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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉格非 | zh_TW |
| dc.contributor.advisor | Ko-Fei Liu | en |
| dc.contributor.author | 張正力 | zh_TW |
| dc.contributor.author | Cheng-Li Chang | en |
| dc.date.accessioned | 2025-08-18T16:12:36Z | - |
| dc.date.available | 2025-08-19 | - |
| dc.date.copyright | 2025-08-18 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-11 | - |
| dc.identifier.citation | [1] Arattano, M., & Marchi, L. (2008). Systems and sensors for debris-flow monitoring and warning. Sensors, 8(4), 2436–2452. https://doi.org/10.3390/s8042436
[2] Fujita, I., Muste, M., & Kruger, A. (1998). Large-scale image velocimetry for flow analysis in hydraulic applications. Journal of Hydraulic Research, 36(3), 397-414. https://doi.org/10.1080/00221689809498626 [3] Fujita, I., Watanabe, H., & Tsubaki, R. (2007). Development of a non-intrusive and efficient flow monitoring technique: The space-time image velocimetry (STIV). International Journal of River Basin Management, 5(2), 105–114. https://doi.org/10.1080/15715124.2007.9635310 [4] Gonzalez, R. C., & Woods, R. E. (2018). Digital image processing (4th ed.). Pearson. [5] Hearn, D., & Baker, M. P. (1997). Computer graphics: C version (2nd ed.). Prentice Hall. [6] Hu, H., Saga, T., Kobayashi, T., & Taniguchi, N. (1998). Evaluation of the cross correlation method by using PIV standard images. Journal of Visualization, 1(1), 87–94. https://doi.org/10.1007/BF03182477 [7] Itakura, Y., Inaba, H., & Sawada, T. (2005). A debris-flow monitoring devices and methods bibliography. Natural Hazards and Earth System Sciences, 5(6), 971–977. https://doi.org/10.5194/nhess-5-971-2005 [8] Jacquemart, M., Meier, L., Graf, C., & McArdell, B. W. (2017). 3D dynamics of debris flows quantified at sub-second intervals from laser profiles. Natural Hazards, 89(2), 785–800. https://doi.org/10.1007/s11069-017-2993-1 [9] Kim, Y. (2006), Uncertainty analysis for non-intrusive measurement of river discharge using image velocimetry, Ph.D. thesis, Univ. of Iowa, Iowa City. [10] Kim, Y., Fujita, I., Hasegawa, M., & Yoon, J.-S. (2022). Measurement of debris flow velocity in flume using normal image by space-time image velocimetry incorporated with machine learning. Measurement, 199, 111218. https://doi.org/10.1016/j.measurement.2022.111218 [11] Le Boursicaud, R., Pénard, L., Hauet, A., Thollet, F., & Le Coz, J. (2015). Gauging extreme floods on YouTube: Application of LSPIV to home movies for the post-event determination of stream discharges. Hydrological Processes, 30(1), 90–105. https://doi.org/10.1002/hyp.10532 [12] Le Coz, J., Hauet, A., Pierrefeu, G., Dramais, G., & Camenen, B. (2010). Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in Mediterranean rivers. Journal of Hydrology, 394(1–2), 42–52. https://doi.org/10.1016/j.jhydrol.2010.05.049 [13] Marchi, L., Arattano, M., & Deganutti, A. M. (2002). Ten years of debris-flow monitoring in the Moscardo Torrent (Italian Alps). Geomorphology, 46(1–2), 1–17. https://doi.org/10.1016/S0169-555X(01)00162-3 [14] Muste, M., Fujita, I., & Hauet, A. (2008). Large-scale particle image velocimetry for measurements in riverine environments. Water Resources Research, 44(W00D19). https://doi.org/10.1029/2008WR006950 [15] Pham, M. V., & Kim, Y. T. (2022). Debris flow detection and velocity estimation using deep convolutional neural network and image processing. Landslides, 19(12), 2473–2488. https://doi.org/10.1007/s10346-022-01931-6 [16] Raffel, M., Willert, C. E., Scarano, F., Kähler, C. J., Wereley, S. T., & Kompenhans, J. (2018). Particle image velocimetry: A practical guide (3rd ed.). Springer International Publishing. [17] Rao, S. S. (2001). Applied numerical methods for engineers and scientists. Prentice Hall. [18] Theule, J. I., Crema, S., Marchi, L., Cavalli, M., & Comiti, F. (2018). Exploiting LSPIV to assess debris-flow velocities in the field. Natural Hazards and Earth System Sciences, 18, 1–13. https://doi.org/10.5194/nhess-18-1-2018 [19] 呂珍謀、李明靜、賴泉基、詹勳全、林國暉（2008）。影像分析方法應用於土石流表面速度計算之研究。農業工程學報，54(1)，13-23。 [20] 張智涵(2021)。以傅立葉轉換估算影像中顆粒位移的方法(碩士論文)。國立台灣大學土木工程學研究所。 [21] 郭亭妤（2020）。以影像偵測土石流前鋒（碩士論文）。國立台灣大學土木工程學研究所。 [22] 蘇得罕(2020)。以試驗探討土石流都卜勒效應（碩士論文）。國立台灣大學土木工程學研究所。 [23] 行政院農業委員會水土保持局。(2017)。水土保持手冊。 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98715 | - |
| dc.description.abstract | 台灣山區地勢陡峭，河道坡陡流急，極易在強降雨事件中引發土石流災害。由於土石流具突發性與高速流動等特性，對鄰近居民與基礎設施造成極大威脅，因此即時監測與流速偵測技術之發展，為提升災害預警效能之關鍵課題。
本研究提出一套以即時影像為基礎之土石流表面流速萃取方法，利用影像中兩個感興趣區域（Region of Interest, ROI）之平均灰階值與其時間變化特徵作為分析依據。透過影像灰階化、平均灰階值平滑處理與斜率計算，建構兩組時間序列，並進行時間延遲分析。進一步結合均方根誤差（Root Mean Square Deviation, RMSD）進行最佳時間平移量判定，據以估算事件於兩ROI間之平均表面流速。此外，本研究引入浮動式門檻值以排除環境雜訊干擾，並輔助判斷事件進出ROI之時段。 本研究分別透過人造數值影像、室內水槽實驗與現地監測影像三種方式進行驗證。數值實驗結果顯示，在理想條件下，本方法可準確估算流速，誤差約小於1.4%。室內水槽實驗中，由於光源變化與相機ISO及光圈設定為自動模式，導致影像亮度不一致，產生約-20.22%的誤差。至於現地土石流影像，受限於天候因素與設備限制，影像多處於模糊或沾附水滴、霧氣等情形，雖部分案例誤差可低至15.13%，惟整體誤差偏高，亦存在多起誤判狀況。 整體而言，本研究所提出之方法可應用於土石流流速之初步推估，但於現地實務應用上，影像品質受環境與氣候條件限制，仍為其準確性之主要影響因素，後續尚需針對此部分進行改善與強化。 | zh_TW |
| dc.description.abstract | Taiwan's mountainous terrain and rapid streams make the region prone to debris flows during heavy rainfall, posing serious risks to people and infrastructure. Real-time monitoring and velocity detection are crucial for early warning.
