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A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction., , , and . Knowl. Based Syst., (2016)Improving Earthquake Prediction with Principal Component Analysis: Application to Chile., , , , and . HAIS, volume 9121 of Lecture Notes in Computer Science, page 393-404. Springer, (2015)A Chilean seismic regionalization through a Kohonen neural network., and . Neural Comput. Appl., 19 (7): 1081-1087 (2010)Improved long time accuracy for projection methods for Navier-Stokes equations using EMAC formulation., , , , and . CoRR, (2022)Prediction of Particlulate Air Pollution using Neural Techniques., and . Neural Comput. Appl., 10 (2): 165-171 (2001)Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula., , , and . Knowl. Based Syst., (2013)Applications of Computational Intelligence in Time Series., , , , and . Comput. Intell. Neurosci., (2017)A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula., , , , , and . Entropy, 17 (7): 5000-5021 (2015)Neural networks to predict earthquakes in Chile., , and . Appl. Soft Comput., 13 (2): 1314-1328 (2013)Using principal component analysis to improve earthquake magnitude prediction in Japan., , , , and . Log. J. IGPL, 25 (6): 949-966 (2017)