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DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 韓仁毓 | zh_TW |
dc.contributor.advisor | Jen-Yu Han | en |
dc.contributor.author | 林培宇 | zh_TW |
dc.contributor.author | Pei-Yu Lin | en |
dc.date.accessioned | 2024-03-05T16:22:40Z | - |
dc.date.available | 2024-03-06 | - |
dc.date.copyright | 2024-03-05 | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-02-06 | - |
dc.identifier.citation | Abdollahi, S., Pourghasemi, H.R., Ghanbarian, G.A. and Safaeian, R., 2018. Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions, Bulletin of Engineering Geology and the Environment, 78(6):4017-4034.
Anselin, L., 1995. Local indicators of spatial association—LISA, Geographical analysis, 27(2):93-115. Arabameri, A., Saha, S., Roy, J., Tiefenbacher, J.P., Cerda, A., Biggs, T., Pradhan, B., Thi Ngo, P.T. and Collins, A.L., 2020. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility, Sci Total Environ, 726:138595. Bagheri-Gavkosh, M., Hosseini, S.M., Ataie-Ashtiani, B., Sohani, Y., Ebrahimian, H., Morovat, F. and Ashrafi, S., 2021. Land subsidence: A global challenge, Science of The Total Environment, 778:146193. Bandara, K., Hyndman, R.J. and Bergmeir, C., 2021. MSTL: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns, arXiv preprint arXiv:2107.13462. Biot, M.A., 1941. General theory of three‐dimensional consolidation, Journal of applied physics, 12(2):155-164. Breiman, L., 2001. Random forests, Machine learning, 45(1):5-32. Chen, B., Gong, H., Chen, Y., Li, X., Zhou, C., Lei, K., Zhu, L., Duan, L. and Zhao, X., 2020. Land subsidence and its relation with groundwater aquifers in Beijing Plain of China, Sci Total Environ, 735:139111. Chen, C.-H., Wang, C.-H., Hsu, Y.-J., Yu, S.-B. and Kuo, L.-C., 2010. Correlation between groundwater level and altitude variations in land subsidence area of the Choshuichi Alluvial Fan, Taiwan, Engineering Geology, 115(1-2):122-131. Chen, T. and Guestrin, C. Xgboost: A scalable tree boosting system, Proceedings of the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785-794. Chen, Y., He, Y., Zhang, L., Chen, Y., Pu, H., Chen, B. and Gao, L., 2021. Prediction of InSAR deformation time-series using a long short-term memory neural network, International Journal of Remote Sensing, 42(18):6919-6942. Chu, B. and Qureshi, S., 2022. Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast US GDP Growth, Computational Economics:1-43. Chu, H.-J., Ali, M.Z. and Burbey, T.J., 2021. Development of spatially varying groundwater-drawdown functions for land subsidence estimation, Journal of Hydrology: Regional Studies, 35:100808. Ciampalini, A., Bardi, F., Bianchini, S., Frodella, W., Del Ventisette, C., Moretti, S. and Casagli, N., 2014. Analysis of building deformation in landslide area using multisensor PSInSAR™ technique, International Journal of Applied Earth Observation and Geoinformation, 33:166-180. Cleveland, R.B., Cleveland, W.S., McRae, J.E. and Terpenning, I., 1990. STL: A seasonal-trend decomposition, J. Off. Stat, 6(1):3-73. Cutler, D.R., Edwards Jr, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J. and Lawler, J.J., 2007. Random forests for classification in ecology, Ecology, 88(11):2783-2792. Fitts, C.R., 2013. 