Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task
Publish Year: 1391
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJE-26-2_006
تاریخ نمایه سازی: 17 خرداد 1393
Abstract:
In this paper, the multivariate adaptive regression splines (MARS) is employed to predict earthquake events based on two common approaches in sequence learning. In the first scenario, the task is definedas a sequence prediction problem, and consequently the MARS model is used as a predictor. In thesecond scenario, the same task is considered as a sequence recognition problem and the model of MARS, this time, is used as a binary classifier with results that could alternatively help to predict an earthquake event. Forecasting results of applying the methods to a cluster of seismic data on pacificring of fire indicate that MARS as a binary classifier outperforms the predictor MARS. In fact, while both approaches are plausible, the best results are achieved when the earthquake prediction problem is considered as a sequence recognition task
Keywords:
Earthquake Prediction , Multivariate Adaptive Regression Splines , (MARS Model) , Sequence Learning , Sequence Recognition , Time Series Analysis
Authors
a bali
International Institute of Earthquake Engineering and Seismology, Tehran-۱۹۵۳۷-۱۴۴۵۳, Iran
m mahdinejad noori
Ministry of Science, Research and Technology, Tehran, Iran