A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System
Various natural disasters such as floods, fires, earthquakes, etc. have affected human life. Detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to Earth scientist. One of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. Based on latest achievements of Data Mining international institutions such as Rexer-KDnugget-Gartner and also newest authentic articles, SVM, KNN, C4.5, MLP are from most important and popular and leading classifiers in data world. Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ، C4.5 decision tree and KNN near neighbourhood have been utilized in a combined form to detect and categorize micro seismic events. In general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting pre-processing and separating training and testing samples, 4) generating relevant models with training samples and detecting and clustering test samples and 5) extracting a cluster with the maximum candidate using boost learning. After simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. Accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively.
Monadi, A., B. Eslampour, N. Baghaeimehr and F. Abed, Crisis Management in Cities from Holy Quran Perspective, Journal Appl. Environ. Biol. Sci, Vol. 2, No. 12, 2012, pp. 606-608.
Solymani, A., A. Abdolahii and M. Rahimi, Evaluation and Improvement of Urban Worn against Earthquakes (Case Study Neighborhood of Shiraz Zvalanvar), Journal Appl. Environ. Biol. Sci, Vol. 4, No. 1, 2014, pp. 157-165.
Abraham, A., Rule-based expert systems, Handbook of measuring system design, 2005, pp. 909-919.
Hadjimichael, M., A. P. Kuciauskas, P.M. Tag, R.L. Bankert and J.E. Peak, A meteorological fuzzy expert system incorporating subjective user input, Knowl Inf Syst, Vol. 4, No. 3, 2002, pp. 350-369.
Mansiya, K., Z. Alma, M. Torgyn, M. Marzhan and N., Kanat, The methodology of expert systems, International Journal of Computer Science and Network Security, Vol. 14, No. 2, 2014, pp. 62-66.
A. Shahbahrami and Z. Mehdidoust Jalali, Evaluation of Different Data Mining Algorithms to Predict Earthquakes Using Seismic Hazard Data, Journal Appl. Environ. Biol. Sci, Vol. 7, Vol. 2, 2017, pp. 142-150.
Larose, D. T, Discovering knowledge in data: an introduction to data mining, John Wiley & Sons, 2014.
Zaki, Mohammed J., Wagner Meira Jr, and Wagner Meira. Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press, 2014.
Suthaharan, Shan. "Support vector machine." In Machine Learning Models and Algorithms for Big Data Classification, pp. 207-235. Springer US, 2016.
Zhu, Ji, and Trevor Hastie. "Kernel logistic regression and the import vector machine." Journal of Computational and Graphical Statistics 14, no. 1 (2005): 185-205.
Rui, H. O. U., and Bi-xi Zhang. "A Method for Forecasting Regional Logistics Demand Based on MLP Neural Network and Its Application [J]." Systems Engineering-theory & Practice 12 (2005): 006.
J. Ross Quinlan, C4.5: Programs for Machine Learning, Computer Science Artificial Intelligence Machine Learning, 2014, 302.
Liu, Z. G., Pan, Q., & Dezert, J, A new belief-based K-nearest neighbor classification method, Pattern Recognition, Vol. 46, No. 3, 2013, pp. 834-844.
Rie Kamei1, Nori Nakata2, and David Lumley1, Introduction to micro seismic source mechanisms, THE LEADING EDGE August 2 015
Entao Liu, Lijun Zhu, Anupama Govinda Raj, James H. McClellan, Abdullatif Al-Shuhail, SanLinn I. Kaka, Naveed Iqbal Micro seismic events enhancement and detection in sensor arrays using autocorrelation based filtering, arXiv:1612.01884 [physics.geo-ph]
S.Z. Cai, Q.F. Zhang, X.P. Xu, D.H. Hu and Y.M. Qu, The Data Mining Technology of Particle Swarm Optimization Algorithm in Earthquake Prediction, Advanced Materials Research, Vol. 9, 2014, pp. 1570-1573.
T. C. W. LANDGREBE AND R. D. MULLER, Uncovering the relationship between sub ducting bathymetric ridges and volcanic chains with significant earthquakes using geophysical data mining, Australian Journal of Earth Sciences, 2015, pp. 162-171.
Mark Last, Nitzan Rabinowitz, Gideon Leonard, Predicting the Maximum Earthquake Magnitude from Seismic Data in Israel and Its Neighboring Countries, Plos One, 2016.
Anshuman Bhardwaj, Shakti man Singhb, Lydia Samb, Akanksha Bhardwajd, Javier Martín-Torresa, Atar Singh, Rajesh Kumar, MODIS-based estimates of strong snow surface temperature anomaly related to high altitude earthquakes of 2015, Remote Sensing of Environment, Vol 188, 2017, pp. 1–8.
Jari Kortström, MarjaUski, TimoTiira, Automatic classification events within a regional seismograph network of seismic Computers & Geosciences, Volume 87, February 2016, Pages 22-30.
- There are currently no refbacks.
Copyright (c) 2017 Saeed Ghorbani
This work is licensed under a Creative Commons Attribution 4.0 International License.