Earthquake Prediction in California Using Feature Selection Techniques

  1. Roiz-Pagador, Joaquin
  2. Chacon-Maldonado, Andres 1
  3. Ruiz, Roberto 1
  4. Asencio-Cortes, Gualberto 1
  1. 1 Universidad Pablo de Olavide
    info

    Universidad Pablo de Olavide

    Sevilla, España

    ROR https://ror.org/02z749649

Actas:
16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) Advances in Intelligent Systems and Computing

Editorial: Springer

ISSN: 2194-5357,  2194-5365

ISBN: 9783030878689,  9783030878696

Año de publicación: 2021

Páginas: 728-738

Tipo: Aportación congreso

DOI: 10.1007/978-3-030-87869-6_69 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Predicting the magnitude of earthquakes is of vital importance and, at the same time, of extreme complexity, where each attribute contributes differently in the process, even introducing noise. Preprocessing using attribute selection techniques helps to alleviate this drawback. In this work, this is demonstrated through an extensive comparison of 47 years of data from the Northern California Earthquake Data Center, where a wide range of feature selection algorithms are applied composed by different search, like population, local and ranking search based; and evaluators, like Correlations, consistency and distance metrics. After that, prediction algorithms will allow to compare the result with and without the application of feature selection, showing that the number of existing attributes can be reduced by 80%, improving metrics of the original, ensuring that the use of attribute selection in this type of problem is quite promising.

Referencias bibliográficas

  • Asencio-Cortés, G., Martínez-Álvarez, F., Morales-Esteban, A., Reyes, J.: A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction. Knowl. Based Syst. 101, 15–30 (2016)
  • Dwi Prayogo, R., Ikhsan, N.: Attribute selection effect on tree-based classifiers for letter recognition. In: 2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA), pp. 13–18 (2020)
  • Sugianela, Y., Ahmad, T.: Pearson correlation attribute evaluation-based feature selection for intrusion detection system. In: 2020 International Conference on Smart Technology and Applications (ICoSTA), pp. 1–5 (2020)
  • Han, W., Gan, Y., Chen, S., Wang, X.: Study on earthquake prediction model based on traffic disaster data. In: 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS), pp. 331–334 (2020)
  • Banna, M.H.A., et al.: Attention-based bi-directional long-short term memory network for earthquake prediction. IEEE Access 9, 56589–56603 (2021)
  • Hashimoto, T., Shepard, D., Kuboyama, T., Shin, K.: Event detection from millions of tweets related to the great east Japan earthquake using feature selection technique. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 7–12 (2015)
  • Hall, M.: Correlation-based feature selection for machine learning, vol. 19. Department of Computer Science (2000)
  • Urbanowicz, R.J., Meeker, M., La Cava, W., Olson, R.S., Moore, J.H.: Relief-based feature selection: introduction and review. J. Biomed. Inform. 85, 189–203 (2018)
  • Liu, H., Setiono, R.: A Probabilistic Approach to Feature Selection - A Filter Solution, pp. 319–327. Morgan Kaufmann (1996)
  • Saikhu, A., Arifin, A.Z., Fatichah, C.: Correlation and symmetrical uncertainty-based feature selection for multivariate time series classification. Int. J. Intell. Eng. Syst. 12, 129–137 (2019)
  • Dorigo, M., Birattari, M., Stützle, T.: Ant colony optimization. Comput. Intell. Mag. 1, 28–39 (2006)
  • Ma, X.X., Wang, J.S.: Optimized parameter settings of binary bat algorithm for solving function optimization problems. J. Electr. Comput. Eng. 2018, 3847951 (2018)
  • Teodorović, D.: Bee colony optimization (BCO). In: Lim C.P., Jain L.C., Dehuri S. (eds.) Innovations in Swarm Intelligence. Studies in Computational Intelligence, vol. 248. Springer, Heidelberg. https://doi.org/10.1007/978-3-642-04225-6_3
  • Farreny, H., Prade, H.: Heuristics—intelligent search strategies for computer problem solving, by Judea Pearl. (Reading, MA: Addison-Wesley, 1984). Int. J. Intell. Syst. 1, 48 (1986)
  • Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, New York (1996)
  • Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the International Conference on Neural Networks, ICNN 1995, vol. 4, pp. 1942–1948 (1995)
  • Martí, R., Corberán, A., Peiró, J.: Scatter Search, pp. 1–24. Springer, Cham (2016). https://doi.org/10.1007/978-1-4615-0337-8
  • Glover, F.: Tabu search—part i. ORSA J. Comput. 1(3), 190–206 (1989)
  • Mackay, D.J.: Introduction to Gaussian Processes (1998)
  • Aha, D., Kibler, D.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)
  • Quinlan, R.J.: Learning with continuous classes. In: 5th Australian Joint Conference on Artificial Intelligence, pp. 343–348. World Scientific, Singapore (1992)
  • Shevade, S., Keerthi, S., Bhattacharyya, C., Murthy, K.: Improvements to the SMO algorithm for SVM regression. IEEE Trans. Neural Netw. 11, 1188–1193 (1999)
  • Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
  • John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Mateo (1995)
  • Asencio-Cortés, G., Morales-Esteban, A., Shang, X., Martínez-Álvarez, F.: Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Comput. Geosci. 115, 198–210 (2018)
  • Knuth, D.: ANSS composite earthquake catalog through the Northern California earthquake data center (NCEDC). UC Berkeley Seismological Laboratory (2014). Accessed 15 April 2017
  • Frank, E., et al.: Weka-a machine learning workbench for data mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 1269-1277. Springer, Boston, MA (2009). https://doi.org/10.1007/978-0-387-09823-4_66