請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李鴻源 | |
| dc.contributor.author | Shih-Wei Lin | en |
| dc.contributor.author | 林士惟 | zh_TW |
| dc.date.accessioned | 2021-06-17T04:48:41Z | - |
| dc.date.available | 2023-08-08 | |
| dc.date.copyright | 2018-08-08 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-07-31 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71017 | - |
| dc.description.abstract | 氣候變遷與人口增加是現代都市發展所共同面臨的文明病，從水利工程的角度來看，高效的雨洪管理應是解決問題的手段之一。面對氣候變遷，雨水管理的觀念也漸有改變，暴雨控制從集中處理轉變為使用就地處理的低衝擊開發（LID），其主要概念是增加集水區的入滲或蓄水空間，直接減少地表逕流。LID除暴雨控制的能力，同時能增加地表綠覆減輕都市熱島效應，有效降低洪水、熱浪等災害脆弱度，提升都市韌性。傳統雨洪管理的評估，暴雨控制成效的因子為洪峰流量，然而洪峰流量僅能代表空間中某個點的水文量，不足以表示暴雨期間的逕流過程。因此本研究的評估除考慮下游整體洪峰流量因子外，也同時考慮能表示逕流過程的水文滯留足跡（HFR）為影響因子。
研究區域以高密度開發的新北市中永和地區為例，使用美國環保署（US EPA）的暴雨管理模式（SWMM），依照該地區的土地利用特性選擇適合的LID設施或是滯洪池，然後以不同重現期及不同延時的降雨進行模擬。結果顯示LID在低重現期、短延時降雨中最能發揮效益，在25年重現期以上的降雨則使用滯洪池的效益較高。此外在空間有限的情況下，應使用雨水收集；而成本有限的情況下，應使用綠屋頂，才能發揮最大效益。 在不同區域設置LID的情境模擬中，預算有限的情況下必須探討空間配置的優先順序。本研究使用多目標基因演算法（MOGA）後得到最佳空間配置下的成本與洪峰、HFR的關係曲線，LID設置達可設置面積的80%即與全部配置的水文效果相同。欲削減洪峰應將LID設置於流入排水主幹中游的支線上游，欲削減HFR則以主幹上游、支線下游的區域優先。最後以蒙地卡羅試驗證明在都市，管線複雜的情況下，不透水率較高的區域應設置較高比例的LID。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-17T04:48:41Z (GMT). No. of bitstreams: 1 ntu-107-R05521302-1.pdf: 11467891 bytes, checksum: b9748c0b5bd0ddadf052402489bfbeb2 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 誌謝 I
摘要 II ABSTRACT III 目錄 V 圖目錄 VIII 表目錄 XI 第一章 緒論 1 1.1 研究目的 1 1.2 研究流程 2 第二章 文獻回顧 4 2.1 都市韌性 4 2.1.1 都市韌性的演進 4 2.1.2 韌性城市的實踐 5 2.2 低衝擊開發 7 2.2.1 低衝擊開發概述 7 2.2.2 低衝擊開發策略相關研究 11 2.2.3 低衝擊開發最佳化研究 13 2.3 水文滯留足跡（Hydrologic footprint residence, HFR） 16 第三章 研究方法 22 3.1 SWMM運算原理 22 3.1.1 降雨逕流模組 22 3.1.2 幹線輸水模組 25 3.1.3 LID元件 30 3.2 多目標基因演算法 35 3.2.1 適應函數（Fitness function） 36 3.2.2 基因（Gene）與個體（Individual） 37 3.2.3 柏拉圖邊界（Pareto frontier） 37 3.2.4 人群與世代（Population and Generation） 38 3.2.5 階級（Rank）與競爭（Tournament） 39 3.2.6 交配（Crossover） 39 3.2.7 突變（Mutation） 40 3.2.8 擁擠距離（Crowding distance） 40 3.2.9 傳播值（Spread） 41 3.2.10 演算過程 41 第四章 模式建立 42 4.1 研究區域概述 42 4.2 SWMM模式建立 46 4.2.1 雨量（rain gage） 46 4.2.2 子集水區（subcatchment） 49 4.2.3 人孔（junction） 51 4.2.4 管線（conduit） 51 4.2.5 LID設置原則 52 4.2.6 LID設置上限 54 第五章 評估LID的降雨條件 55 5.1 模擬情境概述 55 5.1.1 LID策略 55 5.1.2 BMPs策略 55 5.2 不同降雨對於LID與滯洪池削減洪峰效果影響 57 5.3 不同降雨對於LID與滯洪池削減HFR效果影響 60 5.4 不同降雨對於LID元件的效果分析 63 5.4.1 減洪效果比較 64 5.4.2 削減HFR效果比較 66 第六章 評估LID的空間配置 69 6.1 最佳化條件設定 69 6.1.1 最佳化分區 69 6.1.2 LID配置原則 70 6.1.3 設計降雨 71 6.2 LID配置於不同區域對下游整體洪峰流量及HFR的影響 72 6.3 都市LID減洪最佳空間配置 75 6.4 都市LID削減HFR最佳空間配置 81 6.5 蒙地卡羅試驗 87 第七章 結論與建議 93 7.1 結論 93 7.2 建議 94 參考文獻 96 | |
| dc.language.iso | zh-TW | |
| dc.subject | 水文滯留足跡 | zh_TW |
| dc.subject | 多目標基因演算法 | zh_TW |
| dc.subject | 低衝擊開發 | zh_TW |
| dc.subject | 都市韌性 | zh_TW |
| dc.subject | 暴雨管理模式 | zh_TW |
| dc.subject | Urban resilience. | en |
| dc.subject | Low impact development (LID) | en |
| dc.subject | Hydrologic footprint residence (HFR) | en |
| dc.subject | Multi-objective genetic algorithm (MOGA) | en |
| dc.subject | Storm Water Management Model (SWMM) | en |
| dc.title | 多目標基因演算法於韌性城市評估之研究 | zh_TW |
| dc.title | Apply Multi-Objective Genetic Algorithm To Evaluate Urban Resilience. | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 何昊哲,葉克家 | |
| dc.subject.keyword | 水文滯留足跡,多目標基因演算法,低衝擊開發,都市韌性,暴雨管理模式, | zh_TW |
| dc.subject.keyword | Hydrologic footprint residence (HFR),Low impact development (LID),Multi-objective genetic algorithm (MOGA),Storm Water Management Model (SWMM),Urban resilience., | en |
| dc.relation.page | 99 | |
| dc.identifier.doi | 10.6342/NTU201802253 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-01 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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