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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.參考文獻 i. 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(2010). “Landslide susceptibility mapping in Injae, Korea, using a decision tree,” Engineering Geology, vol. 116, no. 3-4, pp. 274–283, 2Young NE, Anderson RS, Chignell SM, Vorster AG, Lawrence R, Evangelista PH. A survival guide to Landsat preprocessing. Ecology. 2017 Apr;98(4):920-932. doi: 10.1002/ecy.1730. Epub 2017 Mar 20. Erratum in: Ecology. 2021 Nov;102(11):e03508. PMID: 28072449.Google Earth Engine Guide “Compositing and Mosaicking” (2022) website:https://developers.google.com/earthengine/guides/ic_composite_mosaicUSGS 2022 https://www.usgs.gov/landsat-missions/landsat-enhanced-vegetationindexTensorflow 2022 https://www.tensorflow.org/?hl=zh-twEsri 2022 https://www.esri.com/en-us/home 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 Gasselt en_US dc.contributor.author (Authors) 曾璟庭 zh_TW dc.contributor.author (Authors) Zeng, Jing-Ting en_US dc.creator (作者) 曾璟庭 zh_TW dc.creator (作者) Zeng, Jing-Ting en_US dc.date (日期) 2023 en_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) G0110257029 en_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) 110257029 zh_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 IIAbstract IIIContent IVFigures VITables XChapter 1. Introduction 11.1 Motivation and Background 11.2 Research Objectives 61.3 Research Structure and Conceptual Framework 7Chapter 2. Literature Review 82.1 Landslide Detection 82.2 Landslides Detection Research before AI 102.3 Landslides Detection Research in the Era of AI 13Chapter3. Methodology 293.1 Study area 293.2 Workflow 333.3 Research data and tool 353.4 Study method and Image preprocessing 39Chapter 4. Result 524.1 Maps result 524.2 The combination of three maps for analyzing the landslide area 63Chapter 5. Conclusion and Suggestion 725.1 Conclusion 725.2 Suggestion and Limitation 73Reference 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/#G0110257029 en_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) Landslide en_US dc.subject (關鍵詞) Landslide detection en_US dc.subject (關鍵詞) Landslide susceptibility map en_US dc.subject (關鍵詞) Land use and land cover map en_US dc.subject (關鍵詞) Disaster planing en_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 Taiwan en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) i. 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