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題名 運用機器學習綜合分析山崩偵測和潛感圖以評估臺灣南部風險地區
Landslide Detection and Integrated Susceptibility Analysis using Machine Learning for Areas at Risk in Southern Taiwan
作者 曾璟庭
Zeng, Jing-Ting
貢獻者 范噶色
Stephan Van Gasselt
曾璟庭
Zeng, Jing-Ting
關鍵詞 山崩
山崩偵測
山崩潛感圖
機器學習
土地利用與覆蓋地圖
防災規劃
Landslide
Landslide detection
Landslide susceptibility map
Land use and land cover map
Disaster planing
日期 2023
上傳時間 2-Aug-2023 13:40:40 (UTC+8)
摘要 臺灣位於環太平洋地震帶和易受北太平洋西部颱風影響的地區，每年都遭受著多種自然災害的侵襲，其中山崩災害為影響相當劇烈的一種。近年來，針對山崩的研究逐漸增多，包括山崩偵測技術以及山崩潛感圖的製作等，然而這些研究往往缺乏整合並進行深入的後續分析。
本研究選擇高雄作為研究地區，採用機器學習方法，融合山崩偵測圖和山崩潛感圖以尋找潛在的危險地區。再進一步利用半徑 5 公尺、50 公尺、100公尺和 500 公尺的環域分析，結合土地利用與覆蓋地圖，評估近期不同範圍內受到山崩威脅的程度，並確定受到影響的住宅區域。研究結果顯示，危害地區主要集中於靠近山區且臨近河流的區域。特別是桃源、六龜和甲仙區的住宅區，存在著較高的山崩災害風險，且將近一半的高雄山區具有潛在山崩風險。這些發現進一步強調了這些地區面臨山崩災害的嚴重性。
綜上所述，本研究以機器學習方法為基礎，整合山崩偵測、山崩潛感圖和土地利用與覆蓋地圖，成功地找出高雄地區近期受到山崩不同威脅程度的區域。透過具體的危害風險分析結果，本研究為山崩防災提供了重要的參考依據，有助於相關當局在進行風險評估和防災規劃時做出明確且有效的決策。這項研究的成果對於提升山崩防災能力、保護人民的生命和財產安全具有重要的意義。
Taiwan is located at the junction of converging tectonic plates, also, the monsoon climate and frequent typhoons have a tremendous impact on Taiwan’s environment due to its geographic location, which is affected by various natural disasters, among which landslides are of significant concern. In recent years, research focusing on landslides has increased, including landslide detection techniques and landslide susceptibility mapping. However, these studies often lack integration and subsequent analysis.
This study focuses on Kaohsiung City and employs machine learning techniques to integrate a landslide detection map and landslide susceptibility map (LSM) to identify areas at risk. Furthermore, by utilizing buffer analysis with a radius of 5 meters, 50 meters, 100 meters, and 500 meters, in conjunction with a land use and land cover (LULC) map, the study evaluates the different degrees of landslide threat levels within proximity ranges and identifies residential areas that may be affected. The results reveal that hazardous areas are mainly concentrated in mountainous regions and close to rivers. Moreover, residential areas in Taoyuan District, Liouguei District, and Jiasian District face higher landslide risks. It is noteworthy that nearly half of the mountainous areas in Kaohsiung exhibit potential landslide hazards. These findings underscore the severity of landslide disasters in these regions.
In conclusion, this study utilizes machine learning techniques to successfully integrate landslide detection map, landslide susceptibility map, and land use and land cover map, effectively identifying areas in the Kaohsiung region with different degrees of landslide threats. The specific analysis of landslide risks provided by this study serves as a valuable reference for landslide disaster mitigation, assisting relevant government departments in making informed and effective decisions regarding risk assessment and disaster planning. The outcomes of this research are of great significance in enhancing landslide disaster resilience and safeguarding the lives and properties of the population.
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張崑宗、高啟軒、王主一、劉進金.(2010). 暴雨型崩塌地自動判釋及特徵分析之研究. 航測及遙測學刊 第十五卷 第 1 期 79-95
張崑宗、劉進金.(2006). 利用神經網路方法於高解析衛星影像及地形資料之崩塌地辨識-以九份二山為例. 航測及遙測學刊 第十一卷 第 二 期 161-174
詹勳全, 陳柏安, & 溫祐霆.(2015). 以地理資訊系統搭配羅吉斯回歸、不安定指數及支撐向量機建立山崩潛感之分析比較. 中華水土保持學報, 213-222.
陳怡睿, 謝舜傑, 陳景文, & 倪柏寧.(2013). 基因演算法自動演化類神經網路應用於山崩災害之評估.

