2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Dec. 19, 2016 to Dec. 21, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2016.073
Earthquake prediction has been long considered as impossible phenomenon but recent research studies show some progress in this field by considering it as a data mining problem. There are numerous challenges in earthquake prediction, which includes highly non-linear behavior of seismic activity and non-availability of reliable seismic precursors. This work focuses on earthquake prediction in Hindukush region by employing mathematically computed seismic features and using these features to model earthquake occurrences through employing machine learning techniques. The study aims to consider earthquake prediction as a binary classification problem. The short term earthquake prediction is performed using tree based ensemble classifiers, where rotation forest has shown good prediction results, compared to random forest and rotboost.
Earthquakes, Predictive models, Decision trees, Data mining, Vegetation, Mathematical model, Computational modeling
K. M. Asim, A. Idris, F. Martinez-Alvarez and T. Iqbal, "Short Term Earthquake Prediction in Hindukush Region Using Tree Based Ensemble Learning," 2016 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2016, pp. 365-370.