5 - Hydrology and Geology, Groundwater Science (Second Edition) (C.R. Fitts, editor), Academic Press, Boston, pp. 123-186. Fotheringham, A.S., Brunsdon, C. and Charlton, M., 2003. Geographically weighted regression: the analysis of spatially varying relationships, John Wiley & Sons, Freund, Y. and Schapire, R.E., 1997. A decision-theoretic generalization of on-line learning and an application to boosting, Journal of computer and system sciences, 55(1):119-139. Friedman, J.H., 2001. Greedy function approximation: a gradient boosting machine, Annals of statistics:1189-1232. Gabrysch, R., 1982. Ground-water withdrawals and land-surface subsidence in the Houston-Galveston region, Texas, 1906-80), US Geological Survey. Galloway, D.L., Jones, D.R. and Ingebritsen, S.E., 1999. Land subsidence in the United States, Geological Survey (USGS), Galloway, D.L., Erkens, G., Kuniansky, E.L. and Rowland, J.C., 2016. Preface: Land subsidence processes, Hydrogeology Journal, 24(3):547-550. Gao, Y., Cheng, J., Meng, H. and Liu, Y., 2019. Measuring spatio-temporal autocorrelation in time series data of collective human mobility, Geo-spatial Information Science, 22(3):166-173. Guzy, A. and Malinowska, A.A., 2020. State of the art and recent advancements in the modelling of land subsidence induced by groundwater withdrawal, Water, 12(7):2051. Hastie, T., Tibshirani, R., Friedman, J.H. and Friedman, J.H., 2009. The elements of statistical learning: data mining, inference, and prediction, Springer, Hewamalage, H., Ackermann, K. and Bergmeir, C., 2023. Forecast evaluation for data scientists: common pitfalls and best practices, Data Mining and Knowledge Discovery, 37(2):788-832. Hochreiter, S. and Schmidhuber, J., 1997. Long Short-Term Memory, Neural Computation, 9(8):1735-1780. Hsu, W.-C., Chang, H.-C., Chang, K.-T., Lin, E.-K., Liu, J.-K. and Liou, Y.-A., 2015. Observing land subsidence and revealing the factors that influence it using a multi-sensor approach in Yunlin County, Taiwan, Remote Sensing, 7(6):8202-8223. Hung, W.-C., Hwang, C., Liou, J.-C., Lin, Y.-S. and Yang, H.-L., 2012. Modeling aquifer-system compaction and predicting land subsidence in central Taiwan, Engineering Geology, 147:78-90. Hyndman, R.J. and Athanasopoulos, G., 2018. Forecasting: principles and practice, OTexts, Ilic, I., Görgülü, B., Cevik, M. and Baydoğan, M.G., 2021. Explainable boosted linear regression for time series forecasting, Pattern Recognition, 120:108144. James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An Introduction to Statistical. With Applications in R), Springer (New York). 426p. https://doi. org/10.1007/978-1-4614-7138-7. Kumar, S., Kumar, D., Donta, P.K. and Amgoth, T., 2022. Land subsidence prediction using recurrent neural networks, Stochastic Environmental Research and Risk Assessment, 36(2):373-388. Landwehr, N., Hall, M. and Frank, E., 2005. Logistic model trees, Machine learning, 59:161-205. Minderhoud, P., Coumou, L., Erban, L., Middelkoop, H., Stouthamer, E. and Addink, E., 2018. The relation between land use and subsidence in the Vietnamese Mekong delta, Science of The Total Environment, 634:715-726. Motagh, M., Shamshiri, R., Haghighi, M.H., Wetzel, H.-U., Akbari, B., Nahavandchi, H., Roessner, S. and Arabi, S., 2017. Quantifying groundwater exploitation induced subsidence in the Rafsanjan plain, southeastern Iran, using InSAR time-series and in situ measurements, Engineering Geology, 218:134-151. Numata, K. and Tanaka, K., 2020. Stochastic Threshold Model Trees: A Tree-Based Ensemble Method for Dealing with Extrapolation, arXiv preprint arXiv:2009.09171. Porat, I., Shoshany, M. and Frenkel, A., 2012. Two phase temporal-spatial autocorrelation of urban patterns: Revealing focal areas of re-urbanization in Tel Aviv-Yafo, Applied Spatial Analysis and Policy, 5:137-155. Quinlan, J.R. Learning with continuous classes, Proceedings of the 5th Australian joint conference on artificial intelligence, 1992, (World Scientific), pp. 343-348. Rahmati, O., Falah, F., Naghibi, S.A., Biggs, T., Soltani, M., Deo, R.C., Cerdà, A., Mohammadi, F. and