基於數位孿生基礎建設耦合數值天氣預報模式及都市淹水模式建立都市淹水預報;A Digital Twin Urban Flood Forecasting System Coupling a Numerical Weather Prediction Model and an Urban Flood Model

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/98494

Title:	基於數位孿生基礎建設耦合數值天氣預報模式及都市淹水模式建立都市淹水預報;A Digital Twin Urban Flood Forecasting System Coupling a Numerical Weather Prediction Model and an Urban Flood Model
Authors:	陳孟淵;Chen, Meng-Yuan
Contributors:	遙測科技碩士學位學程
Keywords:	數位孿生;物聯網;天氣預報;淹水預報;導航;Digital Twin;Internet of Things;Weather Forecast;Flood Forecast;Navigation
Date:	2025-08-26
Issue Date:	2025-10-17 12:50:37 (UTC+8)
Publisher:	國立中央大學
Abstract:	在極端氣候加劇與都市化快速擴張的雙重衝擊下，台灣都市地區淹水風險日益嚴峻。傳統災防系統普遍存在資料異質、採樣密度不均與應變整合機制缺乏等問題，難以即時應對複雜的降雨與城市環境變化。為強化都市防災韌性，本研究導入數位孿生（Digital Twin）技術，發展一套具備高解析度、即時性與決策支援功能的都市淹水預報與導航系統。研究首先建構一套模組化、可擴充與雲端運算導向的數位孿生基礎建設，實現異質物聯網感測資料處理與儲存、以及進入運算流的全自動流程。接著基於此建設耦合數值天氣預報模式及都市淹水模式，整合衛星資料、IoT感測資料，產製10公尺解析度72小時逐時都市淹水預報。在應用層面，本研究進一步發展淹水導航服務，結合OpenStreetMap道路資料與淹水預報成果，建立可反映淹水災情的可通行路網，並實作基於Dijkstra演算法之避災路徑規劃功能，以利防災應變。本研究以台南市善化區為研究區域，應用本系統於2021年強降雨及2024年凱米颱風之案例，結果顯示其具備基本淹水預測能力，並能即時提供避災導航資訊，驗證了數位孿生技術於智慧城市虛實整合應用之潛力。;Due to intensifying impacts of extreme weather and rapid urbanization, the flood risk in urban areas of Taiwan has become increasingly severe. Traditional disaster prevention systems often suffer from issues such as heterogeneous data, uneven sampling density, and lack of integrated emergency response mechanisms, making it difficult to effectively respond to complex rainfall events and urban environmental changes. To enhance urban disaster resilience, this study introduces a Digital Twin framework to develop a high-resolution, efficient, and decision-support-capable urban flood forecasting and navigation system. First, this study constructs a modular, scalable, and cloud-based digital twin system, enabling automated workflows for processing and storing heterogeneous IoT sensor data into application streams. Based on this system, this study couples a numerical weather prediction (NWP) model with an urban flood model, integrating satellite data and IoT sensor data to produce 72-hour flood forecasts at 10-meter and hourly resolution. In terms of application, the study further develops a flood navigation service by combining OpenStreetMap road data with flood forecasting results to create a flood-aware, drivable road network. It also implements an evacuation route planning feature based on the Dijkstra algorithm to support emergency response.The study selected Shanhua District in Tainan City as the study area and applied the system to cases of heavy rainfall in 2021 and Typhoon Gaemi in 2024. Results indicate that the system exhibits fundamental flood forecasting capabilities and can provide efficient disaster navigation information, demonstrating the potential of digital twin technology for cyber-physical integration in smart cities.
Appears in Collections:	[Master of Science Program in Remote Sensing Science and Technology ] Electronic Thesis & Dissertation

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