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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 林國峰(Gwo-Fong Lin) | |
dc.contributor.author | Chieh-Lin Chen | en |
dc.contributor.author | 陳頡林 | zh_TW |
dc.date.accessioned | 2021-06-17T01:28:48Z | - |
dc.date.available | 2022-08-10 | |
dc.date.copyright | 2017-08-10 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-08-04 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67349 | - |
dc.description.abstract | 近年來氣候災害所造成的危害程度,隨著人口的增加、社會經濟的發展及極端降雨事件日趨嚴重。台灣也因極端降雨事件,使得旱澇加劇越來越頻繁,其中颱風事件所帶來豪雨,使大量雨水在短時期內傾盆下降,致使平原地帶、較低地區氾濫成災,造成洪水災害。淹水的發生不僅造成經濟上的損失,也使人民性命受到威脅。本研究建立淹水潛勢評估模式,進而繪製淹水潛勢地圖,提供作為防災參考依據,以達到減災之目的。
本研究選擇台灣東北部蘭陽溪流域為研究區域。蒐集2004年到2015年的颱風淹水事件及十二個淹水影響因子,包含了高程、坡度、坡向、曲率、平面曲率、剖面曲率、地形濕度指數、逕流強度指數、與河川的距離、岩性、土地使用、最大小時累積雨量。 研究中選用了三種不同的機器學習法來建立淹水潛勢評估模式,分別為決策樹、裝袋決策樹及隨機森林;以交叉驗證的方式驗證各模式淹水判別成效,比較決策樹、裝袋決策樹及隨機森林這三種方法建立的淹水潛勢式評估模式;建立混淆矩陣來分析各模式準確率。另外,利用接收者操作特徵曲線與其曲線下面積,評鑑三種模式之識別力。 各個模式在淹水潛勢判別上都有不錯的表現,其中又以隨機森林所建立之淹水潛勢評估模式表現最佳。隨機森林能準確模擬研究區域淹水分布之特性,並能展現當地地形因子特性。另外,以多次運行隨機森林模式,證明隨機森林有非常良好地強健性。最後,分析潛在淹水因子之貢獻度,貢獻度越大,代表對於淹水影響越大,最大小時累積降雨高達35%,其次為與河川的距離、地形濕度指數和逕流強度指數。未來可以根據本研究所發展之淹水潛勢評估模式,協助相關管理機關擬訂適當的防災策略。 | zh_TW |
dc.description.abstract | In recent years, rapid urban growth and climate change have led to many environmental problems and increased risks of natural disasters, including flooding, landslide and drought. The typhoon rainfall is an important source of water resources. However, the heavy rainfall brought by typhoons frequently result in serious disasters. Floods have become more and more frequent in recent decades. Therefore, flood susceptibility models can efficiently mitigate the disasters are desired.
The study area is the Lanyang river basin in northeastern Taiwan. All the flood-related factors and the historical flooding data are based on GIS technology. Flood events from 2004 to 2015 are collected. Moreover, twelve flood-related factors are used in the flood susceptibility analysis, namely, elevation, slope, aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to river, lithology, land use and maximum hour rainfall. In this study, three methods are employed to construct flood susceptibility models for the study area. The three methods include the decision tree (DT), bagging tree (BT) and random forest (RF). The performances of three models are checked by the accuracy and the area under the receiver operating characteristic curve (AUC). For flood susceptibility maps, the DT-based model and the BT-based model underestimated the actual flooded area. However, the RF-based model has a good consistency between the correctly prediction and actual maps. The RF-based model outperforms over the DT-based model and the BT-based model in the study area. The RF-based model has efficiently improvement for flood susceptibility assessment. The maximum 1-hour rainfall, distance to river, topographic wetness index, and stream power index are the top four most important of the twelve flood-related factors. In conclusion, the proposed model is validated in this study, and it can be used for planning protective and mitigation measures. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T01:28:48Z (GMT). No. of bitstreams: 1 ntu-106-R04521307-1.pdf: 5389791 bytes, checksum: 2a7a751c3de72097df493d61aed339b6 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 iii 中文摘要 iv Abstract v 目錄 vii 圖目錄 x 表目錄 xiii 第一章 緒論 1 1.1 研究動機與目的 1 1.2文獻回顧 3 1.2.1淹水之定義 3 1.2.2淹水影響因子 4 1.2.3淹水潛勢分析方法 6 1.3論文組織架構 12 第二章 研究區域與資料 13 2.1 研究區域 13 2.2 研究資料 15 2.2.1 淹水事件資料 15 2.2.2 淹水因子資料 27 第三章 研究方法與模式建立 40 3.1決策樹 40 3.2裝袋決策樹 41 3.3隨機森林 42 3.4模式建立 43 3.4.1研究流程 44 3.4.2隨機森林參數設定 45 第四章 交替驗證與評鑑指標 47 4.1交替驗證 47 4.2評鑑指標 47 4.2.1混淆矩陣 48 4.2.2 ROC曲線與AUC 49 第五章 結果與討論 51 5.1 動態誘發因子選擇 51 5.1.1 評鑑指標結果比較 52 5.1.2 淹水潛勢結果比較 55 5.2模式結果 58 5.2.1決策樹結果 58 5.2.2裝袋決策樹結果 59 5.2.3隨機森林結果 60 5.3模式比較 61 5.3.1 評鑑指標結果比較 61 5.3.2 淹水潛勢結果比較 65 5.4隨機森林強健性 69 5.5潛在淹水因子重要性 72 第六章 結論與建議 74 6.1 結論 74 6.2 建議 76 參考文獻 77 附錄 81 | |
dc.language.iso | zh-TW | |
dc.title | 結合地理資訊系統與機器學習法於淹水潛勢分析 | zh_TW |
dc.title | Flood susceptibility mapping using geographic information system and machine learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 賴進松,李方中 | |
dc.subject.keyword | 淹水,淹水潛勢評估模式,決策樹,裝袋決策樹,隨機森林, | zh_TW |
dc.subject.keyword | Flood,Flood susceptibility model,Decision tree,Bagging tree,Random forest, | en |
dc.relation.page | 89 | |
dc.identifier.doi | 10.6342/NTU201702575 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-08-07 | |
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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