Brown, S. J., Caesar, J., and Ferro, C. A. T.: Global changes in extreme
daily temperature since 1950, J. Geophys. Res., 113, D05115,
https://doi.org/10.1029/2006JD008091, 2008.
Chen, L.: A modified Levenberg–Marquardt method with line search for
nonlinear equations, Comput. Optim. Appl., 65, 753–779, 2016.
Descartes, R.: Discours de la methode: pour bien conduire sa raison, &
chercher la verité dans les sciences: Plus La dioptrique, et Les
meteores. Qui sont des essais de cette methode, A Paris: Chez Theodore Girard
Salle du Palais, 414 pp., 1667.
Eslamian, S.: Handbook of Engineering Hydrology: Modeling, Climate Change,
and Variability, Handbook of Engineering Hydrology, Vol.2, CRC Press, 646
pp., 2014.
Ferrier, D.: The Functions of the Brain”, London, Smith, Elder and Company,
1876.
Finsterle, S. and Kowalsky, M. B.: A truncated Levenberg–Marquardt algorithm
for the calibration of highly parameterized nonlinear models, Comput.
Geosci., 37, 731–738, https://doi.org/10.1016/j.cageo.2010.11.005, 2010.
Fukushima, K.: Neocognitron: A self-organizing neural network model for a
mechanism of pattern recognition unaffected by shift in position, Biol.
Cybern., 36, 93–202, https://doi.org/10.1007/BF00344251, 1980.
Gentili, S. and Michelini, A.: Automatic picking of P and S phases using a
neural tree, J. Seismol., 10, 39–63, https://doi.org/10.1007/s10950-006-2296-6, 2006.
Iervolino, I., Giorgio, M., and Manfredi, G.: Expected loss-based alarm
threshold set for earthquake early warning systems, Earthq. Eng. Struct. D.,
36, 1151–1168, https://doi.org/10.1002/eqe.675, 2007.
Kecman, V.: Learning and soft computing: support vector machines, neural
networks, and fuzzy logic models. Massachusetts Institute of Technology
press, Cambridge, MA, 608 pp., 2001.
Kuyuk, H. S., Allen, R. M., H., Brown, M. H., Henson, I., and Neuhauser, D.:
Designing a Network-Based Earthquake Early Warning Algorithm for California:
ElarmS-2, B. Seismol. Soc. Am., 104, 162–173, https://doi.org/10.1785/0120130146, 2014.
Levenberg, K.: A method for the solution of certain non-linear
problems in least squares, Q. Appl. Math., 2, 164–168,
https://doi.org/10.1090/qam/10666, 1944.
Lin, J. W.: Backpropagation neural network (BPNN) as tracing method to trace
physionet EMG signals: a case study, Advanced Studies in Medical Sciences, 5,
63–69, 2017.
Moustafa, A.: Earthquake Engineering – From Engineering Seismology to
Optimal Seismic Design of Engineering Structures, InTech, 408 pp.,
https://doi.org/10.5772/58499, 2015.
Naveen, M., Jayaraman, S., Ramanath, V., and Chaudhuri, S.: Modified Levenberg
Marquardt Algorithm for Inverse Problems, Asia-Pacific Conference on
Simulated Evolution and Learning SEAL 2010: Simulated Evolution and Learning,
623–632, 2010.
Nguyen, D. and Widrow, B.: Improving the Learning Speed of 2-Layer Neural
Networks by Choosing Initial Values of the Adaptive Weights, Information
Systems Laboratory, Stanford University, Stanford, CA 94305, 2009.
Ouazzani, R. M., Salmon, S. J. A. J., Antoci, V., Bedding, T. R., Murphy, S.
J., and Roxburgh, I. W.: A new asteroseismic diagnostic for internal rotation
in Doradus stars, MNRAS, 465, 2294–2309, https://doi.org/10.1093/mnras/stw2717, 2017.