This study presents an image-based method to extract debris flow surface velocity in real time. Two Regions of Interest (ROIs) are analyzed by tracking average grayscale values and their changes over time. Using smoothed grayscale time series and slope data, a time delay analysis with Root Mean Square Deviation (RMSD) determines the optimal shift to estimate surface velocity. A floating threshold mechanism filters environmental noise and detects event timing within ROIs. This study was validated using synthetic images, indoor flume experiments, and field debris flow video. In ideal conditions, the method showed high accuracy with an error below 1.4%. In flume experiments, lighting variation and automatic camera settings caused brightness inconsistency, leading to an error of about -20.22%. Field images were affected by poor visibility, water droplets, and mist, resulting in higher errors, though some cases achieved a minimum error of 15.13%. Overall, the proposed method is effective for preliminary surface velocity estimation. However, its accuracy in field applications is limited by environmental and weather-related image quality issues, highlighting the need for further improvements. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-18T16:12:36Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-18T16:12:36Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii ABSTRACT iii 目次 iv 圖次 vii 表次 xiv Chapter1 緒論 1 1.1 研究背景及目的 1 1.2 文獻回顧 2 1.3 論文架構 6 Chapter2 研究方法 8 2.1 基本定義 8 2.1.1 數位影像 8 2.1.2 解析度 8 2.1.3 灰階化 9 2.1.4 感興趣區域 10 2.2 方法概述 11 2.2.1 分析概念與研究假設 11 2.2.2 灰階變化特性觀察 12 2.2.3 分析考量 15 2.3 平均灰階值與斜率計算 16 2.3.1 平均灰階值計算 16 2.3.2 時間序列平滑處理 16 2.3.3 斜率計算 17 2.3.4 平均灰階值與斜率計算流程 18 2.4 環境雜訊門檻值計算 19 2.4.1 斜率門檻值計算 19 2.4.2 灰階值門檻值計算 19 2.4.3 物體運動情形判別 20 2.4.4 門檻值計算流程 21 2.5 延遲時間(ΔT)分析: 23 2.5.1 灰階值平移 23 2.5.2 以單一值配對 24 2.5.3 以資料群組配對:RMSD分析 25 2.5.4 以資料群組配對:互相關分析 29 2.5.5 RMSD與互相關分析結果比較 31 2.5.6 延遲時間分析流程 32 2.6 數據後處理與延遲時間轉換為流速 34 2.6.1 數據異常值處理 34 2.6.2 流速計算公式應用 35 Chapter3 數值實驗 36 3.1 人為影像生成方式 36 3.2 分析速度方法驗證 38 3.2.1 圓半徑不變速度改變 40 3.2.2 速度不變圓半徑改變 41 3.2.3 等加速度運動 42 3.2.4 背景添加雜訊 43 3.3 小結 45 Chapter4 室內水槽實驗 46 4.1 保麗龍球流動實驗 46 4.1.1 實驗器材與配置 46 4.1.2 實驗步驟 47 4.1.3 以本研究方法分析單顆保麗龍球實驗速度 48 4.1.4 以肉眼判別單顆保麗龍球實驗速度 50 4.1.5 單顆保麗龍球實驗誤差比較 52 4.1.6 以本研究方法分析多顆保麗龍球實驗速度 54 4.1.7 以肉眼判別多顆保麗龍球實驗速度 57 4.1.8 多顆保麗龍球實驗誤差比較 59 4.2 砂石流動實驗 63 4.2.1 實驗器材與配置 63 4.2.2 實驗步驟 65 4.2.3 以本研究方法分析速度 65 4.2.4 以肉眼判別速度 68 4.2.5 誤差比較 69 Chapter5 現地影像分析 72 5.1 案例一:2004年敏督利颱風 72 5.1.1 肉眼判別速度 73 5.1.2 數據分析與結果 73 5.2 案例二:2024年凱米颱風 77 5.2.1 肉眼判別速度 77 5.2.2 數據分析與結果 79 Chapter6 結論與建議 81 參考文獻 83 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 灰階值 | zh_TW |
| dc.subject | 影像處理 | zh_TW |
| dc.subject | 即時監測 | zh_TW |
| dc.subject | 土石流表面流速 | zh_TW |
| dc.subject | 時間延遲分析 | zh_TW |
| dc.subject | time delay analysis | en |
| dc.subject | debris flow surface velocity | en |
| dc.subject | real-time monitoring | en |
| dc.subject | grayscale | en |
| dc.subject | image processing | en |
| dc.title | 即時影像萃取土石流表面流速 | zh_TW |
| dc.title | Real-Time Surface Velocity Extraction of Debris Flows Using Image-Based Analysis | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 詹錢登;周憲德;魏士超 | zh_TW |
| dc.contributor.oralexamcommittee | Chan-Deng Jan;Hsien-Ter Chou;Shih-Chao Wei | en |
| dc.subject.keyword | 影像處理,灰階值,時間延遲分析,土石流表面流速,即時監測, | zh_TW |
| dc.subject.keyword | image processing,grayscale,time delay analysis,debris flow surface velocity,real-time monitoring, | en |
| dc.relation.page | 86 | - |
| dc.identifier.doi | 10.6342/NTU202502670 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-13 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2025-08-19 | - |
| 顯示於系所單位： | 土木工程學系 | |
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