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Ground Motion Prediction Model Using Adaptive Neuro-Fuzzy Inference Systems: An Example Based on the NGA-West 2 Data

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Abstract

Adaptive neuro-fuzzy inference systems (ANFIS) are used here to obtain the robust ground motion prediction model (GMPM). Avoiding a priori functional form, ANFIS provides fully data-driven predictive models. A large subset of the NGA-West2 database is used, including 2335 records from 580 sites and 137 earthquakes. Only shallow earthquakes and recordings corresponding to stations with measured V s30 properties are selected. Three basics input parameters are chosen: the moment magnitude (Mw), the Joyner–Boore distance (R JB) and V s30. ANFIS model output is the peak ground acceleration (PGA), peak ground velocity (PGV) and 5% damped pseudo-spectral acceleration (PSA) at periods from 0.01 to 4 s. A procedure similar to the random-effects approach is developed to provide between- and within-event standard deviations. The total standard deviation (SD) varies between [0.303 and 0.360] (log10 units) depending on the period. The ground motion predictions resulting from such simple three explanatory variables ANFIS models are shown to be comparable to the most recent NGA results (e.g., Boore et al., in Earthquake Spectra 30:1057–1085, 2014; Derras et al., in Earthquake Spectra 32:2027–2056, 2016). The main advantage of ANFIS compared to artificial neuronal network (ANN) is its simple and one-off topology: five layers. Our results exhibit a number of physically sound features: magnitude scaling of the distance dependency, near-fault saturation distance increasing with magnitude and amplification on soft soils. The ability to implement ANFIS model using an analytic equation and Excel is demonstrated.

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Acknowledgments

The authors would like to thank the participants of the NGA-West2 programs for providing high-quality data and stimulating ideas. We also thank an anonymous reviewer for their constructive criticism and comments that helped us to improve this study.

Data and Resources

The datasets used in this article have been collected and disseminated by The Pacific Earthquake Engineering Research Center.

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Authors and Affiliations

  1. Risk Assessment and Management laboratory (RISAM), University of Tlemcen, Tlemcen, Algeria

    Mourad Ameur, Boumédiène Derras & Djawed Zendagui

  2. Department of Civil Engineering and Hydraulics, University of Saïda, Saïda, Algeria

    Boumédiène Derras

  3. Institut des Sciences de la Terre (ISTerre), University of Grenoble-Alpes, Grenoble, France

    Boumédiène Derras

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  1. Mourad Ameur
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  2. Boumédiène Derras
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  3. Djawed Zendagui
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Correspondence to Boumédiène Derras.

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Ameur, M., Derras, B. & Zendagui, D. Ground Motion Prediction Model Using Adaptive Neuro-Fuzzy Inference Systems: An Example Based on the NGA-West 2 Data. Pure Appl. Geophys. 175, 1019–1034 (2018). https://doi.org/10.1007/s00024-017-1743-3

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