Pavlenko, V.: Ground motion variability and its effect on the probabilistic
seismic hazard analysis, Thesis (PhD), The Faculty of Natural &
Agricultural Sciences, University of Pretoria, Pretoria, 83 pp., 2017.
Peres, D. J. and Cancelliere, A.: Estimating return period of landslide
triggering by Monte Carlo simulation, J. Hydrol., 541, 256–271,
https://doi.org/10.1016/j.jhydrol.2016.03.036, 2016.
Rath, P. S., Barpanda, N. K., Singh R. P., and Panda, S.: A Prototype
Multiview Approach for Reduction of False alarm Rate in Network Intrusion
Detection System, IJCNCS, 5, 49–59, 2017.
Read, L. K. and Vogel, R. M.: Reliability, return periods, and risk under
nonstationarity, Water Resour. Res., 51, 6381–6398,
https://doi.org/10.1002/2015WR017089, 2015.
Sawicki, A., Chybicki, W., and Kulczykowski, M.: Influence of vertical ground
motion on seismic-induced displacements of gravity structures, Comput.
Geotech., 34, 485–497, https://doi.org/10.1016/j.compgeo.2006.12.002, 2007.
Sinha, S., Singh, T. N., Singh, V. K., and Verma, A. K.: Epoch determination
for neural network by self-organized map (SOM), Comput. Geosci., 14,
199–206, https://doi.org/10.1007/s10596-009-9143-0, 2010.
Shin, T. C., Tsai, Y. B., Yeh, Y. T., Liu, C. C., and Wu, Y. M.: International
Handbook of Earthquake and Engineering Seismology, Vol 81B, 1057–1062, 2002.
Wagarachchi, N. M. and Karunananda, A. S.: Towards a Theoretical Basis For
Modeling of Hidden Layer Architecture In Artificial Neural Networks”, Second
International Conference on Advances in Computing, Electronics and
Communication – ACEC 2014, Institute of Research Engineers and Doctors, USA,
47–52, https://doi.org/10.15224/978-1-63248-029-3-73, 2014.
Wu, S., Beck, J. L., and Heaton, T. H.: ePAD: Earthquake Probability-Based
Automated Decision-Making Framework for Earthquake Early Warning, CACAIE, 28,
737–752, https://doi.org/10.1111/mice.12048, 2013.
Wu, Y. M. and Kanamori, H.: Development of an Earthquake Early Warning System
Using Real-Time Strong Motion Signals, Sensors, 8, 1–9,
https://doi.org/10.3390/s8010001, 2008.
Wu, Y. M. and Teng, T. I.: A Virtual Subnetwork Approach to Earthquake Early
Warning, B. Seismol. Soc. Am., 92, 2008–2018, https://doi.org/10.1785/0120010217, 2002.
Xie, L., Tian Q., and Zhang, B.: Simple Techniques Make Sense: Feature
Pooling and Normalization for Image Classification, IEEE T. Circ. Syst. Vid.,
26, 1251–1264, https://doi.org/10.1109/TCSVT.2015.2461978, 2016.
Xu, Z., Ren, C., and Xiao, C.: The ASeismic Design and Nonlinear Dynamic
Analysis of a 350 m High Braced Steel Frame, CTBUH 2015 New York Conference,
561–568, 2015.
Yazdani, A., Salehi, H., and Shahidzadeh, M. S.: A modified three-parameter
lognormal distribution for seismic demand assessment considering collapse
data, KSCE J. Civ. Eng., 22, 204–212, https://doi.org/10.1007/s12205-017-1820-2, 2018.
Zhao, L. H., Cheng, X., Dan, H. C., Tang, Z. P., and Zhang, Y.: Effect of the
vertical earthquake component on permanent seismic displacement of soil
slopes based on the nonlinear Mohr–Coulomb failure criterion, Soils Found.,
57, 237–251, https://doi.org/10.1016/j.sandf.2016.12.002, 2017.