1
Civil & Environmental Engineering, International Institute of Earthquake Engineering
2
Civil & Environmental Engineering, International
Abstract
In this paper, we have tried to predict earthquake events in a cluster of seismic data on pacific ring of fire, using multivariate adaptive regression splines (MARS). The model is employed as either a predictor for a sequence prediction task, or a binary classifier for a sequence recognition problem, which could alternatively help to predict an event. Here, we explain that sequence prediction/recognition, as two aspects of sequence learning, are not the same in general. We show that while both these approaches are plausible for earthquake prediction, the forecasting results indicate that MARS as a binary classifier outperforms the predictor MARS. The results clearly show how it is important to challenge a single earthquake forecasting problem from an appropriate point of view.
mahdinejad noori, M. M., & bali, A. (2013). Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task. International Journal of Engineering, 26(2), 137-142.
MLA
mohammad mahdi mahdinejad noori; aref bali. "Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task". International Journal of Engineering, 26, 2, 2013, 137-142.
HARVARD
mahdinejad noori, M. M., bali, A. (2013). 'Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task', International Journal of Engineering, 26(2), pp. 137-142.
VANCOUVER
mahdinejad noori, M. M., bali, A. Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task. International Journal of Engineering, 2013; 26(2): 137-142.
)
