Earthquake magnitude prediction in Hindukush region using machine learning techniques

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• 作者：K. M. Asim ; F. Martínez-Álvarez ; A. Basit ; T. Iqbal
• 关键词：Earthquake prediction ; Artificial neural networks ; Time series ; Machine learning
• 刊名：Natural Hazards
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：85
• 期：1
• 页码：471-486
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Natural Hazards; Hydrogeology; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Civil Engineering; Environmental Management;
• 出版者：Springer Netherlands
• ISSN：1573-0840
• 卷排序：85

文摘

Earthquake magnitude prediction for Hindukush region has been carried out in this research using the temporal sequence of historic seismic activities in combination with the machine learning classifiers. Prediction has been made on the basis of mathematically calculated eight seismic indicators using the earthquake catalog of the region. These parameters are based on the well-known geophysical facts of Gutenberg–Richter’s inverse law, distribution of characteristic earthquake magnitudes and seismic quiescence. In this research, four machine learning techniques including pattern recognition neural network, recurrent neural network, random forest and linear programming boost ensemble classifier are separately applied to model relationships between calculated seismic parameters and future earthquake occurrences. The problem is formulated as a binary classification task and predictions are made for earthquakes of magnitude greater than or equal to 5.5 (\(M \ge\) 5.5), for the duration of 1 month. Furthermore, the analysis of earthquake prediction results is carried out for every machine learning classifier in terms of sensitivity, specificity, true and false predictive values. Accuracy is another performance measure considered for analyzing the results. Earthquake magnitude prediction for the Hindukush using these aforementioned techniques show significant and encouraging results, thus constituting a step forward toward the final robust prediction mechanism which is not available so far.