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| 題 名 | 風險地區AI時空管理:2025年花蓮縣光復鄉模擬分析芻議=AI-powered Spatiotemporal Management of Risk Areas: A Simulation Analysis Proposal for Guangfu, Hualien, in 2025 |
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| 作 者 | 張世賢; | 書刊名 | 科際整合月刊 |
| 卷 期 | 11:1 2026.01[民115.01] |
| 頁 次 | 頁74-89 |
| 分類號 | 312.83 |
| 關鍵詞 | 風險地區; AI時空管理; 花蓮縣光復鄉; 模擬分析; |
| 語 文 | 中文(Chinese) |
| 中文摘要 | 風險地區,危機重重;時空變化甚複雜,超越人腦想像,要以 AI 補強。本 文以 AI 時空管理的觀點,探討 2025 年花蓮縣光復鄉堰塞湖水災,可以利用 AI 模擬分析,建立救災整合模型。本研究方法擬採用模擬研究法,建立「雨量與 堰塞湖時空模型」,探討可能溢流、潰流情況,與「雨量影響當地交通時空模 型」整合起來,建立「救災整合模型」。因無相關大數據,只能提供芻議,做 為未來模擬研究的前瞻作業。本文研究建議:政府主管機關平素應以 AI 無人機 測量,建立詳細防災大數據,作為建立 AI 時空管理模型,提供給防災單位參 考。 |
| 英文摘要 | High-risk areas are fraught with danger; their spatiotemporal changes are extremely complex. They are beyond human imagination. We need the enhancement of AI capabilities to restore them. This paper explores the potential for a landslide dam in Guangfu, Hualien in 2025 from the perspective of AI spatiotemporal management, utilizing AI simulation analysis to establish an integrated disaster relief model. The research methodology will employ simulation to establish a "rainfall and landslide dammed lake spatiotemporal model," exploring potential overflow and collapse scenarios. This model will be integrated with a "rainfall impacting local transportation spatiotemporal model" to create an "integrated disaster relief model." Due to the lack of relevant big data, only preliminary suggestions can be provided as preliminary work for future simulation research. This study recommends that government authorities should routinely use AI drones for measurement to establish detailed disaster prevention big data, which can then be used to build AI spatiotemporal management models and provided to disaster prevention units for reference. |
本系統中英文摘要資訊取自各篇刊載內容。