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描述 碩士
國立政治大學
地政學系
110257029
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110257029
資料類型 thesis
dc.contributor.advisor 范噶色zh_TW
dc.contributor.advisor Stephan Van Gasselten_US
dc.contributor.author (Authors) 曾璟庭zh_TW
dc.contributor.author (Authors) Zeng, Jing-Tingen_US
dc.creator (作者) 曾璟庭zh_TW
dc.creator (作者) Zeng, Jing-Tingen_US
dc.date (日期) 2023en_US
dc.date.accessioned 2-Aug-2023 13:40:40 (UTC+8)-
dc.date.available 2-Aug-2023 13:40:40 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2023 13:40:40 (UTC+8)-
dc.identifier (Other Identifiers) G0110257029en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146467-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 地政學系zh_TW
dc.description (描述) 110257029zh_TW
dc.description.abstract (摘要) 臺灣位於環太平洋地震帶和易受北太平洋西部颱風影響的地區，每年都遭受著多種自然災害的侵襲，其中山崩災害為影響相當劇烈的一種。近年來，針對山崩的研究逐漸增多，包括山崩偵測技術以及山崩潛感圖的製作等，然而這些研究往往缺乏整合並進行深入的後續分析。
本研究選擇高雄作為研究地區，採用機器學習方法，融合山崩偵測圖和山崩潛感圖以尋找潛在的危險地區。再進一步利用半徑 5 公尺、50 公尺、100公尺和 500 公尺的環域分析，結合土地利用與覆蓋地圖，評估近期不同範圍內受到山崩威脅的程度，並確定受到影響的住宅區域。研究結果顯示，危害地區主要集中於靠近山區且臨近河流的區域。特別是桃源、六龜和甲仙區的住宅區，存在著較高的山崩災害風險，且將近一半的高雄山區具有潛在山崩風險。這些發現進一步強調了這些地區面臨山崩災害的嚴重性。
綜上所述，本研究以機器學習方法為基礎，整合山崩偵測、山崩潛感圖和土地利用與覆蓋地圖，成功地找出高雄地區近期受到山崩不同威脅程度的區域。透過具體的危害風險分析結果，本研究為山崩防災提供了重要的參考依據，有助於相關當局在進行風險評估和防災規劃時做出明確且有效的決策。這項研究的成果對於提升山崩防災能力、保護人民的生命和財產安全具有重要的意義。		zh_TW
dc.description.abstract (摘要) Taiwan is located at the junction of converging tectonic plates, also, the monsoon climate and frequent typhoons have a tremendous impact on Taiwan’s environment due to its geographic location, which is affected by various natural disasters, among which landslides are of significant concern. In recent years, research focusing on landslides has increased, including landslide detection techniques and landslide susceptibility mapping. However, these studies often lack integration and subsequent analysis.
This study focuses on Kaohsiung City and employs machine learning techniques to integrate a landslide detection map and landslide susceptibility map (LSM) to identify areas at risk. Furthermore, by utilizing buffer analysis with a radius of 5 meters, 50 meters, 100 meters, and 500 meters, in conjunction with a land use and land cover (LULC) map, the study evaluates the different degrees of landslide threat levels within proximity ranges and identifies residential areas that may be affected. The results reveal that hazardous areas are mainly concentrated in mountainous regions and close to rivers. Moreover, residential areas in Taoyuan District, Liouguei District, and Jiasian District face higher landslide risks. It is noteworthy that nearly half of the mountainous areas in Kaohsiung exhibit potential landslide hazards. These findings underscore the severity of landslide disasters in these regions.
In conclusion, this study utilizes machine learning techniques to successfully integrate landslide detection map, landslide susceptibility map, and land use and land cover map, effectively identifying areas in the Kaohsiung region with different degrees of landslide threats. The specific analysis of landslide risks provided by this study serves as a valuable reference for landslide disaster mitigation, assisting relevant government departments in making informed and effective decisions regarding risk assessment and disaster planning. The outcomes of this research are of great significance in enhancing landslide disaster resilience and safeguarding the lives and properties of the population.		en_US
dc.description.tableofcontents 謝誌 I
摘要 II
Abstract III
Content IV
Figures VI
Tables X
Chapter 1. Introduction 1
1.1 Motivation and Background 1
1.2 Research Objectives 6
1.3 Research Structure and Conceptual Framework 7
Chapter 2. Literature Review 8
2.1 Landslide Detection 8
2.2 Landslides Detection Research before AI 10
2.3 Landslides Detection Research in the Era of AI 13
Chapter3. Methodology 29
3.1 Study area 29
3.2 Workflow 33
3.3 Research data and tool 35
3.4 Study method and Image preprocessing 39
Chapter 4. Result 52
4.1 Maps result 52
4.2 The combination of three maps for analyzing the landslide area 63
Chapter 5. Conclusion and Suggestion 72
5.1 Conclusion 72
5.2 Suggestion and Limitation 73
Reference 74		zh_TW
dc.format.extent 8281839 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110257029en_US
dc.subject (關鍵詞) 山崩zh_TW
dc.subject (關鍵詞) 山崩偵測zh_TW
dc.subject (關鍵詞) 山崩潛感圖zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 土地利用與覆蓋地圖zh_TW
dc.subject (關鍵詞) 防災規劃zh_TW
dc.subject (關鍵詞) Landslideen_US
dc.subject (關鍵詞) Landslide detectionen_US
dc.subject (關鍵詞) Landslide susceptibility mapen_US
dc.subject (關鍵詞) Land use and land cover mapen_US
dc.subject (關鍵詞) Disaster planingen_US
dc.title (題名) 運用機器學習綜合分析山崩偵測和潛感圖以評估臺灣南部風險地區zh_TW
dc.title (題名) Landslide Detection and Integrated Susceptibility Analysis using Machine Learning for Areas at Risk in Southern Taiwanen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) i. Chinese references

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