Antonio Morales Esteban-rekin lankidetzan egindako argitalpenak (22)

2024

  1. Emerging trends in big data analytics and natural disasters

    Computers and Geosciences

2021

  1. Mahalanobis clustering for the determination of incidence-magnitude seismic parameters for the Iberian Peninsula and the Republic of Croatia

    Computers and Geosciences, Vol. 156

2019

  1. Big data and natural disasters: New approaches for spatial and temporal massive data analysis

    Computers and Geosciences

2018

  1. Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure

    Computers and Geosciences, Vol. 115, pp. 198-210

  2. Mapping of seismic parameters of the Iberian Peninsula by means of a geographic information system

    Central European Journal of Operations Research, Vol. 26, Núm. 3, pp. 739-758

2017

  1. Comparing seismic parameters for different source zone models in the Iberian Peninsula

    Tectonophysics, Vol. 717, pp. 449-472

  2. Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks

    Neural Computing and Applications, Vol. 28, Núm. 5, pp. 1043-1055

  3. Using principal component analysis to improve earthquake magnitude prediction in Japan

    Logic Journal of the IGPL, Vol. 25, Núm. 6, pp. 949-966

2016

  1. A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction

    Knowledge-Based Systems, Vol. 101, pp. 15-30

  2. Comparison between Utsu's and Vere-Jones' aftershocks model by means of a computer simulation based on the acceptance-rejection sampling of von Neumann

    Tectonophysics, Vol. 682, pp. 108-119

2015

  1. A novel method for seismogenic zoning based on triclustering: Application to the Iberian Peninsula

    Entropy, Vol. 17, Núm. 7, pp. 5000-5021

  2. Detecting precursory patterns to enhance earthquake prediction in Chile

    Computers and Geosciences, Vol. 76, pp. 112-120

  3. Improving earthquake prediction with principal component analysis: Application to Chile

    Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

  4. Stability analysis of a slope subject to real accelerograms by finite elements. Application to San Pedro cliff at the Alhambra in Granada

    Soil Dynamics and Earthquake Engineering, Vol. 69, pp. 28-45

2014

  1. A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning

    Computers and Geosciences, Vol. 73, pp. 132-141

  2. Utilización de un sistema de información geográfica para el análisis de las zonificaciones sismogenéticas existentes

    Nuevas técnicas, mismos fundamentos

2013

  1. Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula

    Knowledge-Based Systems, Vol. 50, pp. 198-210

  2. Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence

    Tectonophysics, Vol. 593, pp. 121-134

  3. Neural networks to predict earthquakes in Chile

    Applied Soft Computing Journal, Vol. 13, Núm. 2, pp. 1314-1328

2012

  1. Probabilistic method to select calculation accelerograms based on uniform seismic hazard acceleration response spectra

    Soil Dynamics and Earthquake Engineering, Vol. 43, pp. 174-185