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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 郭鴻基 | zh_TW |
| dc.contributor.advisor | Hung-Chi Kuo | en |
| dc.contributor.author | 游廷碩 | zh_TW |
| dc.contributor.author | Ting-Shuo Yo | en |
| dc.date.accessioned | 2025-08-22T16:04:37Z | - |
| dc.date.available | 2025-08-23 | - |
| dc.date.copyright | 2025-08-22 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-13 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99297 | - |
| dc.description.abstract | 我們提出一個創新的多模態機器學習架構，將機器學習轉型為科學發現工具，並以大台北地區的劇烈降水預報作為測試。我們的研究整合了多種氣象資料，包括再分析資料、雷達資料、衛星影像和探空資料，透過四個已發表和發表中的研究，探討各種資料型態各自在降水預報中的應用與限制。
在個別資料模態研究方面，本論文指出機器學習結合再分析資料能有效預測鋒面、颱風和豪雨事件，其中豪雨預報命中率達78-83%。在雷達資料方面，基於深度學習的體到點（Volume-to-Point, VTP）架構在強降雨偵測上表現卓越，平均命中率達0.8，優於傳統方法。而針對衛星影像的表徵學習顯示，卷積自動編碼器（convolutional autoencoder, CAE）在多種天氣事件分類中表現最佳，但在豪雨預測方面受限於單一時間點資料的不足。探空資料研究則證明，低成本的storm tracker探空儀器經過校正後，能在低對流層提供可靠的高時間-空間解析度觀測，有助於理解與劇烈降水相關的深對流發展。 論文進一步測試了整合三種資料（再分析、雷達、衛星）的多模態預報架構，初步結果顯示，次日劇烈降水預報的命中率為0.71，且再分析資料在預報中顯示出較高的重要性 。總體而言，本研究強調了多模態機器學習在提升劇烈降水預報準確性方面的巨大潛力，並開闢了利用模型可解釋性進行科學探索的新途徑 。 | zh_TW |
| dc.description.abstract | This dissertation presents an innovative multimodal machine learning framework designed to enhance severe rainfall forecasting in the greater Taipei area, thereby transforming machine learning into a tool for scientific discovery. The research integrates diverse meteorological datasets, including reanalysis data, radar data, satellite imagery, and sounding data, exploring their applications and limitations in precipitation forecasting.
In the individual data modality studies, the thesis demonstrates that machine learning combined with reanalysis data can effectively predict frontal, typhoon, and heavy rainfall events, achieving a hit rate of 78-83% for heavy rainfall forecasts. For radar data, the deep learning-based "volume-to-point" (VTP) architecture shows significant superiority in detecting heavy rainfall, with an average hit rate of 0.8, outperforming traditional methods. Representation learning from satellite imagery reveals that the Convolutional Autoencoder (CAE) performs best in classifying various weather events; however, its effectiveness in predicting heavy rainfall is limited by the relevancy between the daily infrared images and small-scale convective cells. The sounding data study confirms that, after calibration, the low-cost Storm Tracker radiosonde can provide reliable, high spatio-temporal resolution observations in the lower troposphere, aiding in the understanding of deep convection development associated with severe rainfall. The dissertation further tests a multimodal forecasting framework integrating three data types (reanalysis, radar, satellite). Preliminary results show a next-day severe rainfall prediction hit rate of 0.71, with reanalysis data demonstrating higher importance in the forecast. Overall, this research highlights the significant potential of multimodal machine learning in enhancing the accuracy of severe rainfall forecasting and opens new avenues for scientific exploration through model interpretability. | en |
