Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/158701
|
| Title: | 利用深度學習進行洪水敏感度評估 - 以高雄市為例
Using Deep Learning for Flood Susceptibility Assessment - A Case Study of Kaohsiung City |
| Authors: | 王耀緯
Wang, Yao-Wei |
| Contributors: | 范噶色
Stephan van Gasselt
王耀緯
Wang, Yao-Wei |
| Keywords: | 淹水潛勢圖
深度學習
U-Net
空間分析
Flood susceptibility mapping
Deep learning
U-Net
Spatial analysis |
| Date: | 2025 |
| Issue Date: | 2025-08-04 15:07:57 (UTC+8) |
| Abstract: | 隨著氣候變遷導致極端降雨事件日益頻繁，都市地區面臨日益嚴峻的淹水風險。因此，提升淹水潛勢圖的精確度與預警系統的可靠性，已成為防災與氣候調適中的重要課題。傳統的淹水潛勢圖多以水理模式為基礎，但近年來，尤其在東南亞地區，已有越來越多研究嘗試應用深度學習技術進行淹水分析。然而，在台灣相關應用仍相對有限。
本研究建構一套應用於高雄市的淹水潛勢分類框架，採用 U-Net 深度學習架構。以 2018 年高雄市歷史淹水點作為訓練標籤，並納入包括雨量、坡度、土地利用、NDVI 在等 11 項淹水影響因子作為訓練資料。模型設定為二分類、三分類、四分類與六分類等不同分類層級，探討分類的類別數對預測效能之影響；並以2019 至 2022 年的獨立歷史淹水資料則作為測試集。
在不同分類設定中，三分類模型展現出最為穩定且現實可行的表現，在 2018 年驗證資料下達到 86.09% 的整體準確率與 0.651 的 Cohen’s Kappa 值。
模型生成之淹水潛勢圖顯示，高風險淹水區（淹水深度 > 1.0 公尺）主要分布於高雄市西部低窪地區，如楠梓區、前鎮區與部分鳳山區。這些區域為高雄市人口最密集的都會核心區之一，涵蓋大量住宅區、工業園區與重要交通基礎設施。相對地，東部山區則顯示出顯著較低的淹水潛勢。此空間分布結果對於風險傳達、防洪規劃與應變資源配置具有重要參考價值。
在訓練過程中，有發現三個變數：土地利用類型（LULC）、距離排水系統與高程（DEM），相較其他影響因子對模型準確度具有更顯著的影響。這些因子可能在高雄市淹水潛勢的空間分布中扮演關鍵角色。然而，相關發現仍屬初步定性觀察，未來研究應進一步進行嚴謹的量化評估，以確認其實際貢獻程度。
With climate change increasing the frequency of extreme rainfall events, urban flood risks are growing. Enhancing the accuracy of flood susceptibility maps and early warning systems is crucial for disaster mitigation. While traditional maps rely on hydrological models, deep learning has gained traction in recent studies, particularly in Southeast Asia. However, its application in Taiwan remains limited.
This study developed a flood susceptibility classification framework using the U-Net deep learning model, focusing on Kaohsiung City. Historical flood points from 2018 served as training data, along with 11 environmental factors including rainfall, slope, land use, and NDVI. The model was tested under four classification schemes (2, 3, 4, and 6 classes), with 2019–2022 data used for validation.
Among these schemes, the three-class model showed the best balance, achieving 86.09% accuracy and a Kappa coefficient of 0.651.
The resulting flood map identified high-risk areas (>1.0 m depth) mainly in the western lowlands, including Nanzih (楠梓區), Cianjhen (前鎮區), and parts of Fengshan (鳳山區), densely populated districts with major residential and industrial zones. In contrast, eastern mountainous areas showed lower susceptibility. These patterns provide guidance for flood risk communication and emergency response.
