Skip to main content

Advertisement

SpringerLink
Log in

The deterministic dendritic cell algorithm with Haskell in earthquake magnitude prediction

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

The earthquake magnitude prediction is a task of utmost difficulty that has been addressed by using many different strategies, with no further transformation thus far. This work evaluates the Haskell based deterministic dendritic cell algorithm (hDCA)’s accuracy when used to predict earthquake magnitude in Sichuan and surroundings. First, eight seismicity indicators have been retrieved from the literature and used as input for the algorithms, and they are calculated from the earthquake catalog of the Sichuan and surroundings by well-known geophysical theory, named Gutenberg-Richter inverse power-law, and characteristic earthquake magnitude distribution and also conclusions drawn by recent related studies. Then, the hDCA is used to predict earthquakes with magnitude larger than 4.5 in the next month. In this work, the proposed method has been compared to the well-known machine learning algorithms, such as Dendritic Cell Algorithm (DCA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN), Probabilistic Neural Network (PNN) and Neural Dynamic Classification (NDC). Overall our method yields the promising results in terms of all qualify parameters evaluated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Adeli H, Panakkat A (2009) A probabilistic neural network for earthquake magnitude prediction. Neural Netw 22(7):1018–1024

    Article  Google Scholar 

  • Allen CR (1976) Responsibilities in earthquake prediction: To the Seismological Society of America. Bull Seismol Soc Am 66(6):2069–2074

    Google Scholar 

  • Asencio-Cortés G, Martínez-Álvarez F, Troncoso A (2015) Medium-large earthquake magnitude prediction in Tokyo with artificial neural networks. Neural Computing and Applications 28(5):1043–1055

    Article  Google Scholar 

  • Asenciocortés G, Moralesesteban A, Shang X (2017) Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Computers & Geosciences 115:198–210

    Article  Google Scholar 

  • Asim KM, Martínez-Álvarez F, Basit A et al (2017) Earthquake magnitude prediction in Hindukush region using machine learning techniques. Nat Hazards 85(1):471–486

    Article  Google Scholar 

  • Asim KM, Idris A, Iqbal T et al (2018a) Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification. Soil Dyn Earthq Eng 111:1–7

    Article  Google Scholar 

  • Asim KM, Idris A, Iqbal T et al (2018b) Earthquake prediction model using support vector regressor and hybrid neural networks. PloS one 13(7):1–22

    Article  Google Scholar 

  • Bhandarkar T, Vardaan K, Satish N et al (2019) Earthquake trend prediction using long short-term memory RNN. Int J Electr Comput Eng 9(2):1304

    Google Scholar 

  • Brehm DJ, Braile LW (1998) Intermediate-term earthquake prediction using precursory events in the New Madrid seismic zone. Bull Seismol Soc Am 88(2):564–580

    Google Scholar 

  • Chelly Z, Elouedi Z (2016) A survey of the dendritic cell algorithm. Knowl Inf Syst 48(3):505–535

    Article  Google Scholar 

  • Cheng YT (2009) Earthquake prediction: retrospect and prospect. Sci China (Ser D): Earth Sci 39(12):1633–1658

    Google Scholar 

  • China Earthquake Networks Center (2015) China Earthquake Networks Center. http://news.ceic.ac.cn/index.html?time=1513924739

  • China Earthquake Data Center (2019) China Earthquake Data Center. http://data.earthquake.cn

  • Christensen K, Olami Z (1992) Variation of the Gutenberg-Richter b values and nontrivial temporal correlations in a Spring-Block Model for earthquakes. J Geophys Res Solid Earth (1978-2012) 97(B6):8729–8735

    Article  Google Scholar 

  • Devries PMR, Viégas F, Martin W (2018) Deep learning of aftershock patterns following large earthquakes. Nature 560(7720):632– 634

    Article  Google Scholar 

  • Fernández-Gómez M, Asencio-Cortés G, Troncoso A, Martínez-Álvarez F (2017) Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning. Applied Science

  • Florido E, Aznarte JL, Morales-Esteban A, Martínez-Álvarez F (2016) Earthquake magnitude prediction based on artificial neural networks: a survey. Croat Oper Res Rev 7(2):687–700

    Google Scholar 

  • Geller RJ, Jackson Dd, Kagan YY, Mulargia F (1997) Enhanced: earthquakes cannot be predicted. Science 275(5306):1616–1620

    Article  Google Scholar 

  • Greensmith J (2007) The Dendritic Cell Algorithm. University of Nottingham, Nottingham

  • Greensmith J, Aickelin U (2008) The Deterministic Dendritic Cell Algorithm. International Conference on Artificial Immune Systems

  • Greensmith J, Gale MB (2017) The functional dendritic cell algorithm a formal specification with Haskell. 2017 IEEE Congress on Evolutionary Computation (CEC)

  • Grant RA, Raulin JP, Freund FT (2015) Changes in animal activity prior to a major (m = 7) earthquake in the Peruvian Andes. Phys Chem Earth Parts A/B/C 85:69–77

    Article  Google Scholar 

  • Gutenberg B, Richter CF (1954) Seismicity of the Earth. Princeton University, Princeton

