基於深度學習之地震以及海平面溫度預測模型;Deep Learning-Based Models for Earthquake and Sea Surface Temperature Prediction

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93566

Title:	基於深度學習之地震以及海平面溫度預測模型;Deep Learning-Based Models for Earthquake and Sea Surface Temperature Prediction
Authors:	許博程;Hsu, Po-Cheng
Contributors:	資訊工程學系
Keywords:	地震;海平面溫度;深度學習
Date:	2024-01-29
Issue Date:	2024-09-19 17:19:41 (UTC+8)
Publisher:	國立中央大學
Abstract:	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.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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