During modeling, three variables—land use/land cover (LULC), distance to artificial drainage systems, and elevation (DEM)—had a stronger impact on accuracy than others. While this finding is based on qualitative analysis, further research is needed to quantify their effects. |
| Reference: | i. Chinese reference
陳振宇、劉維則、許家祥（2017）。使用 QPESUMS 雨量資料建立崩塌災害預警模式。《中國水土保持學報》，48(1)，44–55。
中央氣象署（2020）。台灣氣候概況。取自 https://www.cwa.gov.tw/V8/C/K/Encyclopedia/climate/climate2_all.html
農業部農村發展及水土保持署（2024）。土石流及大規模崩塌防災資訊網。取自 https://246.ardswc.gov.tw/
國家災害防救科技中心（2016）。氣候變遷災害風險調適平台。取自 https://dra.ncdr.nat.gov.tw/Frontend/Disaster/ClimateIndex?Category=DisasterStatus
國家災害防救科技中心（2022）。112 年豪雨及颱風災情彙整報告。取自 https://den.ncdr.nat.gov.tw/media/paefstnk/112%E5%B9%B4%E8%B1%AA%E9%9B%A8%E5%8F%8A%E9%A2%B1%E9%A2%A8%E7%81%BD%E6%83%85%E5%BD%99%E6%95%B4%E5%A0%B1%E5%91%8A.pdf
侯孟筑（2021）。淹水對於本土登革熱病例的影響（碩士論文）。國立成功大學。取自 https://hdl.handle.net/11296/za8s5j
Li, Y. J., Chang, K. C., & Fu, J. C.（2023）。應用降雨量與頻率比方法於淹水災害潛感圖之研究。《中國土木水利工程學刊》，35(6)，551–563。
鄭伊婷. (2009). 以複合型模式分析區域淹水潛勢. 淡江大學水資源及環境工程學系碩士班學位論文, 1-71.
陳嗣祖. (2016). 淹水潛勢區之風險分析. 交通大學土木工程系所學位論文, 2016, 1-107.
ii. English reference
Ahmed, Nassar, A. M., & Omar. (2018). Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arabian Journal of Geosciences, 11(24). https://doi.org/10.1007/s12517-018-4095-0
Andrés Díez Herrero, Luis Laín Huerta, & Miguel Llorente Isidro. (2009). A handbook on flood hazard mapping methodologies. Madrid Geological Survey Of Spain.
Antunes, S. (2020). Physical Modelling vs. Numerical Modelling: Complementarity and Learning. Preprints.org; Preprints. https://www.preprints.org/manuscript/202007.0753/v2
Arthington, A. (2012). Environmental flows: saving rivers in the third millennium (Vol. 4). Univ of California Press.
Artificial Intelligence Applied to Satellite-based Remote Sensing Data for Earth Observation | IET Digital Library. (2021). Books. https://digital-library.theiet.org/doi/book/10.1049/pbte098e
Ayalew, L., & Yamagishi, H. (2004). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1-2), 15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
Bellos, V. (2012). Ways for flood hazard mapping in urbanised environments: A short. Water Util. J, 4, 25-31.
Binh, P. T., Zhu, X., Groeneveld, R. A., & van Ierland, E. C. (2020). Risk communication, women’s participation, and flood mitigation in Vietnam: An experimental study. Land Use Policy, 95, 104436.
