請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80652完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴進松(Jihn-Sung Lai) | |
| dc.contributor.author | Lo-Yi Chen | en |
| dc.contributor.author | 陳羅以 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:11:50Z | - |
| dc.date.available | 2021-11-05 | |
| dc.date.available | 2022-11-24T03:11:50Z | - |
| dc.date.copyright | 2021-11-05 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-22 | |
| dc.identifier.citation | [1] 全臺北淹水災情管制表- Emergency Response Center, WRA，https://fhy.wra.gov.tw/fhy/，2021 年 1 月 28 日讀取。 [2] 交通部中央氣象局：〈臺灣氣候〉，https://www.cwb.gov.tw/V7/climate/climate_info/taiwan_climate/taiwan_5.html ， 2018 年 12 月 11 日讀取。 [3] 陳桂霞, 黃重光. (1999). 自組織映射圖網路簡介. [4] 彩雲颱風災害應變處置報告結報，https://www.emic.gov.tw/，2021 年 7 月 19 日讀取。 [5] 國家災害防救科技 (2018)。台灣氣候的過去與未來；臺灣氣候變遷科學報告2017 ─物理現象與機制重點摘錄。 [6] 臺灣大學氣候天氣災害研究中心團隊。(2017)。106 年度臺北市災害防救深耕 計畫期末 報告書。臺北市政府。 [7] 臺灣大學氣候天氣災害研究中心團隊。(2017)。106 年度新北市災害防救深耕 計畫期末 報告書。新北市政府。 [8] 黃威雄(2000)，應用類神經網路於颱風期間雷達降雨模擬之研究，國立臺灣 大學土木工程學系，碩士論文，共 76 頁。 [9] 黃明宏(2008)，格網型類神經網路颱風降雨量模擬模式，國立成功大學水利 工程研究所，碩士論文，共 110 頁。 [10] 童偉安(2015)，以類神經網路及複迴歸分析建構颱風降雨量及地下水位預測 模式，國立中興大學水土保持學系，碩士學位論文，共 113 頁。 [11] 蔡孟蓉(2006)， 類神經網路結合衛星影像預報颱風降雨量，國立臺灣大學生 物環境系統工程學研究所，碩士論文，共 87 頁。 [12] 周乃昉、鄭子璉、林柏承(2000)，應用類神經網路於颱風降雨量的推估，第 十一屆水利工程研討會論文集，檢自: http://www.tlcheng.tk/Paper/rain00/rain00.htm [13] 羅竣文(2010)，應用類神經網路於颱風降雨量即時預報之研究，國立台灣大 學工學院土木工程研究所，碩士論文，共 90 頁。 [14] 劉鑌鋈(2009)，利用機器學習修正 QPESUMS 雷達估計降雨，國立成功大學 水利工程研究所，碩士論文，共 102 頁。 [15] Akbari Asanjan, A., Yang, T., Hsu, K., Sorooshian, S., Lin, J., Peng, Q. (2018). Short-term precipitation forecast based on the PERSIANN system and LSTM recurrent neural networks. Journal of Geophysical Research: Atmospheres, 123, 12,543– 12,563. [16] CHOI-WAN typhoon disaster response report by Central Emergency Operation Center, 2021 [17] Chang CC, Lin CJ. 2001. LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. [18] Chang FJ, Chung CH. 2012. Estimation of riverbed grain-size distribution using image-processing techniques. Journal of Hydrology 440: 102–112. [19] Chang LC, Shen HY, Wang YF, Huang JY, Lin YT. 2010. Clustering-based hybrid inundation model for forecasting flood inundation depths. Journal of Hydrology 385(1–4): 257–268. [20] Chang, H. K., Lin, Y. J., Lai, J. S. (2018). Methodology to set trigger levels in an urban drainage flood warning system–an application to Jhonghe, Taiwan. Hydrological Sciences Journal, 63(1), 31-49. [21] F D. Miljković, 'Brief review of self-organizing maps,' 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, 2017, pp. 1061-1066, doi: 10.23919/MIPRO.2017.7973581. [22] Gabriela Czibula, Andrei Mihai, Eugen Mihuleţ, Daniel Teodorovici, Using self-organizing maps for unsupervised analysis of radar data for nowcasting purposes, Procedia Computer Science, Volume 159, 2019, Pages 48-57, ISSN 1877-0509, [23] Intergovernmental Panel on Climate Change. Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change: IPCC, 2014. [24] K. Direk and V. Chandrasekar, 'Study of Hurricanes and Typhoons from TRMM Precipitation Radar Observations: Self Organizing Map (SOM) Neural Network,' 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, 2006, pp. 45-48, doi:1109/IGARSS.2006.16. [25] Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A. 2012. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences 67(1): 251–264. [26] Kohonen T. 2001. Self-Organizing Maps. Springer: New York. [27] Liao, H. Y., Pan, T. Y., Chang, H. K., Hsieh, C. T., Lai, J. S., Tan, Y. C., Su, M. D. (2019). Using Tabu Search Adjusted with Urban Sewer Flood Simulation to Improve Pluvial Flood Warning via Rainfall Thresholds. Water, 11(2), 348. [28] Lin GF, Chen GR, Huang PY, Chou YC. 2009a. Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. Journal of Hydrology 372(1–4): 17–29. DOI: 10.1016/j .jhydrol.2009.03.032. [29] Lin GF, Chen GR, Huang PY. 2010. Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting. Advances in Water Resources 33(8): 887–898. DOI: 10.1016/j.advwatres.2010.04.016. [30] Lin GF, Chen GR, Wu MC, Chou YC. 2009b. Effective forecasting of hourly typhoon rainfall using support vector machines. Water Resources Research 45(8): W08440. DOI: 10.1029/2009WR007911. [31] Lin GF, Chen GR. 2004. A systematic approach to the input determination for neural network rainfall–runoff models. Hydrological Processes 22(14): 2524–2530. [32] Lin GF, Chen GR. 2008. A systematic approach to the input determination for neural network rainfall–runoff models. Hydrological Processes 22(14): 2524–2530. DOI: 10.1002/hyp.6849. [33] Lin GF, Chen LH. 2004. A non-linear rainfall-runoff model using radial basis function network. Journal of Hydrology 289(1–4): 1–8. [34] Lin JY, Cheng CT, Chau KW. 2006. Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal 51(4): 599–612. [35] Liu, X., Yang, T., Hsu, K., Liu, C., Sorooshian, S. (2017). Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan plateau. Hydrology and Earth System Sciences, 21(1), 169– 181. [36] Pan TY, Lai JS, Chang TJ, Chang HK, Chang KC, Tan YC. 2011. Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database. Natural Hazards and Earth System Sciences 11(3): 771–787 . [37] Rinehart, R. E., Garvey, E. T. (1978). Three-dimensional storm motion detection by conventional weather radar. In (Vol. 273, pp. 287-289). [38] Toth, E., Brath, A., Montanari, A. (2000). Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of hydrology, 239(1-4), 132-147. [39] Vasiloff, SV, Howard, KW, Rabin, RM, Brooks, HE, Seo, D.-J., Zhang, J., Kitzmiller, DH, Mullusky, MG, Krajewski, WF, Brandes, EA (2007). Improving QPE and very short term QPF: An initiative for a community-wide integrated approach. Bulletin of the American Meteorological Society, 88(12), 1899– 1911. [40] Verworn, A., Haberlandt, U. (2011). Spatial interpolation of hourly rainfalleffect of additional information, variogram inference and storm properties. Hydrology and Earth System Sciences 15 (2011), Nr. 2, 15(2), 569- 584. [41] Zhang, J., and et al., 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 1321–1338, doi:10.1175 /2011BAMS-D-11-00047.1. [42] Zhu, Q., Hsu, K.-l., Xu, Y.-P., Yang, T. (2017). Evaluation of a new satellite-based precipitation data set for climate studies in the Xiang River basin, southern China. International Journal of Climatology, 37(13), 4561– 4575. [43] Buahin, C.A.; Sangwan, N.; Fagan, C.; Maidment, D.R.; Horsburgh, J.S.; Nelson, E.J.; Merwade, V.; Rae, C. Probabilistic flood inundation forecasting using rating curve libraries. J. Am. Water Resour. Assoc. 2017, 53, 300–315. [44] Chang, L.-C., Chang, F.-J., Yang, S.-N., Tsai, F.-H., Chang, T.-H., Herricks, E. E. (2020). Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance. Nature Communications, 11(1), 1-13. [45] Climate change 2014 synthesis report. contribution of working groups I, II, and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC. http://www.mendeley.com/research/climate-change-2014-synthesis -report-contribution-working-groups-i-ii-iii-fifth-assessment-report-in-20 [46] E. Toth, A. Brath and A. Montanari, 2000, Comparison of short-time rainfall prediction models for real-time flood forecasting, Journal of the Hydrology, 239, 132-147. [47] Houze, R. A., 1993: Cloud Dynamics. Academic Press, San Diego, 573 pp. [48] Houze, R. A.,1997: Stratiform precipitation in regions of convection: A meteorological paradox?. Bull. Amer. Meteor. Soc., 78, 2179–2196. [49] Smith K. 2001. Environmental Hazards Assessing Risk and Reducing Disaster, 3 rd edn. Routledge 11 New Fetter Lane: London. [50] UNDRR, Structural and non-structural measures. Software available at https://www.undrr.org/terminology/structural-and-non-structural-measures. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80652 | - |
