文章基本信息

标题：Earthquake Prediction using Hybrid Machine Learning Techniques
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作者：Mustafa Abdul Salam ; Lobna Ibrahim ; Diaa Salama Abdelminaam 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：5
页码：654
DOI：10.14569/IJACSA.2021.0120578
出版社：Science and Information Society (SAI)
摘要：This research proposes two earthquake prediction models using seismic indicators and hybrid machine learning techniques in the region of southern California. Seven seismic indicators were mathematically and statistically calculated depending on pervious recorded seismic events in the earthquake catalogue of that region. These indicators are namely, time taken during the occurrence of n seismic events (T), average magnitude of n events (M_mean), magnitude deficit that is the difference between the observed magnitude and expected one (ΔM), the curve slope for n events using inverse power law of Gutenberg Richter (b), mean square deviation for n events using inverse power law of Gutenberg Richter (η), the square root of the released energy during T time (DE1/2) and average time between events (µ). Two hybrid machine learning models are proposed to predict the earthquake magnitude during fifteen days. The first model is FPA-ELM, which is a hybrid of the flower pollination algorithm (FPA) and the extreme learning machine (ELM). The second is FPA-LS-SVM, which is a hybrid of FPA and the least square support vector machine (LS-SVM). These two models' performance is compared and assessed using four assessment criteria: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Percent Mean Relative Error (PMRE). The simulation results showed that the FPA-LS-SVM model outperformed the FPA-ELM, LS-SVM, and ELM models in terms of prediction accuracy.
关键词：Extreme learning machine; least square support vector machine; flower pollination algorithm; earthquake prediction