Bui, D.T., 2019. Land subsidence modelling using tree-based machine learning algorithms, Science of The Total Environment, 672:239-252. Salles, R., Belloze, K., Porto, F., Gonzalez, P.H. and Ogasawara, E., 2019. Nonstationary time series transformation methods: An experimental review, Knowledge-Based Systems, 164:274-291. Smith, R.G. and Majumdar, S., 2020. Groundwater Storage Loss Associated With Land Subsidence in Western United States Mapped Using Machine Learning, Water Resources Research, 56(7). Stone, M., 1974. Cross‐validatory choice and assessment of statistical predictions, Journal of the royal statistical society: Series B (Methodological), 36(2):111-133. Tashman, L.J., 2000. Out-of-sample tests of forecasting accuracy: an analysis and review, International journal of forecasting, 16(4):437-450. Terzaghi, K., 1943. Theory of Consolidation, Theoretical Soil Mechanics), pp. 265-296. Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Pradhan, B., Chen, W., Khosravi, K., Panahi, M., Bin Ahmad, B. and Saro, L., 2018. Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms, Sensors (Basel), 18(8). Vapnik, V., 1999. The nature of statistical learning theory, Springer science & business media, Wang, Y.-Q., Wang, Z.-F. and Cheng, W.-C., 2019. A review on land subsidence caused by groundwater withdrawal in Xi’an, China, Bulletin of Engineering Geology and the Environment, 78(4):2851-2863. Xu, T. and Liang, F., 2021. Machine learning for hydrologic sciences: An introductory overview, Wiley Interdisciplinary Reviews: Water, 8(5):e1533. Zhang, H., Nettleton, D. and Zhu, Z., 2019. Regression-enhanced random forests, arXiv preprint arXiv:1904.10416. 台灣省政府交通處港灣技術研究所,1993。海岸土層下陷行為與預測研究,臺中。 張良正,2012。臺灣地區地下水區水文地質調查及地下水資源評估-地下水補注潛勢評估與地下水模式建置(4/4)。 梁昇、黃天福,1984。地下水介紹(一)地下水的存在與含水層,水土保持學報,16、17(1):27-36 陳旭昇,2013。時間序列分析:總體經濟與財務金融之應用,臺灣東華。 經濟部中央地質調查所,1999。濁水溪沖積扇水文地質調查研究總報告,台北。 經濟部水利署,2022。111年度彰化與雲林地區地層下陷監測及分析,詮華國土測繪有限公司。 經濟部水利署,2019。多元整合空間資訊技術於地層下陷監測之應用。上網日期:2024年2月1日。檢自:https://epaper.wra.gov.tw/Article_Detail.aspx?s=5139&n=30177 經濟部水資源局,1999。台灣地區地下水觀測網整體計畫第一期(81~87年度)成果彙編,臺北。 鄧教宏、李哲瑋、羅偉誠、王常勉,2018。載重效率對於未飽和土壤壓密過程之影響評估,農業工程學報,64(4):1-22 | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92119 | - |
dc.description.abstract | 地層下陷是一種土層垂直沉降之地質災害,由於經常發生在用水量較大之農業、住宅地區,過去常將其歸咎於超抽地下水之後果。地層下陷分析模式可分為統計經驗法、理論分析法與人工智慧方法。前兩者大多專注於探討地層下陷與地下水位變化的關聯性,然而統計經驗法需蒐集大量觀測資料以建立誤差小之分析模式,理論分析法則須透過現地觀測與室內壓密試驗估計水文地質參數以描述一地區之土壤透水性質與壓縮特性才得以有效模擬地層下陷行為。上述方法並不適用於缺乏觀測資料且水文地質參數未經估計之地區,且水文地質參數也因其空間異質性而僅適用於單一地區之地層下陷模擬。雖然機器學習、深度學習等人工智慧方法可以直接建構各影響因子與土層壓縮量之非線性關係,在處理具有趨勢性觀測資料之預測任務,如未來之地層下陷量預測,模型將因為測試資料與訓練資料的值域不同,而無法獲得良好的樣本外預測結果。因此,本研究欲建構一適用於複數地區之地層下陷分析模式以提供大範圍之地層下陷趨勢,預計可作為政府實施自然保育、用水管制政策之依據。本研究之目標為二:(一)建立地層下陷擬合模型,並探討各影響因素與地層下陷之間的作用機制,(二)利用現有觀測資料對地層下陷執行樣本外預測,並比較原始觀測資料與去趨勢性觀測資料之預測成果。本研究以空間自相關分析評估105至110年來主要地層下陷地區,並透過機器學習方法模擬雲林地區近年來之地表高程變化,模型之自變數可分為地下水位因子、土地使用因子與地質地形因子。研究成果顯示考量上述地層下陷影響因素後,本研究建構之地層下陷分析模式於單一GPS固定站之均方根誤差約為3公分,最低可至0.2公分;應用於推估未來地表下陷量的準確度可達1.4公分,且具備捕捉地表因地下水位變化造成之季節性波動的能力。此外,部分GPS固定站之土地使用因子對地表高程變化模擬之貢獻度甚至高於地下水位因子。綜合上述,本研究認為短期內的地下水位僅能描述季節性之地層下陷行為,必須加入其他輔助資料才能完整描述其空間異質性。並且以機器學習方法模擬地層下陷時,應根據不同土層的沉陷特性區分趨勢性與季節性,且分別建立兩個模型以提高未來地層下陷趨勢變化的預測準確度。 | zh_TW |