| dc.description.abstract | "近年隨著氣候變遷的影響，極端天氣事件發生的頻率和強度增加，並常在臺灣雨量最豐沛的汛期時，因梅雨、颱風以及對流雨興盛，致使發生積淹水機率逐年增加。目前水利署因應淹水災害而研訂的警戒參考標準，給定不同區域（鄉鎮或鄰里）在以單位小時之降雨量，訂定各區域之降雨淹水警戒值，並配合即時雨量觀測（如QPESUMS）以及全臺灣各地實際降雨狀況研判因應。然而因現今警戒系統流程相對耗時，能提前發佈預測與相對災害風險之警戒仍有改善之空間。 為發展更完善於警戒標準區域之積淹水災害預警方法，本研究收集在2014至2018年間，臺北市淹水總次數紀錄最高之區域－松山區中華里之即時雷達回波觀測圖及歷年時雨量資料，爾後採用類神經網路中自組織特徵映射（self-organizing feature maps, SOM）的分群演算法，依序訓練長安國小站及民生國中站之災區相近測站，建立研究區域之三維空間雷達回波數據和雨量實況觀測的關係，並利用聚類演算降訓練資料維度來進行群聚分析，對應出拓樸圖中致災風險與降雨範圍之關係，並採用K-means分群 (Clustering)有效地辨識在多變量數據中之致災特徵。 研究結果顯示，經由自組織特徵映射分群後，將淹水災害事件進行挑選，結果顯示雷達回波分類與發生災害和極端降雨事件的機率相關性極高；除此之外，SOM各個神經元亦能呈現出機率降雨範圍。接續K-means-SOM分類雷達空間的降雨致災特徵結果也表現顯著，藉由Nest-SOM最後訓練可調降降雨警戒閥值至40毫米並產生未來1至3小時之機率淹水警戒。此研究期待能加強鄰里區域之小尺度淹水預警準確度，並提供當地居民相關洪災風險機率分析，來提升及健全面對未來極端降雨之調適能力。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:11:50Z (GMT). No. of bitstreams: 1 U0001-2010202116104500.pdf: 7870709 bytes, checksum: 73511cdbb1665665959b66c166c81b8e (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 # 致謝 i 中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Literature review 3 1.3 Structure 9 Chapter 2 Methodology 10 2.1 Data and data preprocessing 10 2.2 Self-organizing Map 15 2.2.1 Network architecture 15 2.2.2 Network algorithm 17 2.2.3 Network parameter setting 19 2.3 K-means cluster analysis 21 2.4 Nest-SOM 22 2.5 Flowchart 23 Chapter 3 Study area and the sensitivity tests of model 24 3.1 Zhonghua village 24 3.2 Sensitivity tests of SOM model 28 Chapter 4 Models results and case studies 32 4.1 Data extracted by SOM 32 4.1.1 SOM-mean rain ranking 36 4.1.2 Zonghua village corresponds to SOM 40 4.2 Performance of the proposed model 42 4.2.1 Frequency of rainfall range 42 4.2.2 K-means-SOM 47 4.2.3 Nest-SOM 53 4.3 Probabilistic warnings of the extreme events 60 4.4 Particular situation analysis 72 Chapter 5 Summary 75 REFERENCE 80 | |
| dc.language.iso | en | |
| dc.subject | K-means | zh_TW |
| dc.subject | 雷達回波 | zh_TW |
| dc.subject | SOM | zh_TW |
| dc.subject | Nest-SOM | zh_TW |
| dc.subject | 降雨－淹水警戒值 | zh_TW |
| dc.subject | 機率式預報 | zh_TW |
| dc.subject | radar reflectivity | en |
| dc.subject | probabilistic flood early warning system | en |
| dc.subject | rainfall threshold | en |
| dc.subject | Nest-SOM | en |
| dc.subject | K-means | en |
| dc.subject | self-organizing map | en |
| dc.title | 應用SOM於雷達回波之機率淹水預警 | zh_TW |
| dc.title | Using self-organizing map to develop a probabilistic flood early warning system based on radar reflectivity | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 潘宗毅(Tsung-Yi Pan) | |
| dc.contributor.oralexamcommittee | 謝宜桓(Hsin-Tsai Liu),余化龍(Chih-Yang Tseng),陳柏孚 | |
| dc.subject.keyword | 雷達回波,SOM,K-means,Nest-SOM,降雨－淹水警戒值,機率式預報, | zh_TW |
| dc.subject.keyword | radar reflectivity,self-organizing map,K-means,Nest-SOM,rainfall threshold,probabilistic flood early warning system, | en |
| dc.relation.page | 86 | |
| dc.identifier.doi | 10.6342/NTU202103934 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-25 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 氣候變遷與永續發展國際學位學程 | zh_TW |
| 顯示於系所單位： | 氣候變遷與永續發展國際學位學程(含碩士班、博士班) | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2010202116104500.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 7.69 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。