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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林國峰(Gwo-Fong Lin) | |
| dc.contributor.author | Cheng-Yi Liao | en |
| dc.contributor.author | 廖承億 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:11:48Z | - |
| dc.date.available | 2021-08-24 | |
| dc.date.available | 2022-11-23T09:11:48Z | - |
| dc.date.copyright | 2021-08-24 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-17 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79799 | - |
| dc.description.abstract | 臺灣年平均降雨量豐沛，卻因地形條件使水資源保存不易，而降雨又常集中於夏秋的梅雨與颱風時節，使得臺灣在枯水期用水吃緊。根據臺灣近六十年的降雨量統計顯示，乾旱年的發生頻率與單次影響時間都有增加的趨勢，因此穩定的長期流量預報對水庫操作以及相關單位制定長期用水策略有助。 本研究介接中央氣象局第一代海氣耦合模式，利用K-最近鄰居法將全球的雨量及溫度預報降至集水區尺度作為人工智慧建置之降雨逕流模式的輸入。建置模式的人工智慧有機器學習法二種：支持向量機與多層感知器，以及深度學習法四種：深度神經網路、遞迴神經網路、長短期記憶以及門閘遞迴單元。模式用以預報臺灣中南部六個重要水庫及攔河堰未來180日的入流量，透過平均值、標準差、偏態係數、降雨機率、均方根誤差和平均絕對誤差等評鑑指標，選出該集水區表現最佳的長期流量預報模式。結果顯示，六個集水區在雨量及溫度皆以K = 1的降尺度模式表現最佳，表示其降雨及溫度長期變化較穩定且單一。六個集水區於枯水期與豐水期所建置的模式，深度學習法的預報趨勢和評鑑指標表現較機器學習法好，又大多數研究區域係採用深度神經網路建置長期流量預報模式。 本研究提出的長期流量預報模式，針對各個地區條件不同而有不同的建模方式，產出穩定且準確的預報結果，可供相關單位作為水庫操作及長期用水策略之參考依據。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:11:48Z (GMT). No. of bitstreams: 1 U0001-0908202114045000.pdf: 33075200 bytes, checksum: 37667d0c5a6356fe521c4f23b0285799 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 I 誌謝 II 摘要 III Abstract IV 目錄 VI 圖目錄 IX 表目錄 XII 第一章 緒論 1 1.1 研究動機與目的 1 1.2 文獻回顧 2 1.2.1 降尺度模式 2 1.2.2 機器學習建立降雨逕流模式 4 1.3 論文架構 6 第二章 研究區域與資料 7 2.1 研究區域概述及觀測資料 7 2.1.1 曾文水庫 7 2.1.2 八掌溪攔河堰 9 2.1.3 南化水庫 11 2.1.4 甲仙攔河堰 13 2.1.5 月眉攔河堰 15 2.1.6 牡丹水庫 17 2.2 第一代海氣耦合模式 19 第三章 研究方法 21 3.1 K-最近鄰居法 21 3.2 機器學習法 22 3.3 深度學習法 24 3.4 網格搜尋法 27 第四章 模式建立與應用 29 4.1 研究流程 29 4.2 評鑑指標 31 第五章 結果與討論 33 5.1 雨量降尺度 33 5.1.1 曾文水庫 34 5.1.2 八掌溪攔河堰 41 5.1.3 南化水庫 47 5.1.4 甲仙、月眉攔河堰 52 5.1.5 牡丹水庫 61 5.1.6 小結 65 5.2 溫度降尺度 66 5.3 長期流量預報模式 75 5.3.1 曾文水庫 75 5.3.2 八掌溪攔河堰 81 5.3.3 南化水庫 85 5.3.4 甲仙攔河堰 89 5.3.5 月眉攔河堰 93 5.3.6 牡丹水庫 97 5.3.7 小結 101 第六章 結論與建議 102 6.1 結論 102 6.2 建議 104 參考文獻 105 附錄A 112 附錄B 125 附錄C 133 | |
| dc.language.iso | zh-TW | |
| dc.subject | 遞迴神經網路 | zh_TW |
| dc.subject | K-最近鄰居法 | zh_TW |
| dc.subject | 長期流量預報 | zh_TW |
| dc.subject | 第一代海氣耦合模式 | zh_TW |
| dc.subject | 深度神經網路 | zh_TW |
| dc.subject | K-nearest neighbor method | en |
| dc.subject | recurrent neural network | en |
| dc.subject | deep neural network | en |
| dc.subject | Central Weather Bureau One-Tier Forecast System | en |
| dc.subject | long-term streamflow forecast | en |
| dc.title | 以人工智慧介接第一代海氣耦合模式輸出發展長期流量預報 | zh_TW |
| dc.title | Long-term streamflow forecasting using an AI-based rainfall-runoff model with TCWB1T1 output | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李方中(Hsin-Tsai Liu),賴進松(Chih-Yang Tseng),林文欽 | |
| dc.subject.keyword | K-最近鄰居法,長期流量預報,第一代海氣耦合模式,深度神經網路,遞迴神經網路, | zh_TW |
| dc.subject.keyword | K-nearest neighbor method,long-term streamflow forecast,Central Weather Bureau One-Tier Forecast System,deep neural network,recurrent neural network, | en |
| dc.relation.page | 150 | |
| dc.identifier.doi | 10.6342/NTU202102208 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-17 | |
| 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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