  • Hainzl S, Zller G, Kurths J, Zschau J (2000) Seismic quiescence as an indicator for large earthquakes in a system of self-organized criticality. Geophys Res Lett 27(5):597–600

    Article  Google Scholar 

  • Huang JP, Wang XA, Zhao Y (2018) Large earthquake magnitude prediction in Taiwan based on deep learning neural network. Neural Netw World 28(2):149–160

    Article  Google Scholar 

  • Jaishi HP, Singh S, Tiwari RP (2014) Temporal variation of soil radon and thoron concentrations in Mizoram (India), associated with earthquakes. Nat Hazards 72(2):443–454

    Article  Google Scholar 

  • Kirschvink JL (2000) Earthquake prediction by animals: evolution and sensory perception. Bull Seismol Soc Am 90(2):312–323

    Article  Google Scholar 

  • Madahizadeh R, Allamehzadeh M (2009) Prediction of aftershocks distribution using artificial neural networks and its application on the May 12, 2008 Sichuan Earthquake. Ocean Dyn 11(3):111–120

    Google Scholar 

  • Martínez-Álvarez F, Troncoso A, Morales-Esteban A, Riquelme JC (2011) Computational intelligence techniques for predicting earthquakes. Lecture Notes in Artificial Intelligence. Expert Syst Appl 6679(2):287–294

    Google Scholar 

  • McGuire JJ, Boettcher MS, Jordan TH (2005) Foreshock sequences and short-term earthquake predictability on East Pacific Rise transform faults. Nature 434(7032):457–461

    Article  Google Scholar 

  • Morales-Esteban A, Martínez-Álvarez F, Troncoso A, de Justo JL, Rubio-Escudero C (2010) Pattern recognition to forecast seismic time series. Expert Syst Appl 37(12):8333–8342

    Article  Google Scholar 

  • Morales-Esteban A, Martínez-Álvarez F, Reyes J (2013) Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence. Tectonophysics 593:121–134

    Article  Google Scholar 

  • Nature (1999) Nature, https://www.nature.com/. Nature

  • Nuannin P, Kulhanek O, Persson L (2005) Spatial and temporal b value anomalies preceding the devastating off coast of nw sumatra earthquake of december 26, 2004. Geophys Res Lett:32

  • Panakkat A, Adeli H (2007) Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int J Neural Syst 17(1):13–33

    Article  Google Scholar 

  • Panakkat A, Adeli H (2008) Recent efforts in earthquake prediction. Nat Hazards Rev 9(2):70–80

    Article  Google Scholar 

  • Panakkat A, Adeli H (2009) Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Comput-Aided Civ Infrastruct Eng 24(4):280–292

    Article  Google Scholar 

  • Petersen MD, Cao T, Campbell KW, Frankel AD (2007) Time-independent and time-dependent seismic hazard assessment for the State of California: uniform California Earthquake Rupture Forecast Model 1.0. Seismol Res Lett 78(1):99–109

    Article  Google Scholar 

  • Rafiei MH, Adeli H (2017) A new neural dynamic classification algorithm. IEEE Trans Neural Netw Learn Syst 28(12):3074–3083

    Article  Google Scholar 

  • Reyes J, Morales-Esteban A, Martínez-Álvarez F (2013) Neural networks to predict earthquakes in Chile. Appl Soft Comput 13(2):1314–1328

    Article  Google Scholar 

  • Shodiq MN, Kusuma DH, Rifqi MG et al (2019) Adaptive neural fuzzy inference system and automatic clustering for earthquake prediction in indonesia. JOIV: Int J Inf Vis 3(1):47–53

    Article  Google Scholar 

  • Strikwerda C (2008) The danger theory and its application to artificial immune systems. University of Kent at Canterbury, pp 141– 148

  • Wang T, Wang XA (2014) A sensor of ground temperature and its application in big earthquake monitoring. South China J Seismol 34(01):33–37

    Google Scholar 

  • Yip CF, Ng WL, Yau CY (2018) A hidden Markov model for earthquake prediction. Stoch Env Res Risk A 32(5):1415– 1434

    Article  Google Scholar 

  • Zhou W, Liang YW (2017) A Numerical Differentiation based Dendritic Cell Model. International Conference on Tools with Artificial Intelligence (ICTAI 2017)

Download references

Acknowledgments

The authors want to thank NSFC- http://www.nsfc.gov.cn/ for the support through Grants Number 61877045, and Military Commission Innovation Special Zone for the support through Grants Number 17-H863-01-ZT-002-016-02, and Fundamental Research Project of Shenzhen Science and Technology Program for the support through Grants Number JCYJ20160428153956266.

Author information

Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Wen Zhou & Yiwen Liang

  2. School of Cyber Science and Engineering, Wuhan University, Wuhan, China

    Hongbin Dong

Authors
  1. Wen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongbin Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yiwen Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wen Zhou.

Additional information

Communicated by: H. Babaie

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, W., Dong, H. & Liang, Y. The deterministic dendritic cell algorithm with Haskell in earthquake magnitude prediction. Earth Sci Inform 13, 447–457 (2020). https://doi.org/10.1007/s12145-020-00442-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-020-00442-z

Keywords

Search

Navigation