Bahram Choubin, Hosseini, F. S., Rahmati, O., & Mansor Mehdizadeh Youshanloei. (2022). A step toward considering the return period in flood spatial modeling. Natural Hazards, 115(1), 431–460. https://doi.org/10.1007/s11069-022-05561-y
Breiman, L. (2001). Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
Castelluccio, M., Poggi, G., Sansone, C., & Verdoliva, L. (2015). Land Use Classification in Remote Sensing Images by Convolutional Neural Networks. ArXiv.org. https://arxiv.org/abs/1508.00092
Chen, M.-J., Lin, C.-Y., Wu, Y.-T., Wu, P.-C., Lung, S.-C., & Su, H.-J. (2012). Effects of Extreme Precipitation to the Distribution of Infectious Diseases in Taiwan, 1994–2008. PLoS ONE, 7(6), e34651–e34651. https://doi.org/10.1371/journal.pone.0034651
Chen, Y.-R., Yeh, C.-H., & Yu, B. (2011). Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Natural Hazards, 59(3), 1261–1276. https://doi.org/10.1007/s11069-011-9831-7
Chollet, E., Balbastre, Y., Magnain, C., Fischl, B., & Wang, H. (2024). A label-free and data-free training strategy for vasculature segmentation in serial sectioning OCT data. ArXiv.org. https://arxiv.org/abs/2405.13757
Chou, T., Hoang, T., Fang, Y., Nguyen, Q., Lai, T. A., Pham, V., Vu, V., & Bui, Q. (2020). Swarm‐based optimizer for convolutional neural network: An application for flood susceptibility mapping. Transactions in GIS, 25(2), 1009–1026. https://doi.org/10.1111/tgis.12715
Climate Change 2022: Impacts, Adaptation and Vulnerability. (2022). Ipcc.ch; IPCC. https://www.ipcc.ch/report/ar6/wg2/
Caprario, J., Rech, A. S., Tasca, F. A., & Finotti, A. R. (2019). Influence of drainage network and compensatory techniques on urban flooding susceptibility. Water Science and Technology, 79(6), 1152-1163.
Chen, M.-J., Lin, C.-Y., Wu, Y.-T., Wu, P.-C., Lung, S.-C., & Su, H.-J. (2012). Effects of extreme precipitation to the distribution of infectious diseases in Taiwan, 1994–2008. PloS One, 7(6), e34651.
Darabi, H., Choubin, B., Rahmati, O., Haghighi, A. T., Pradhan, B., & Kløve, B. (2019). Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques. Journal of hydrology, 569, 142-154.
Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. (2017). Geomatics, Natural Hazards and Risk. https://doi.org/10.1080//19475705.2017.1294113
Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. (2016). Geomatics, Natural Hazards and Risk. https://doi.org/10.1080//19475705.2015.1045043
Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches. (2016). Hydrological Sciences Journal – Journal Des Sciences Hydrologiques. https://doi.org/10.1080//02626661003683389
François Chollet. (2021). Deep Learning with Python, Second Edition. Shelter Island, Ny Manning Publications.
Fang, Z., Wang, Y., Peng, L., & Hong, H. (2021). A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. International Journal of Geographical Information Science, 35(2), 321-347.
Fu, S., Lyu, H., Wang, Z., Hao, X., & Zhang, C. (2022). Extracting historical flood locations from news media data by the named entity recognition (NER) model to assess urban flood susceptibility. Journal of Hydrology, 612, 128312.
García-Ruiz, J. M., Regüés, D., Alvera, B., Lana-Renault, N., Serrano-Muela, P., Nadal-Romero, E., ... & Arnáez, J. (2008). Flood generation and sediment transport in experimental catchments affected by land use changes in the central Pyrenees. Journal of Hydrology, 356(1-2), 245-260.
Global Report on Internal Displacement 2023 (GRID 2023): Internal displacement and food security - World. (2023, May 11). ReliefWeb. https://reliefweb.int/report/world/global-report-internal-displacement-2023-grid-2023-internal-displacement-and-food-security
Geneva, Switzerland, pp. 35-115, doi: 10.59327/IPCC/AR6-9789291691647
Internal Displacement Monitoring Centre. (2023). Global report on internal displacement 2023. https://www.internal-displacement.org/global-report/grid2023/
Ha, H., Bui, Q. D., Khuc, T. D., Tran, D. T., Pham, B. T., Mai, S. H., Nguyen, L. P., & Luu, C. (2022). A machine learning approach in spatial predicting of landslides and flash flood susceptible zones for a road network. Modeling Earth Systems and Environment, 8(4), 4341–4357. https://doi.org/10.1007/s40808-022-01384-9
Helsel, D., & Hirsch, R. (2002). Statistical methods in water resources. https://doi.org/10.3133/twri04a3
Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. In Library Union Catalog of Bavaria, Berlin and Brandenburg (B3Kat Repository). University of Illinois Urbana-Champaign. https://doi.org/10.1002/0471722146
Hamzah, H. (2005). Roadmap toward effective flood hazard mapping in Malaysia. Concluding Report, JICA region focused training course on flood hazard mapping, JFY.
