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研究生:許博程
研究生(外文):Po-Cheng Hsu
論文名稱:基於深度學習之地震以及海平面溫度預測模型
論文名稱(外文):Deep Learning-Based Models for Earthquake and Sea Surface Temperature Prediction
指導教授:王家慶
指導教授(外文):Jia-Ching Wang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:42
中文關鍵詞:地震海平面溫度深度學習
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地震以及海平面溫度的預測對於地球科學和氣象研究至關重要。本研究提出了兩個獨立的預測模型,分別針對地震電離層前兆和海平面溫度,其中地震電離層前兆預測模型結合了深度神經網路(DNN)以及長短期記憶(LSTM)網路的比較,而海平面溫度預測則僅使用了LSTM網路。
在地震電離層前兆預測方面,本論文研究了深度神經網路(DNN)和LSTM網路的效能。透過比較這兩種模型的結果,能夠深入了解它們在捕捉地震電離層前兆模式和趨勢方面的優勢。實驗結果顯示,LSTM網路在地震電離層前兆預測中表現出色,相對於DNN模型有更好的泛化能力,特別是對於時間序列數據的建模。
在海平面溫度預測方面,本論文專注於LSTM網路的應用。這種網路的適應性和長期記憶特性使其成為捕捉溫度變化的理想工具。本文通過大量實驗證明,LSTM模型能夠有效地捕捉海平面溫度的季節性和趨勢,並在預測中表現出色。
總體而言,本研究提供了一個綜合性的地震電離層前兆和海平面溫度預測模型,結合了LSTM網路的優勢。
The prediction of earthquakes and sea surface temperatures is crucial for Earth science and meteorological research. This study introduces two independent predictive models, focusing on earthquake and sea surface temperature forecasts. The earthquake prediction model integrates a comparison between Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) network results, while the sea surface temperature prediction model exclusively utilizes the LSTM network.
In terms of earthquake prediction, we investigate the performance of Deep Neural Network (DNN) and LSTM network. By comparing the results of these two models, we gain insights into their advantages in capturing earthquake patterns and trends. Experimental results demonstrate that the LSTM network excels in earthquake prediction, exhibiting better generalization capabilities compared to the DNN model, particularly in modeling time-series data.
For sea surface temperature prediction, we focus on the application of the LSTM network. The adaptability and long-term memory characteristics of this network make it an ideal tool for capturing temperature variations. Through extensive experiments, we validate that the LSTM model effectively captures the seasonality and trends in sea surface temperatures, demonstrating outstanding performance in prediction.
In summary, this study provides a comprehensive earthquake and sea surface temperature prediction model, leveraging the advantages of the LSTM network.
中文摘要……………………………………………………………………I
ABSTRACT…………………………………………………………………II
致謝…………………………………………………………………………IV
章節目錄……………………………………………………………………V
圖目錄………………………………………………………………………VIII
表目錄………………………………………………………………………IX
第一章 緒論………………………………………………………………1
1-1 背景……………………………………………………………1
1-2 研究動機與目的………………………………………………2
1-3 研究方法與章節介紹…………………………………………3
第二章 相關文獻探討……………………………………………………4
2-1 深度學習………………………………………………………4
2-1-1 類神經網路發展及概念……………………………6
2-1-2 感知機………………………………………………6
2-2 深度神經網路(DNN) …………………………………………8
2-3 長短期記憶(LSTM)…………………………………………10
2-4 高斯噪聲………………………………………………………11
第三章 研究內容與方法…………………………………………………13
3-1 地震預測模型…………………………………………………13
3-1-1 模型架構介紹………………………………………13
3-1-2 資料前處理…………………………………………14
3-1-3 模型訓練策略………………………………………14
3-2 海平面溫度預測模型…………………………………………15
3-2-1 模型架構介紹………………………………………15
3-2-2 資料前處理…………………………………………15
3-2-3 模型訓練策略………………………………………16
第四章 實驗結果與討論…………………………………………………17
4-1 實驗設備………………………………………………………17
4-2 資料集介紹……………………………………………………17
4-2-1地震資料集……………………………………………17
4-2-2海平面溫度資料集……………………………………19
4-3 實驗結果與討論………………………………………………19
4-3-1地震預測模型…………………………………………19
4-3-2海平面溫度預測模型…………………………………23
第五章 結論及未來研究方向……………………………………………25
5.1 結論……………………………………………………………25
5-2 未來研究方向…………………………………………………25
第六章 參考文獻…………………………………………………………27
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[2]Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. "ImageNet Classification with Deep Convolutional Neural Networks." Neural Information Processing Systems (NIPS), 2012, vol. 25, pp. 1097-1105.
[3]Sepp Hochreiter, Jürgen Schmidhuber. "Long Short-Term Memory." Neural Computation, vol. 9, pp. 1735–1780, 1997.
[4]Pedro Domingos. "A Few Useful Things to Know About Machine Learning." Communications of the ACM, vol. 55, pp. 78-87, 2012.
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[6]Kaiming He, et al. "Deep Residual Learning for Image Recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.
[7]Frank Rosenblatt. "Perceptron: An Introduction to Computational Geometry." Psychological Review, vol. 65, pp. 386-408, 1958.
[8]Matthew D. Zeiler, Rob Fergus. "Visualizing and Understanding Convolutional Networks." European Conference on Computer Vision (ECCV), pp. 818-833, 2014.
[9]Kaiming He, et al. "Deep Residual Learning for Image Recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.
[10]Ilya Sutskever, Oriol Vinyals, Quoc V. Le. "Sequence to Sequence Learning with Neural Networks." Advances in Neural Information Processing Systems (NIPS), 2014, pp. 3104–3112.
[11]Gaussian noise in deep learning: why and how to use it:https://www.51cto.com/article/745091.html
[12]Ian Goodfellow et al. "Generative Adversarial Nets." Advances in Neural Information Processing Systems (NIPS), vol. 27, pp. 2672-2680, 2014.
[13]Tero Karras et al. "Progressive Growing of GANs for Improved Quality, Stability, and Variation." arXiv preprint arXiv:1710.10196, 2018.
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