深度學習與資料擴增於山崩監測預測之可行性評估

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姓名

邱鈺智(Yu-Zhi Qiu) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

深度學習與資料擴增於山崩監測預測之可行性評估
(Development of Deep learning and GAN applied to landslide prediction)

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至系統瀏覽論文 (2024-9-19以後開放)

摘要(中)

隨著氣候的劇烈變遷，山崩破壞也越加嚴重，因此如果能預測山崩就能減少人員傷亡以及財產損失，而現今山崩預測研究中又以深度學習（Deep Learning）的成長最為顯著，但利用深度學習預測山崩上有一困難點為山崩監測資料較為缺乏，如地下水位、邊坡位移量獲取相對於其他水文或氣象資料更難以取得。而在深度學習模型的訓練過程中，是需要有大量的資料才能有良好的預測結果，故本研究同步提出使用生成對抗網路（Generative Adversarial Networks, GAN），以2007-2008年廬山監測資料為範例進行資料擴增，並建置支持向量機（Support Vector Machine, SVM）、長短期記憶網路（Long Short-Term Memory Network, LSTM）和門閥限循環單元網路（Gated Recurrent Unit Network, GRU）三種預測模型，除探討利用原始資料進行訓練預測外，本研究並利用資料擴增後之資料集探討預測結果。實驗顯示使用原始資料且預測成效較差的水位(孔位B01、B04、B07)，其GRU預測R-Square值分別為 0.690、0.347、0.759，再使用GAN資料擴增後，B01、B04、B07 GRU預測R-Square值提升至0.943、0.901、0.760。最後使用文獻兩組山崩實際案例進行比較可得知，在資料擴增後預測山崩位移皆有更佳預測結果。本研究所提出深度學習應用可應用對於缺乏山崩資料而想使用深度學習預測之參考依據。

摘要(英)

With the dramatic climate changes, landslides damage has become more serious. Therefore, if landslides can be successfully predicted, casualties and property damage can be reduced. For various landslide predictions, the growth of Deep Learning is the most significant. However, a major difficulty in landslide prediction is obtaining landslide monitoring data, such as groundwater level and slope displacement. They are more challenging to obtain than other hydrological or meteorological data. In the deep learning model training process, a large amount of data is necessary for good prediction results. Therefore, this study proposes to combine Generative Adversarial Networks, using data augmentation with 2007-2008 Lushan monitoring data as an example, and build three prediction models: Support Vector Machine (SVM), Long Short-Term Memory(LSTM), and Gated Recurrent Unit (GRU), to compare whether the augmented data has more predictive results than using the original data. Experiment results show that GRU with original data can predict water levels and R-Square values of B01, B04, and B07 are 0.690, 0.347, and 0.759. Using GAN data augmentation for water levels has the predicted R-Square values of B01, B04, and B07 of 0.943, 0.901, and 0.760. The latter one has significantly improved. This study further evaluates deep learning applications that can be applied in other cases. Consequently, the proposed GRU with the GAN method is a feasible approach for landslide prediction.

關鍵字(中)

★ 深度學習
★ 山崩預測
★ 資料擴增
★ 生成對抗網路
★ 支持向量機
★ 長短期記憶神經網路
★ 門閥循環單元網路

關鍵字(英)

★ Deep Learning
★ Landslide Prediction
★ Data Augmentation
★ GAN
★ SVM
★ LSTM
★ GRU

論文目次

摘要 i
Abstract ix
誌謝 x
目錄 xi
圖目錄 xiii
表目錄 xviii
第一章緒論 1
1-1 研究背景 1
1-2 研究問題與目的 2
第二章文獻回顧 4
2-1 山崩類別 4
2-2 山坡監測儀器介紹 10
2-3 人工智慧基本原理 22
2-3-1 機器學習 24
2-3-2 深度學習 32
2-4 深度學習於山崩之應用文獻 45
2-5 文獻回顧總結 50
第三章研究方法 51
3-1 研究標地-廬山介紹 51
3-1-1 廬山地形與地質簡介 52
3-1-2 廬山監測項目說明 55
3-1-3 廬山監測資料預處理 66
3-2 資料擴增模型建置 97
3-3 預測山崩模型建置 99
3-3-1 支持向量機（SVM） 99
3-3-2 長短期記憶神經網路（LSTM） 102
3-3-3 門閥循環單元網路（GRU） 104
3-4 實驗環境 106
3-5 實驗資料 107
第四章實驗結果與分析 109
4-1 實驗結果 109
4-1-1 原始資料於各模型預測結果 109
4-1-2 GAN資料擴增結果 131
4-1-3 資料擴增於各模型預測結果 137
4-2 實際案例比較結果 159
4-2-1 三峽大壩案例比對 159
4-2-2 白家堡滑坡案例比對 167
第五章結論與建議 173
5-1 結論 173
5-2 建議 174
參考文獻 175
評審意見回覆表 179

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指導教授

鐘志忠

審核日期

2022-9-19

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