以隨機森林方法預測地震震度;Earthquake Intensity Prediction Using the Random Forest Method

NCUIR > Engineering College > Graduate Institute of Mechanical Engineering > Electronic Thesis & Dissertation > Item 987654321/95985

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/95985

Title:	以隨機森林方法預測地震震度;Earthquake Intensity Prediction Using the Random Forest Method
Authors:	李驊原;LI, HUA-YUAN
Contributors:	機械工程學系
Keywords:	地震震度預測;機器學習;隨機森林;地震P波擷取;earthquake magnitude prediction;machine learning;random forest;earthquake P-wave extraction
Date:	2024-08-16
Issue Date:	2024-10-09 17:28:07 (UTC+8)
Publisher:	國立中央大學
Abstract:	地震是地球上主要的自然災害之一，當強烈地震發生時，對地震的認知不足及疏於防範，往往會造成嚴重的財產損失及人員傷亡，而臺灣位於環太平洋地震帶上，地震活動頻繁，如何預防地震已經成為不可忽視的重要課題。本研究中嘗試對地震預警系統進行研究。研究方法為由人工智慧中機器學習領域的理論出發，使用向中央氣象局申購取得及公開下載之桃園(局部)地區地震資料，分別以隨機森林回歸與分類模型，利用取得之歷史性地震資料進行訓練及分析，擷取地震初始之加速度訊號，以對當次地震最大地動加速度(PGA)及震級進行預測，並探討不同地震地震波(P波)特徵組合及加入虛擬數據對預測結果的影響。最終目標為迅速估算當次地震震度後，系統可以決定使否需要發出地震預警警報，使收到警報的居民可以迅速逃生。關鍵字：地震震度預測、機器學習、隨機森林、地震P波擷取 ;Earthquakes are one of the major natural disasters on earth. When strong earthquakes occur, insufficient awareness of earthquakes and neglect of precautions often result in serious property losses and casualties. Taiwan is located in the Pacific Rim Seismic Belt, and seismic activity is Frequently, how to prevent earthquakes has become an important issue that cannot be ignored. This study attempts to build an earthquake early warning system. using seismic acceleration data in the Taoyuan (local) area obtained from the Central Meteorological Administration and publicly downloaded based on the theory of machine learning in artificial intelligence. The specific methods used in this thesis are random forest regression and classification models. These models capture the initial acceleration signals of the earthquake to predict the maximum ground acceleration (PGA) and magnitude of the current earthquake. This study also explores the influence of choosing different combinations of earthquake seismic wave (P wave) characteristics and adding virtual data for predicting the results.
Appears in Collections:	[Graduate Institute of Mechanical Engineering] Electronic Thesis & Dissertation

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