dc.description.abstract | Land subsidence is a type of geohazard that causes vertical settlement of the earth’s surface. Often observed in agricultural and residential regions, land subsidence was attributed to the over-exploitation of groundwater. Theoretical approaches based on the relationship between ground surface level and groundwater level require field or laboratory experiments to obtain more accurate parameters to describe the hydrogeological properties of rocks. However, these approaches are not applicable in places where hydrogeological datasets are unknown. While AI methods can construct the non-linear relationship between land subsidence and its effective drivers, predicting trends like future subsidence using machine learning encounters challenges due to data discrepancies. Understanding the future trend of land subsidence in these places allows the government to imply appropriate mitigation strategies, thereby avoiding economic and environmental losses. The objective of this study is twofold: first, constructing a land subsidence prediction model that can be applied without prior knowledge of hydrogeological properties; second, forecasting the amount of land subsidence with existing observations. In this study, machine learning techniques were used to model the surface ground height in Yunlin, Taiwan from 2016 to 2020. Machine learning techniques can show a non-linear relationship between different datasets, including land-use change, lithology, and topological conditions. The results indicated over 99% of the variance in the ground surface level could be explained, and the root mean squared error of simulation was below 3 cm. Results also show that our model significantly improves predictions after combining time series decomposition, providing precise subsidence forecasting with 1.4cm accuracy. According to our simulation, land subsidence might be more strongly influenced by land use type than changes in groundwater level in some circumstances. These findings suggest that when simulating land subsidence, developing separate models for trend and seasonal components can enhance the accuracy of predicting future trends in land subsidence. Short-term groundwater level changes can only depict the seasonal behavior of land subsidence; auxiliary data should also be considered. This study can provide a reference for nature conservation and water resources management in Taiwan. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-03-05T16:22:39Z No. of bitstreams: 0 | en |
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dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii 目次 iv 圖次 vii 表次 x 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 研究流程 4 1.4 論文架構 5 第二章 文獻回顧 6 2.1 地下水資源開發引致之地層下陷 6 2.2 地層下陷模式化方法 8 2.2.1 理論分析法 8 2.2.2 統計經驗法 9 2.2.3 人工智慧方法 10 2.3 地層下陷影響因素 11 2.3.1 地下水位與含水層壓密 11 2.3.2 地質環境與地形條件 12 2.3.3 土地使用現況與土地使用變遷 13 2.4 小結 14 第三章 研究方法 15 3.1 空間自相關分析 15 3.2 地層下陷影響因素 17 3.2.1 地下水位因子 17 3.2.2 土地使用因子 18 3.2.3 地質地形因子 18 3.3 地層下陷模式化方法 19 3.3.1 決策樹 19 3.3.2 引導聚集演算法與隨機森林 20 3.3.3 提升法 21 3.3.4 模型樹 22 3.4 時間序列分解 23 3.5 精度評估 24 3.6 小結 25 第四章 實驗成果與分析 26 4.1 現地區域資料 26 4.1.1 地層下陷監測設備分布 26 4.1.2 水文地質環境概述 27 4.2 空間自相關分析 28 4.3 地層下陷影響因素 30 4.3.1 地下水位因子 31 4.3.2 土地使用因子 35 4.3.3 地質地形因子 35 4.4 預測分析與成果驗證 37 4.4.1 地層下陷模式建構 38 4.4.2 地層下陷未來趨勢推估 50 第五章 結論與建議 59 5.1 結論 59 5.2 建議與未來工作 60 參考文獻 62 附錄 67 | - |
dc.language.iso | zh_TW | - |
dc.title | 應用機器學習方法於地層下陷分析模式建構與未來趨勢推估 | zh_TW |
dc.title | Modeling Land Subsidence and Forecasting its Future Trends with Machine Learning Techniques | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 楊國鑫;吳日騰 | zh_TW |
dc.contributor.oralexamcommittee | Kuo-Hsin Yang;Rih-Teng Wu | en |
dc.subject.keyword | 地層下陷預測,衛星定位系統,皮爾森空間自相關,時間序列分解,機器學習, | zh_TW |
dc.subject.keyword | Land Subsidence Forecasting,Global Positioning System (GPS),Autocorrelation of Pearson,Time Series Decomposition,Machine Learning, | en |
dc.relation.page | 70 | - |
dc.identifier.doi | 10.6342/NTU202400522 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2024-02-11 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 土木工程學系 | - |
顯示於系所單位: | 土木工程學系 |
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