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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林國峰 | |
| dc.contributor.author | Chia-Chuang Chang | en |
| dc.contributor.author | 張家銓 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:42:36Z | - |
| dc.date.available | 2009-07-16 | |
| dc.date.copyright | 2009-07-16 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-13 | |
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'Application of an Artificial Neural Network to Typhoon Rainfall Forecasting.' Hydrological Processes 19(9): 1825-1837. 19. Lin, G.F., Chen, L.H., (2005b). 'Time Series Forecasting by Combining the Radial Basis Function Network and the Self-organizing Map.' Hydrological Processes 19(10): 1925-1937. 20. Lin, G.F., Wang, C.M., (2006). 'Performing Cluster Analysis and Discrimination Analysis of Hydrological Factor in One Step.' Advances in Water Resources 29(11): 1573-1585. 21. Lin, G.F., Wang, C.M., (2007a). 'A Nonlinear Rainfall-runoff Model Embedded with an Automated Calibration Method. Part 1. The Model.' Journal of Hydrology 341(3-4): 186-195. 22. Lin, G.F., Wang, C.M., (2007b). 'A Nonlinear Rainfall-runoff Model Embedded with an Automated Calibration Method. Part 2. The Automated Calibration Method.' Journal of Hydrology 341(3-4): 196-206. 23. Lin, G.F., Wu, M.C., (2007). 'A SOM-based Approach to Estimating Design Hyetographs of Ungauged Sites.' Journal of Hydrology 339(3-4): 216-226. 24. Lin, G.F., Chen, G.R., (2008). 'A Systematic Approach to the Input Determination for Neural Network Rain-Runoff Models.' Hydrological Processes 22(14): 2524-2530. 25. Lin, G.F., Chen, G.R., Huang, P.Y., Chou, Y.C., (2009). 'Support Vector Machine-based Models for Hourly Reservoir Inflow Forecasting during Typhoon-Warning Periods.' Journal of Hydrology 372(1-4): 17-29. 26. Maidment, D.R., (1992). 'Handbook of Hydrology.' McGraw Hill: 17.26-17.48. 27. Maier, H.R., Dandy, G.C., (2000). 'Neural Network for the Prediction and Forecasting of Water Resources Variables: A Review of Modelling Issues and Application.' Environmental Modelling & Software 15: 101-124. 28. Moradkhani, H., Hsu, K., Gupta, H.V., (2004). 'Improved Streamflow Forecasting Using Self-organizing Radial Basis Function Artificial Neural Networks.' Journal of Hydrology 295(1-4): 246-262. 29. Parasuraman, K., Elshorbagy, A., Carey, S.K., (2006). 'Spiking Modular Neural Networks: A Neural Network Modeling Approach for Hydrological Processes.' Water Resources Research 42(5): W05412. 30. Xu, Z.X., Li, J.Y., (2002). 'Short-time Inflow Forecasting Using an Artificial Neural Network Model.' Hydrology Processes 16(12): 2423-2439. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43206 | - |
| dc.description.abstract | 本研究以自組織映射線性輸出模式(Self-organizing Linear Output Map, SOLO)架構一個有效的水庫時入流量預報模式。在類神經網路領域裡，倒傳遞類神經網路模式(Back-propagation Neural Network, BPNN) 被廣泛使用。相對於BPNN而言，SOLO模式的優勢在於：(1)準確度高、(2)架構簡單、(3)訓練所需時間少、(4)有助於分析。為了展示上述的四個優勢，本研究將SOLO應用於翡翠水庫入流量預報，研究結果顯示SOLO的效能及效率比BPNN來得佳。又為了改善SOLO尖峰入流量的預報，本研究進一步加入資料前處理的步驟以改良SOLO，並命名為改良式自組織映射線性輸出模式(Improved Self-organizing Linear Output Map, ISOLO)。研究成果證實ISOLO可以明顯改善SOLO尖峰入流量的預報。因此，建議可以ISOLO作為現有模式的替代方案，其優異的預報能力對水庫操作也相當有幫助。 | zh_TW |
| dc.description.abstract | Based on self-organizing linear output map (SOLO), effective hourly reservoir inflow forecasting models are proposed. As compared with back-propagation neural network (BPNN) which is the most frequently used conventional neural network (NN), SOLO has four advantages: (1) SOLO has better generalization ability; (2) the architecture of the SOLO is simpler; (3) SOLO is trained much more rapidly, and (4) SOLO could provide features that facilitate insight into underlying processes. An application is conducted to clearly demonstrate these four advantages. The results indicate that the SOLO model is more well-performed and efficient than the existing BPN-based models. To further improve the peak inflow forecasting, SOLO with data preprocessing named ISOLO is also proposed. The comparison between SOLO and ISOLO confirms the significant improvement in peak inflow forecasting. The proposed model is recommended as an alternative to the existing models. The proposed modeling technique is also expected to be useful to support reservoir operation systems. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:42:36Z (GMT). No. of bitstreams: 1 ntu-98-R96521321-1.pdf: 3727417 bytes, checksum: 95d4cf930ffff0cea4c0d690f0e990e0 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | 誌謝 I
摘要 II ABSTRACT III 目錄 IV 圖目錄 VI 第 1 章 緒論 1 1 - 1 前言與目的 1 1 - 2 文獻回顧 3 第 2 章 模式方法 5 2 - 1 倒傳遞類神經網路模式(BPNN) 5 2 - 2 自組織映射線性輸出模式(SOLO) 8 2 - 3 改良式自組織映射線性輸出模式(ISOLO) 13 第 3 章 模式建立與應用 20 3 - 1 研究區域與資料 20 3-1-1 翡翠水庫 20 3-1-2 水文資料 21 3 - 2 交替驗證與評鑑指標 23 3-2-1 交替驗證 23 3-2-2 評鑑指標 23 3 - 3 模式參數及輸入項設定 27 3-3-1 輸入項設定 27 3-3-2 BPNN參數設定 27 3-3-3 SOLO及ISOLO參數設定 28 第 4 章 結果與討論 37 4 - 1 異分佈分析 37 4 - 2 自組織映射拓撲分析 39 4 - 3 模式效能比較 41 4-3-1 預報準確度 41 4-3-2 訓練所需時間 43 第 5 章 結論與建議 75 5 - 1 結論 75 5 - 2 建議 78 參考文獻 79 | |
| dc.language.iso | zh-TW | |
| dc.subject | 尖峰入流量預報 | zh_TW |
| dc.subject | 自組織映射圖 | zh_TW |
| dc.subject | 水庫入流量預報 | zh_TW |
| dc.subject | 自組織映射線性輸出模式 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | 資料前處理 | zh_TW |
| dc.subject | data preprocessing | en |
| dc.subject | peak inflow forecasting | en |
| dc.subject | self-organizing map | en |
| dc.subject | reservoir inflow forecasting | en |
| dc.subject | self-organizing linear output map | en |
| dc.subject | neural network | en |
| dc.title | 改良式自組織映射線性輸出模式於水庫入流量預報之研究 | zh_TW |
| dc.title | Improved Self-organizing Linear Output Map for Reservoir Inflow Forecasting | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張斐章,陳主惠,曾惠斌 | |
| dc.subject.keyword | 自組織映射線性輸出模式,水庫入流量預報,自組織映射圖,尖峰入流量預報,資料前處理,類神經網路, | zh_TW |
| dc.subject.keyword | self-organizing linear output map,reservoir inflow forecasting,self-organizing map,peak inflow forecasting,data preprocessing,neural network, | en |
| dc.relation.page | 82 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-07-13 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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