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| dc.description.provenance | Made available in DSpace on 2025-08-22T16:04:37Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii 英文摘要 Abstract iii 目次 iv 圖次 x 表次 xv 第一章 前言：科學與機器學習 1 1.1 人工智慧浪潮下的科學研究 2 1.1.1 資料分析與型態識別（Pattern Recognition）的強大工具 2 1.1.2 建立預測模型的方法 2 1.1.3 探勘資料內部結構與關聯性的架構 4 1.1.4 作為傳統科學方法的補充 5 1.2 可解釋的人工智慧 （Explainable AI） 6 1.2.1 可詮釋性（interpretability）：揭示模型的運作機制（how） 7 1.2.2 可解釋性（explainability）：證明模型決策的合理性（why） 8 1.3 多模態機器學習 （Multimodal Machine Learning） 9 1.3.1 多模態機器學習簡介 9 1.3.2 多模態機器學習中的資料融合（Data Fusion） 11 1.4 以多模態架構進行劇烈降水預報 12 1.4.1 結合可解釋人工智慧作為科學發現的工具 13 1.5 本論文的架構 15 第二章 以再分析資料預測綜觀天氣事件 17 2.1 研究導論 18 2.2 資料來源 19 2.2.1 鋒面事件 19 2.2.2 颱風事件 19 2.2.3 豪雨事件 19 2.2.4 再分析資料 20 2.3 分析方法 20 2.3.1 輸入與輸出資料 20 2.3.2 特性層的選擇 21 2.3.3 資料維度降減 21 2.3.4 選擇天氣事件辨識的演算法 22 2.4 實驗結果 23 2.4.1 二元預測結果的評估指標 23 2.4.2 交叉驗證與實驗結果 25 2.5 討論與結論 29 2.5.1 鋒面系統的識別 29 2.5.2 熱帶氣旋的檢測 30 2.5.3 豪雨事件的辨識 30 2.5.4 總結與未來展望 30 第三章 基於深度學習的雷達定量降水估計 32 3.1 研究背景 32 3.1.1 雷達與降水估計 33 3.1.2 Z-R 關係式與調整方法 33 3.1.3 體到點架構（volume-to-point, VTP） 34 3.2 資料與研究方法 35 3.2.1 資料集介紹 35 3.2.2 體對點的預測架構 38 3.2.3 評估方法 41 3.3 研究結果 42 3.3.1 結果分析：整體結果與地理位置 42 3.3.2 強降雨事件偵測能力 44 3.3.3 特殊個案：梅姬颱風期間的 VTP 表現 46 3.4 結論 48 3.4.1 敏感度測試 48 3.4.2 擴展 VTP 架構 48 3.4.3 VTP 架構在降水預報中的應用與潛力 49 3.4.4 總結 50 第四章 衛星影像的表徵學習 52 4.1 研究導論 52 4.2 方法 55 4.2.1 主成分分析 55 4.2.2 自動編碼器 56 4.2.3 預訓練模型 57 4.3 資料與實驗設計 57 4.3.1 GridSat-B1 CDR 57 4.3.2 天氣事件 58 4.3.3 實驗設計 60 4.4 結果 61 4.4.1 實驗 1：基準線 61 4.4.2 實驗 2：衛星影像的解析度 63 4.4.3 實驗 3：潛在空間的大小 66 4.5 討論與結論 68 4.5.1 潛在空間的重構 69 4.5.2 表徵的可解釋性 69 4.5.3 計算成本 72 4.5.4 總結 73 第五章 多模態預報架構的概念驗證 75 5.1 資料型態與劇烈降水 75 5.1.1 再分析資料 75 5.1.2 雷達資料 76 5.1.3 衛星影像 76 5.2 多模態預報架構的概念驗證 77 5.2.1 資料體（原始輸入） 78 5.2.2 預報（輸出） 78 5.2.3 資料預處理 78 5.2.4 多模態融合（multimodal fusion）與可解釋的預測模型 79 5.2.5 評估多模態預報架構 80 5.3 未來展望 81 5.4 總結 84 參考文獻 86 附錄一：探空資料與機器學習 105 A1.1 研究引言 106 A1.2 資料與前處理 111 A1.2.1 資料收集 111 A1.2.2 同步施放資料的前處理 112 A1.3 資料校正方法 117 A1.3.1 基於CDF的機率匹配 118 A1.3.2 通用線性模型 121 A1.4 結果 123 A1.5 討論 128 A1.5.1 ST的隨機誤差 128 A1.5.2 ST的總體性能 131 A1.5.3 ST在午後雷暴研究中的應用 136 A1.5.4 ST在海洋邊界層觀測研究中的應用 136 A1.6 結論 138 附錄二：給科學家的機器學習快速上手指南 140 A2.1. 科學與機器學習 140 A2.1.1 對科學家而言，機器學習是什麼？ 142 A2.1.2 科學家為什麼要關心機器學習？ 146 A2.1.3 這篇指南的目標與範圍 147 A2.2. 機器學習簡史：從概念到實踐 148 A2.2.1 「人工智慧」的誕生與早期的構思（1950年以前） 148 A2.2.2 早期 AI 的興起與沒落（1950s - 1970s） 150 A2.2.3 「專家系統」與第二波 AI 寒冬（1980s - 1990s） 154 A2.2.4 機器學習的再興：統計學習與資料探勘（1990s - 2010） 158 A2.3. 新典範：深度學習革命 162 A2.3.1 類神經網路的回歸：反向傳播演算法的突破 162 A2.3.2 何謂深度神經網路？ 163 A2.3.3 深度學習的重要架構 166 A2.4. 當前面臨的挑戰與未來的發展方向 174 A2.4.1 了解「黑盒子」：可解釋性的人工智慧（explainable AI） 174 A2.4.2 資料的品質 176 A2.4.3 倫理考量與對社會的影響 178 A2.5. 延伸閱讀 182 A2.5.1 開放式教科書 182 A2.5.2 線上課程 182 A2.5.3 其他資源 183 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 多模態機器學習 | zh_TW |
| dc.subject | 劇烈降水 | zh_TW |
| dc.subject | 探空儀器 | zh_TW |
| dc.subject | 雷達 | zh_TW |
| dc.subject | 衛星影像 | zh_TW |
| dc.subject | 數值模式 | zh_TW |
| dc.subject | radiosonde | en |
| dc.subject | multimodal machine learning | en |
| dc.subject | numerical model | en |
| dc.subject | satellite imaging | en |
| dc.subject | radar | en |
| dc.subject | heavy rainfall | en |
| dc.title | 多模態機器學習架構於大台北地區劇烈降水預報之應用 | zh_TW |
| dc.title | A Multimodal Approach to Severe Rainfall Forecasting in the Taipei Area | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 楊明仁;周仲島;劉清煌;吳健銘;陳柏孚 | zh_TW |
| dc.contributor.oralexamcommittee | Ming-Jen Yang;Ben Jong-Dao Jou;Ching-Hwang Liu;Chien-Ming Wu;Buo-Fu Chen | en |
| dc.subject.keyword | 多模態機器學習,劇烈降水,探空儀器,雷達,衛星影像,數值模式, | zh_TW |
| dc.subject.keyword | multimodal machine learning,heavy rainfall,radiosonde,radar,satellite imaging,numerical model, | en |
| dc.relation.page | 183 | - |
| dc.identifier.doi | 10.6342/NTU202504003 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-14 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 大氣科學系 | - |
| dc.date.embargo-lift | 2030-08-05 | - |
| Appears in Collections: | 大氣科學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-113-2.pdf Restricted Access | 4.86 MB | Adobe PDF | View/Open |
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