IPCC, 2023: Sections. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC,
Ji, J., Choi, C., Yu, M., & Yi, J. (2012). Comparison of a data-driven model and a physical model for flood forecasting. WIT Transactions on Ecology and the Environment. https://doi.org/10.2495/friar120111
Kaleem, M., & Zeb, J. Abdullah.(2021). Role of Socio-Economic Resilience in Coping with Flood.
Kamran Chapi, Singh, V. P., Ataollah Shirzadi, Shahabi, H., Bui, D. T., Pham, B. T., & Khosravi, K. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software, 95, 229–245. https://doi.org/10.1016/j.envsoft.2017.06.012
Kaya, C. M., & Derin, L. (2023). Parameters and methods used in flood susceptibility mapping: a review. Journal of Water and Climate Change, 14(6), 1935-1960.
Khosravi, K., Panahi, M., Golkarian, A., Keesstra, S. D., Saco, P. M., Bui, D. T., & Lee, S. (2020). Convolutional neural network approach for spatial prediction of flood hazard at the national scale of Iran. Journal of Hydrology, 591, 125552–125552. https://doi.org/10.1016/j.jhydrol.2020.125552
Kotaridis, I., & Lazaridou, M. (2022). Integration of convolutional neural networks for flood risk mapping in Tuscany, Italy. Natural Hazards, 114(3), 3409–3424. https://doi.org/10.1007/s11069-022-05525-2
Lee, Y., & Brody, S. D. (2017). Examining the impact of land use on flood losses in Seoul, Korea. Land Use Policy, 70, 500–509. https://doi.org/10.1016/j.landusepol.2017.11.019
Li, Y., Osei, F. B., Hu, T., & Stein, A. (2023). Urban flood susceptibility mapping based on social media data in Chengdu city, China. Sustainable Cities and Society, 88, 104307.
Luu, C., Bui, Q. D., Costache, R., Nguyen, L. T., Nguyen, T. T., Van Phong, T., Van Le, H., & Pham, B. T. (2021). Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam. Natural Hazards, 108(3), 3229–3251. https://doi.org/10.1007/s11069-021-04821-7
Mahyat Shafapour Tehrany, Lee, M.-J., Pradhan, B., Mustafa Neamah Jebur, & Lee, S. (2014). Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environmental Earth Sciences, 72(10), 4001–4015. https://doi.org/10.1007/s12665-014-3289-3
Melgar-García, L., Martínez-Álvarez, F., Tien Bui, D., & Troncoso, A. (2023). A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area. International Journal of Digital Earth, 16(1), 3661-3679.Miles, R. E., Snow, C. C., Meyer, A. D., & Coleman Jr, H. J. (1978). Organizational strategy, structure, and process. Academy of management review, 3(3), 546-562.
Pham, V. D., Nguyen, Q. H., Nguyen, H. D., Pham, V. M., & Bui, Q. T. (2020). Convolutional neural network—optimized moth flame algorithm for shallow landslide susceptible analysis. IEEE Access, 8, 32727-32736.
Rahmati, O., & Pourghasemi, H. R. (2017). Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models. Water Resources Management, 31(5), 1473–1487. https://doi.org/10.1007/s11269-017-1589-6
Rizeei, H. M., Saharkhiz, M. A., Pradhan, B., & Ahmad, N. (2016). Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models. Geocarto international, 31(10), 1158-1177.
Rofiat Bunmi Mudashiru, Nuridah Sabtu, Ismail Abustan, & Balogun, W. (2021). Flood hazard mapping methods: A review. Journal of Hydrology, 603, 126846–126846. https://doi.org/10.1016/j.jhydrol.2021.126846
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer International Publishing.
