使用權重組合模型預測雲林縣地層下陷

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：16

、訪客IP：3.17.78.157

姓名

唐子傑(Zi-Jie Tang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

使用權重組合模型預測雲林縣地層下陷
(Using Ensemble Model Predict The Land Subsidence in Yunlin County)

相關論文

★ 基於質譜儀資料使用機器學習辨識克雷伯氏肺炎桿菌之多重抗藥性	★ 結合多種訊號預處理方法於質譜儀資料以辨識細菌對抗生素之抗藥性
★ 利用機器學習預測濁水溪沖積扇區域之地下水位	★ 使用表徵學習和機器學習方法於晶圓線切割機台之異常偵測
★ 基於質譜儀資料利用人工智慧方法辨識革蘭氏陰性菌對環丙沙星抗藥性之特徵峰值	★ 應用數位分身於馬達軸承之異常偵測
★ 基於光誘導介電泳影像處理檢測流體抗藥性	★ 利用機器學習方法基於多類型地層監測資料預測濁水溪沖積扇地區之地層下陷
★ 基於人工智慧模型預測抗菌肽的最小抑菌濃度於特定菌株上	★ 使用語言模型嵌入和不平衡調整之深度學習方法識別多功能抗菌肽
★ 基於深度學習從核醣核酸定序表達譜推斷外周血單核細胞之細胞組成

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

地層下陷是一種不可逆的地理現象，世界各地發生的地層下陷已經嚴重影響了人身安全，而台灣也不例外，所以預測地層下陷並做到提前防治下陷成為目前重要的議題。在早期台灣地層下陷預測較著重在數值研究模擬，相較於數值模擬，人工智慧方法不需提供推估的水文參數和抽水量資料，可以直接透過給定的資料集進行建模，而其他應用人工智慧方式預測的研究也礙於紀錄資料不夠充足無法做到有效的預測，隨著紀錄資料越來越豐富和人工智慧技術越發進步，在此議題上更能夠取得不錯的效果。本研究使用克利金插值法(Kriging Interpolation)對 Global Navigation Satellite System(GNSS)自動固定站資料建立雲林縣全域的資料集和以測站為單位的資料集，分別測試雲林縣全域地層下陷敏感性模型對地層下陷的描述能力和預測數座需要特別關注的 GNSS 測站未來數周地層下陷量以每彌補測站測量資料時所需的時間延遲。在使用了七種特徵建立模型 – 地下水水位、濕度、氣溫、降雨量、日照時數、土地利用、地質組成，在雲林縣全域地層下陷敏感性測試中已經可建立有效描述真實下陷量的模型，指標中 R2
(決定系數 R 平方, Coefficient of Determination) – 0.954、Cor
(決定系數 R 平方,Pearson Correlation Coefficient) – 0.979、MSE( 平均平方誤差, Mean Square Error) – 2.20E-05(單位:平方公尺)；特定 GNSS 測站預測中，使用 8 周訓練資
料建立預測未來8周的地層下陷模型有最穩定的預測結果，組合模型中的 R2– 0.221 、Cor – 0.519、RMSE(平均平方根誤差, Root Mean Square Error) – 0.00207(單位:公尺)。

摘要(英)

Land subsidence is an irreversible geological phenomenon that has had a significant
impact on human safety around the world, and Taiwan is no exception. Therefore,
predicting land subsidence and implementing early prevention measures have become
important issues. In the early stages, land subsidence prediction in Taiwan focused
mainly on numerical research and simulations. Compared to numerical simulations,
artificial intelligence methods do not require estimated hydrological parameters and
pumping data. They can directly model the given dataset. However, other studies that
apply artificial intelligence methods for prediction have been hindered by insufficient
recorded data, making it challenging to achieve effective predictions. With the
increasing availability of recorded data and advancements in artificial intelligence
technology, more promising results can be achieved in this field. This study utilized
the Kriging interpolation to establish a dataset for the entire Yunlin County using
Global Navigation Satellite System (GNSS) automatic reference station data, as well
as station-based datasets. This study utilized the Kriging method to establish a dataset
for the entire Yunlin County using Global Navigation Satellite System (GNSS)
automatic reference station data, as well as station-based datasets. By using seven
features, including groundwater level, humidity, temperature, rainfall, sunshine hours,
land use, and geological composition, the model was able to effectively describe the
real subsidence levels in the Yunlin County-wide land subsidence sensitivity test. The
evaluation indicators were as follows: R
2
(Coefficient of Determination) – 0.954, Cor
(Pearson Correlation Coefficient) – 0.979, MSE (Mean Square Error) – 2.20E-05 (unit:
square meter). Regarding the prediction of specific GNSS stations, the model showed
the most stable results when using an 8-week training dataset to predict subsidence for
the next 8 weeks. The combined model yielded the following results: R2 – 0.221, Cor– 0.519, and RMSE (Root Mean Square Error) – 0.00207(unit: meter).

