應用長短期記憶遞迴神經網路預測Kp地磁指數

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姓名

邱思齊(Shih-Chi Chiu) 查詢紙本館藏

畢業系所

太空科學與工程研究所

論文名稱

應用長短期記憶遞迴神經網路預測Kp地磁指數
(Forecasting of Kp Index Using Long Short-Term Memory Recurrent Neural Networks)

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摘要(中)

Kp指數是用於檢測地磁擾動水平的最重要指標之一，地磁擾動受驅動於太陽風與磁層之間的交互作用。不同情況的太陽風與磁層狀態的耦合使得Kp隱含著多種未知的時變模式。過去的深度學習模型幾乎都使用標準前饋式神經網路預測Kp，然而由於磁暴期間(Kp ≥ 5) 的參數變化劇烈，並且資料相對較少，前饋式神經網路預測磁暴期間Kp的準確度會被局限。本研究使用長短期記憶來預測Kp，長短期記憶是遞迴神經網路的一種變形，能夠儲存隱含在不同輸入特徵之中長期與目標的時間序列相依性。在本研究中，我們設定模型訓練時每次通過當前時步的神經元都包含過去二十四小時的輸入特徵資訊，成功將預測一小時後Kp的準確度比過去研究增加六個百分比。為了滿足不同的需求與條件，本研究也發展了不使用歷史Kp作為輸入參數的模型，並且使用磁層頂日下點對峙距離與磁尾張開程度的參數取代Kp作為模型輸入。結果顯示輸入磁層頂位置參數能有效地降低使用歷史Kp作為模型輸入的依賴性，也能稍微提升磁暴期間的Kp預測準確度。由於磁暴期間較大的地磁擾動會影響地球上的通信設備和電網系統，因此提高Kp的預測準確度有助於太空天氣預報和預警系統的發展，且能有效降低突如其來的極端太空天氣對人類生活的影響。

摘要(英)

The Kp index is one of the most important indicators used to monitor the level of the geomagnetic disturbances driven by interactions between the solar wind and the magnetosphere. Different coupling modes of the solar wind-magnetosphere are hidden in time-varying Kp, which makes Kp hard to be forecasted. Most of the previous studies used the general feedforward neural networks (FNN) to predict Kp. However, the accuracy of the FNN is limited to the highly variable and sparse nature in parameters during magnetic storms (Kp ≥ 5). This study uses the long short-term memory (LSTM) to predict the Kp. The LSTM is a variant of the recurrent neural networks (RNN) that can store long-term time series dependencies hidden in different input features. We passed the input feature information for the past twenty-four hours to all neurons during each epoch of model training, successfully increasing the accuracy of predicting Kp for one-hour ahead by 6%. This study also developed a model that did not use historical Kp as an input parameter. We used the parameters of the subsolar distance of the magnetopause and the level of its tail flaring to replace Kp as the model input. Our results show that inputting the magnetopause position parameters can reduce the dependence of using historical Kp as the model input, and the accuracy of a Kp prediction during magnetic storms is slightly increased. Since the large geomagnetic disturbance will affect the communication equipment and power grid systems on the earth, improving the accuracy of the Kp prediction will help develop space weather forecasting and warning systems, which can effectively reduce the impact of extreme space weather on human life.

關鍵字(中)

★ Kp指數
★ 太陽風電漿
★ 行星際磁場
★ 長短期記憶
★ 磁層頂位置

關鍵字(英)

★ Kp Index
★ Solar Wind Plasma
★ Interplanetary Magnetic Field
★ Long Short-Term Memory
★ Magnetopause Position

論文目次

摘要....................i
Abstract....................ii
誌謝....................iii
目錄....................v
圖目錄....................vii
表目錄....................ix
符號說明....................x
第一章緒論....................1
1-1 研究背景與動機 - 太空天氣與Kp地磁指數....................1
1-2 研究文獻回顧 - Kp預測模型發展歷程....................8
1-3 研究目的與流程....................12
第二章數據分析及預處理....................14
2-1 數據取得 – OMNI資料庫....................14
2-1-1 原始數據集 – 模型輸入選擇....................16
2-2 數據預處理....................19
2-2-1 數據填補 – 缺失值處理....................19
2-2-2 數據觀察 – 機率密度分布....................22
2-2-3 離群值定義 – 四分位距與盒形圖....................25
2-2-4 特徵縮放 (Feature Scaling) – 離群值處理....................29
第三章模型訓練與評估....................35
3-1 人工神經網路 (Artificial Neural Network)....................35
3-1-1 神經網路原理....................35
3-1-2 前饋式神經網路預測Kp的缺陷....................36
3-2 遞迴神經網路 (Recurrent Neural Network)....................37
3-2-1 RNN網路構造與網路層....................38
3-2-2 標準RNN用於預測Kp的局限性....................39
3-3 長短期記憶單元 (Long Short-Term Memory)....................41
3-3-1 遺忘閥 (Forget Gate)....................44
3-3-2 輸入閥 (Input Gate)....................45
3-3-3 LSTM記憶單元狀態更新....................46
3-3-4 輸出閥 (Output Gate)....................47
3-4 訓練、驗證與測試集劃分....................47
3-5 不同LSTM模型架構下的Kp預測能力....................49
第四章 Kp預測結果與分析....................53
4-1 磁層頂公式....................53
4-2 模型輸入包含Kp (一小時模型)....................56
4-2-1 模型I (輸入Kp、Tp、Np、Vsw、Pd、Bx、By與Bz)....................56
4-2-2 模型II (輸入Kp、Tp、Np、Vsw、Bx、By、r_0與α)....................61
4-3 模型輸入不包含Kp (一小時模型)....................63
4-3-1 模型III (輸入Tp、Np、Vsw、Pd、Bx、By與Bz)....................63
4-3-2 模型IV (輸入Tp、Np、Vsw、Bx、By、r_0與α)....................65
4-4 模型V – 三小時模型....................67
第五章討論與結論....................70
5-1 討論....................70
5-2 結論....................70
參考文獻....................73

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指導教授

許志浤(Jih-Hong Shue)

審核日期

2022-8-15

推文