使用深度學習及隨機森林預測地震之分析

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姓名

羅正勛(Cheng-Hsun LO) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

使用深度學習及隨機森林預測地震之分析
(Earthquake Prediction Using Deep Learning and Random Forest)

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摘要(中)

在無數的自然災害中，地震能夠在轉瞬間給予人類文明造成巨大的破壞，對地震的認知不足及疏於防範將導致災難及無法抹滅的痛苦和陰影，因此，地震預測在研究主題中一直是重要焦點。而台灣位於環太平洋地震帶上，地震活動頻繁，如何預防地震成為重要的課題。本研究中嘗試對地震預測領域進行概述，並由人工智慧中機器學習領域的理論出發，以人工神經網路的深度學習模型及隨機森林模型，對中央氣象局提供桃園地區的歷史性地震資料進行訓練、預測及分析。本研究包含中央氣象局公開下載之災害性地震資料及購買之資料兩組預測組別，並且後者包含前者，分別以兩種模型預測加速度及震度；此外，本研究也探討將地震資料取對數或平方對預測結果的影響。

摘要(英)

Among countless natural disasters, earthquakes can cause huge damage to human civilization in an instant. Insufficient understanding of earthquakes and neglect of prevention will lead to disasters and indelible pain and shadows. Therefore, earthquake prediction has always been an important research topic. Taiwan is located in the Pacific Rim seismic belt, with frequent seismic activity. Prevention of earthquakes disasters has become an important issue. This study will give an overview of the field of earthquake prediction, using machine learning methods. Here, the deep learning model of artificial neural network and random forest model were used to analyzes the historical earthquake data in Taoyuan area provided by Central Weather Bureau. This study considered two prediction data groups, the disastrous earthquake data publicly downloaded by the Central Weather Bureau and the purchased data, the latter including the former, and two models are used to predict acceleration and intensity. In addition, this study also explores the effect of taking the logarithm or square of the earthquake data on the prediction results.

關鍵字(中)

★ 地震預測
★ 機器學習
★ 深度學習
★ 隨機森林

關鍵字(英)

★ Earthquake prediction
★ Machine learning
★ Deep learning
★ Random forest

論文目次

摘要 i
Abstract ii
誌謝 iii
圖目錄 vii
表目錄 x
第1章. 緒論 1
1.1 研究背景與目的 1
1.2 文獻回顧 1
1.2.1 理查·艾倫和金森博雄 (2003) 1
1.2.2 日本緊急地震速報系統 (2004) 1
1.2.3 國家地震工程研究中心 (2010) 2
1.2.4 台灣科技大學建築科技中心 (2013、2020) 2
第2章. 地震預測簡介 4
2.1 基本地震現象介紹 4
2.1.1 震度 5
2.2 中央氣象局的預測系統 8
第3章. 基本理論 9
3.1 機器學習簡介 9
3.1.1 數據或資料 9
3.1.2 模型 10
3.1.3 訓練 10
3.1.4 監督學習 10
3.1.5 無監督學習 10
3.1.6 半監督學習 11
3.1.7 強化學習 11
3.1.8 線上學習 11
3.1.9 機器學習流程小結 11
3.2 人工神經網路 12
3.2.1 神經網路的監督學習 12
3.2.2 人工神經元 13
3.2.3 結構 13
3.2.4 超參數 15
3.2.5 反向傳播 15
3.2.6 深度神經網路 15
3.2.7 過擬合 16
3.2.8 深度學習中的不確定性 17
3.2.9 人工神經網路小結 18
3.3 決策樹與隨機森林 19
3.3.1 決策樹 19
3.3.2 隨機森林 21
3.4 皮爾森積動差相關係數 22
第4章. 研究方法 23
4.1 中央氣象局資料選用 23
4.1.1 公開下載之災害性地震資料 23
4.1.2 購買之資料 23
4.1.3 資料結構 24
4.2 軟體選用 26
4.3 特徵提取、選用及資料彙整 27
4.3.1 特徵及其代號以及計算公式 28
4.3.2 皮爾森積動差相關係數分析 31
4.4 輸入(特徵)及輸出之組合 32
4.4.1 輸入之選用 32
4.4.2 輸入之組合 32
4.4.3 輸出之選用 32
4.5 模型選用 32
4.5.1 深度學習 33
4.5.2 隨機森林 35
4.6 平均絕對百分誤差MAPE 36
4.7 決定係數 R2 36
第5章. 數值結果與討論 38
5.1 公開下載之災害性地震資料(A組) 38
5.1.1 深度學習模型預測加速度之結果 38
5.1.2 不同深度學習模型參數對預測結果之影響 42
5.2 中央氣象局購買之資料(B組) 46
5.2.1 深度學習模型預測加速度之結果 46
5.2.2 深度學習模型預測震度之結果 58
5.2.3 隨機森林回歸模型之預測結果 64
5.2.4 隨機森林分類模型之預測結果 74
第6章. 結論與未來展望 77
6.1 結論 77
6.2 未來展望 77
參考文獻 78

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指導教授

黃以玫(Yi-Mei Huang)

審核日期

2022-8-31

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