關鍵字(中)

★ 地層下陷預測
★ 雲林縣濁水溪沖積扇
★ 克利金插值法
★ 機器學習
★ 深度學習
★ 長短期記憶模型
★ 組合權重模型

關鍵字(英)

★ Land subsidence prediction
★ Alluvial fan of Zhuoshuixi, Yunlin County
★ Kriging Interpolation
★ Machine learning
★ Deep learnging
★ Long Short -Term Memory
★ Ensemble model

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 vi
表目錄 viii
第一章緒論 1
1-1 研究背景 1
1-2 文獻回顧 3
1-3 動機與目標 6
第二章材料和方法 7
2-1 研究區域 7
2-2 資料收集 8
2-3 資料前處理 12
2-3-1 資料清理 12
2-3-2 轉化資料型態 14
2-4 方法 17
2-4-1 前處理方法 17
2-4-2 預測模型方法 22
2-5 評估指標 28
第三章結果 29
3-1 雲林縣全域地層下陷敏感性預測 29
3-2 特定 GNSS 測站預測 33
第四章討論和結論 55
4-1 討論 55
4-2 結論 58
參考文獻 59

參考文獻

[1] 楊偉甫,"台灣地區水資源利用現況與未來發展問題,"臺灣水環境再生協會,
用水合理化與新生水水源開發論壇,2010.
[2] 台灣經濟部水利署, "107臺灣水文環境情勢專刊", 2019.
[3] 鍾侑達, 郭峻菖, and 陳昶憲, "台灣區域降雨趨勢分析,"農業工程學報, 55(4):
p. 1-18, 2009.
[4] 徐國錦, and 林政偉, "地層下陷成因與防治," 土木水利, 48(6): p. 68-74, 2021
[5] 柳志錫, 廖志中, and 潘以文, "複雜含水地層之抽水沉陷行為," 2003.
[6] Lin, C.-W., Hwung, H.-H., Hsiao, S.-C., Yeh, C.-L., and Hsu, J.-T., "Land
Subsidence Caused by Groundwater Exploitation in Yunlin, Taiwan," in ICHE
2016. Proceedings of the 12th International Conference on Hydroscience &
Engineering, November 6-10, Tainan, Taiwan. 2016.
[7] Rahmati, O., et al., "Land subsidence modelling using tree-based machine learning
algorithms," Science of the Total Environment, 672: p. 239-252, 2019.
[8] Eghrari, Z., Delavar, M., R., Zare, M., Beitollahi, A., and Nazari, B., "Land
Subsidence Susceptibility Mapping Using Machine Learning Algorithms," ISPRS
Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences,
10: p. 129-136, 2023.
[9] Hakim, W.-L., Achmad, A.-R., and Lee,C.-W., "Land subsidence susceptibility
mapping in jakarta using functional and meta-ensemble machine learning algorithm
based on time-series InSAR data," Remote Sensing, 12(21): p. 3627, 2020.
[10] Radman, A., Akhoondzadeh, M., and Hosseiny, B., "Integrating InSAR and deeplearning for modeling and predicting subsidence over the adjacent area of Lake
Urmia, Iran," GIScience & Remote Sensing, 58(8): p. 1413-1433, 2021.
[11] Hill, P., Biggs, J., Ponce-López, V., and Bull, D., "Time‐Series Prediction
Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data," Journal of
Geophysical Research: Solid Earth, 126(3): p. e2020JB020176, 2021.
[12] Zhou, D., Zuo, X., and Zhao, Z., "Constructing a large-scale urban land subsidence prediction method based on neural network algorithm from the perspective of
multiple factors," Remote Sensing, 14(8): p. 1803, 2022.
[13] Li, H., Zhun, L., Gong, H., Sun, H., and Yu, J., "Land subsidence modelling using
a long short-term memory algorithm based on time-series datasets," Proceedings
of the International Association of Hydrological Sciences, 382: p. 505-510, 2020.
[14] 王振綱, "利用機器學習方法基於多類型地層監測資料預測濁水溪沖積扇地
區之地層下陷, " 碩士, 資訊工程研究所, 國立中央大學, 桃園縣, 2021.
[15] Ku, C.-Y., and Liu, C.-Y., "Modeling of land subsidence using GIS-based artificial
neural network in Yunlin County," Taiwan. Scientific Reports, 13(1): p. 4090,
2023.
[16] Oliver, M.-A., and Webster, R., "Kriging: a method of interpolation for
geographical information systems," International Journal of Geographical
Information System, 4(3): p. 313-332, 1990.
[17] Pedregosa, F., et al., "Scikit-learn: Machine learning in Python," the Journal of
machine Learning research, 12: p. 2825-2830, 2011.
[18] Hancock, J.-T., and Khoshgoftaar, T.-M., "CatBoost for big data: an
interdisciplinary review," Journal of big data, 7(1): p. 1-45, 2020.
[19] Staudemeyer, R.-C., and Morris, E.-R., "Understanding LSTM--a tutorial into long
short-term memory recurrent neural networks," arXiv preprint arXiv:1909.09586,
2019.
[20] Luong, M.-T., Pham, H., and Manning, C.-D., "Effective approaches to attentionbased neural machine translation," arXiv preprint arXiv:1508.04025, 2015

指導教授

洪炯宗吳立青(Jorng-Tzong Horng)

審核日期

2023-7-27

推文