Serrano, L., Ustin, S. L., Roberts, D. A., Gamon, J. A., & Penuelas, J. (2000). Deriving water content of chaparral vegetation from AVIRIS data. Remote sensing of Environment, 74(3), 570-581.
Saidani, M., & Shibani, A. (2014, January). Use of physical and numerical models in engineering design education. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Bali, Indonesia (pp. 7-9).
Salhab, Melda; Rentschler. (2020, June). People in Harm’s Way: Flood Exposure and Poverty in 189 Countries. Https://Hdl.handle.net/10986/34655. https://hdl.handle.net/10986/34655
Seleem, O., Ayzel, G., de Souza, A. C. T., Bronstert, A., & Heistermann, M. (2022). Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany. Geomatics, Natural Hazards and Risk, 13(1), 1640-1662.
Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J. J., ... & Ahmad, A. (2020). Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sensing, 12(2), 266.
State of the Climate in Asia 2021. (2023, January 19). World Meteorological Organization. https://wmo.int/publication-series/state-of-climate-asia-2021
Tang, Z., Zhang, H., Yi, S., & Xiao, Y. (2018). Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis. Journal of Hydrology, 558, 144–158. https://doi.org/10.1016/j.jhydrol.2018.01.033
Teng, J., Jakeman, A. J., J. Vaze, Croke, B. F. W., Dutta, D., & Kim, S. (2017). Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environmental Modelling & Software, 90, 201–216. https://doi.org/10.1016/j.envsoft.2017.01.006
Thimmaiah Gudiyangada Nachappa, Sepideh Tavakkoli Piralilou, Khalil Gholamnia, Omid Ghorbanzadeh, Rahmati, O., & Blaschke, T. (2020). Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of Hydrology, 590, 125275–125275. https://doi.org/10.1016/j.jhydrol.2020.125275
Trong, N. G., Quang, P. N., Cuong, N. V., Le, H. A., Nguyen, H. L., & Tien Bui, D. (2023). Spatial prediction of fluvial flood in high-frequency tropical cyclone area using TensorFlow 1D-convolution neural networks and geospatial data. Remote Sensing, 15(22), 5429.
Vojtek, M., & Vojteková, J. (2019). Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process. Water, 11(2), 364–364. https://doi.org/10.3390/w11020364
Wilson, J. P., & Gallant, J. C. (2000). Digital terrain analysis. Terrain analysis: Principles and applications, 6(12), 1-27.
Wang, Y., Fang, Z., Hong, H., & Peng, L. (2020). Flood susceptibility mapping using convolutional neural network frameworks. Journal of Hydrology, 582, 124482. https://doi.org/10.1016/j.jhydrol.2019.124482
Wang, Y., Hong, H., Chen, W., Li, S., Pamučar, D., Gigović, L., Drobnjak, S., Tien Bui, D., & Duan, H. (2018). A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China. Remote Sensing, 11(1), 62. https://doi.org/10.3390/rs11010062
World Meteorological Organization. (2022). State of the global climate 2021 (WMO-No. 1290). https://wmo.int/publication-series/state-of-global-climate-2021
Yahaya, S., Ahmad, N., & Abdalla, R. F. (2010). Multicriteria analysis for flood vulnerable areas in Hadejia-Jama’are River basin, Nigeria. European Journal of Scientific Research, 42(1), 71-83.
Zhao, G., Pang, B., Xu, Z., Yue, J., & Tu, T. (2017). Mapping flood susceptibility in mountainous areas on a national scale in China. The Science of the Total Environment, 615, 1133–1142. https://doi.org/10.1016/j.scitotenv.2017.10.037 |
| Description: | 碩士
國立政治大學
地政學系
112257027 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112257027 |
| Data Type: | thesis |
| Appears in Collections: | [地政學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 702701.pdf | | 4656Kb | Adobe PDF | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
