總體經驗模態分解法來判斷轉折時間點的趨勢。本研究利用所提出的框架,將這些方案應用於台灣主要的入流觀測站。在結果討論中本研究將討論翡翠、石門、曾文的逕流模式變化及各站點在轉折時間點變化的趨勢結果。根據本研究結果發現,台灣的逕流模式主要受到各站點的水文條件影響,而轉折點在時間上的趨勢會根據各站而有些許不同。基於本研究的概念假設,轉折點結果表明近年來翡翠的乾濕變化點有提前的趨勢,石門則是有延後的現象,曾文則發現有些許提前趨勢。本研究的結果不僅可用以檢視乾旱資訊,也可望有助於制定抗旱策略。 | zh_TW |
dc.description.abstract | The analysis of streamflow patterns is crucial for effective water resources management. In recent years, climate change has resulted in more frequent and intense hydrological events, leading to unexpected consequences and economic losses. However, limited attention has been addressed to studying the changes in hydrological temporal patterns. This issue is particularly significant in Taiwan due to the uneven distribution of rainfall across different regions and time periods. To address this research gap, an HDCE (Hierarchical Clustering, Dynamic Time Warping, Change Point Detection, and Ensemble Empirical Mode Decomposition) framework is proposed. The framework incorporates various analytical techniques to examine streamflow patterns and identify turning points in hydrological time series data. Hierarchical clustering is utilized, comparing different distance matrices, to determine the optimal number of clusters. The agglomerative coefficient is used to guide this selection process. Dynamic time warping is employed to measure the similarity between time series, providing input values for the distance matrix. Once the optimal clusters are determined for the historical dataset, the AMOC (At Most One Change) approach is applied to analyze the structure of the hydrological streamflow time series. The AMOC assumes that the largest flow difference occurs during wet-dry changes each year. By applying the AMOC to each hydrological time series, the time points of the maximum change are identified. Last, empirical mode decomposition is utilized to identify turning points and determine their trends. The proposed framework is applied to major inflow observation stations in Taiwan, specifically focusing on Feitsui, Shihmen, and Tsengwen. The results and discussions center around streamflow patterns and trends of turning points at these locations. The findings suggest that streamflow patterns in Taiwan are primarily influenced by the hydrological conditions at each station, and the trends of turning points exhibit slight variations among stations. Based on the conceptual assumptions of this study, the identified turning points indicate an advancing trend in wet-dry changes at Feitsui, a lagging phenomenon at Shihmen, and a slight advancement at Tsengwen. These findings not only contribute to drought examination but also have the potential to inform the development of drought mitigation strategies. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-01-28T16:10:32Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-01-28T16:10:33Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iv CONTENTS vi LIST OF FIGURES x LIST OF TABLES xiv Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Objectives 2 1.3 Organization of thesis 3 Chapter 2 Literature Review 5 2.1 Definitions and nomenclature 5 2.2 Water resources management under climate change 6 2.2.1 Different approaches to evaluate the effect of climate change 8 2.2.2 Literature of trend analysis 9 2.3 Data mining in hydrology and water resources 11 2.3.1 Purpose of data mining 11 2.3.2 Literature of data mining tools in hydrology 12 2.4 Pattern recognition: Time series clustering 13 2.4.1 Hierarchical time series clustering 15 2.4.2 Distance measure in time series clustering 16 2.5 Dynamic time warping 17 2.6 Pattern structure: Change point detection 19 2.6.1 Offline change point detection 20 2.6.2 At most one change (AMOC) 21 2.7 Trend detection: EEMD 22 Chapter 3 Methodology 25 3.1 Dynamic time warping 25 3.1.1 Constraints of DTW 30 3.2 Hierarchical clustering 33 3.2.1 Data preprocessing 33 3.2.2 Methods of HCA 34 3.3 Change-point detection 38 3.4 Ensemble empirical mode decomposition (EEMD) 41 3.4.1 Fitted line index 44 3.5 Polynomial equation fitting 46 Chapter 4 Results and discussions 53 4.1 Data collection 53 4.2 Pattern recognition: Hierarchical clustering 57 4.3 Pattern structure: Change point detection 77 4.4 Results of EEMD in changing time and variance 80 4.5 Equation solving for graphing regression line 89 Chapter 5 Conclusions and suggestion 93 5.1 Conclusions 93 5.2 Suggestions 94 References 97 Appendix 106 | - |
dc.language.iso | en | - |
dc.title | 運用 HDCE 方法於流量特性變化之探討 | zh_TW |
dc.title | Investigation of Streamflow Pattern Alterations Using the HDCE Method | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 胡明哲;孫建平;陳憲宗;陳佳正 | zh_TW |
dc.contributor.oralexamcommittee | Ming-Che Hu;Jian-Ping Suen;Shien-Tsung Chen;Chia-Jeng Chen | en |
dc.subject.keyword | 逕流特性,時間序列聚類,階層式分群法,動態時間規整,轉折點測試,總體經驗模態分解法, | zh_TW |
dc.subject.keyword | Streamflow patterns,Time series clustering,Hierarchical clustering,Dynamic Time Warping,Change point detection,Ensemble Empirical Mode Decomposition, | en |
dc.relation.page | 142 | - |
dc.identifier.doi | 10.6342/NTU202302035 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-01 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 土木工程學系 | - |
顯示於系所單位: | 土木工程學系 |
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