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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林國峰(Gwo-Fong Lin) | |
| dc.contributor.author | Jhao-Yu Chen | en |
| dc.contributor.author | 陳昭宇 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:12:13Z | - |
| dc.date.available | 2021-09-01 | |
| dc.date.available | 2022-11-23T09:12:13Z | - |
| dc.date.copyright | 2021-09-01 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-10 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79810 | - |
| dc.description.abstract | 本研究主要目的在於建立桃園地下水位長期預報模式。第一，以氣象局一步法海氣耦合氣候模式(TCWB1T1)，配合桃園地區氣象站資料，以最近鄰居法將大尺度日雨量尺度降至流域尺度。第二，使用傅立葉轉換及小波轉換，分析地下水抽補強度。最後使用第一步的雨量與第二步的抽補強度和30口井水位資料，建置準確且穩定的地下水水位長期預報模式，提供機率式預報和優選後的定率式預報。 本研究使用支援向量機進行預報。第一含水層輸入因子為雨量、平均抽補強度和自身地下水水位；第二至第四含水層使用附近上一層觀測井或同層較上游觀測井的預報結果代替雨量，因此第二到四層使用鄰近扇頂的觀測井水位、淺層觀測井水位、抽補強度和自身的水位進行建模；最後結合多步階預報，透過反覆迭代預報出的地下水位作為輸入項預報出未來180日的地下水位。 本研究中之雨量降尺度為機率式預報，結果顯示絕大多數觀測雨量都在本研究提出之預報範圍內。而地下水位預報因旱季降雨和地下水位的不確定性都較小，因此旱季比溼季更準確；此外，淺層水位資料較深層更多更完整，淺層預報結果較深層佳。本研究預報較為長期，在選擇定率式預報時，較難在旱季時選擇出符合未來180日後濕季的定率式預報，因此時間越長機率式預報的參考性會越高。本研究所發展之地下水位預報模式穩定且準確，可提供未來長地下水位趨勢預判資訊，作為抗旱期間相關因應措施之決策輔助參考。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:12:13Z (GMT). No. of bitstreams: 1 U0001-0708202102412200.pdf: 13303468 bytes, checksum: 435eafad6a0eb68e33af03741f1cd0a8 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 誌謝 iii 中文摘要 iii Abstract iv 圖目錄 viii 表目錄 xii 一、 緒論 1 1.1 研究動機與目的 1 1.2 文獻回顧 3 1.2.1 地下水位預報 3 1.2.2 訊號分析應用方法 5 1.3 論文架構 7 二、 研究區域與資料 8 2.1 研究區域 8 2.1.1 研究區域位置與地形 8 2.1.2 研究區域地質特性 14 2.1.3 研究區域地下水使用情況 18 2.2 研究資料 19 2.2.1 觀測井水位資料 19 2.2.2 氣象站資料 20 2.2.3 天氣預報資料 22 三、 研究方法 23 3.1 抽補強度分析 23 3.1.1 傅立葉轉換 23 3.1.2 小波轉換 25 3.2 空間尺度KNN 26 3.3 預報模式 28 3.3.1 支援向量機SVM 28 3.3.2 多步階預報MSF 30 3.4 研究流程與評鑑指標 31 3.4.1 研究流程 31 3.4.2 評鑑指標 33 四、 結果與討論 35 4.1 抽補強度結果 35 4.1.1 傅立葉分析結果 35 4.1.2 小波分析結果 40 4.1.3 傅立葉與小波分析的驗證與比較 48 4.2 KNN雨量降尺度結果 51 4.2.1 KNN驗證結果 51 4.2.2 KNN未來雨量繁衍結果 54 4.3 地下水預報結果 56 4.3.1 輸入項相關性分析 56 4.3.2 模式驗證結果 60 4.3.3 水位預報結果 66 五、 結論與建議 82 5.1 結論 82 5.2 建議 83 參考文獻 84 | |
| dc.language.iso | zh-TW | |
| dc.subject | 地下水位預報 | zh_TW |
| dc.subject | 支援向量機 | zh_TW |
| dc.subject | 抽補強度 | zh_TW |
| dc.subject | 最近鄰居法 | zh_TW |
| dc.subject | 多步階預報 | zh_TW |
| dc.subject | Support vector machine | en |
| dc.subject | KNN | en |
| dc.subject | Pumping recovery strength | en |
| dc.subject | Groundwater level | en |
| dc.subject | Multi-step forecasting | en |
| dc.title | 結合第一代海氣耦合模式和機器學習發展長期地下水預報 | zh_TW |
| dc.title | Long-term groundwater level forecasting based on the integration of TCWB1T1 output and machine learning | 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 | 地下水位預報,抽補強度,多步階預報,最近鄰居法,支援向量機, | zh_TW |
| dc.subject.keyword | Groundwater level,Pumping recovery strength,KNN,Multi-step forecasting,Support vector machine, | en |
| dc.relation.page | 89 | |
| dc.identifier.doi | 10.6342/NTU202102170 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-10 | |
| 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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