Large earthquake occurrence estimation based on radial basis function neural networks

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Χονδροδήμα, Ευαγγελία	el
dc.contributor.author	Ευθυμίου, Ελευθερία - Ειρήνη	el
dc.contributor.author	Παπαδάκης, Γ.	el
dc.contributor.author	Βαλλιανάτος, Φίλιππος	el
dc.date.accessioned	2015-05-15T19:50:25Z
dc.date.issued	2015-05-15
dc.identifier.uri	http://hdl.handle.net/11400/10478
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://ieeexplore.ieee.org/	en
dc.subject	Clustering methods
dc.subject	Earthquakes
dc.subject	Interevent times
dc.subject	Neural networks (NNs)
dc.subject	Radial basis function (RBF)
dc.subject	Μέθοδοι ομαδοποίησης
dc.subject	Σεισμοί
dc.subject	Νευρωνικά δίκτυα
dc.subject	Βάση ακτινικής λειτουργίας
dc.title	Large earthquake occurrence estimation based on radial basis function neural networks	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.contributorName	Τριάντης, Δήμος Α.	el
heal.identifier.secondary	DOI: 10.1109/TGRS.2013.2288979
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2014-09
heal.bibliographicCitation	ALEXANDRIDIS, A.P., CHONDRODIMA, E., EFTHIMIOU, E.-I., PAPADAKIS, G., VALLIANATOS, F., et al. (2014). Large earthquake occurrence estimation based on radial basis function neural networks. IEEE Transactions on Geoscience and Remote Sensing. [Online] 52 (9). p.5443-5453. Available from: http://ieeexplore.ieee.org/	en
heal.abstract	This paper presents a novel scheme for the estimation of large earthquake event occurrence based on radial basis function (RBF) neural network (NN) models. The input vector to the network is composed of different seismicity rates between main events, which are easy to calculate in a reliable manner. Training of the NNs is performed using the powerful fuzzy means training algorithm, which, in this case, is modified to incorporate a leave-one-out training procedure. This helps the algorithm to account for the limited number of training data, which is a common problem when trying to model earthquakes with data-driven techniques. Additionally, the proposed training algorithm is combined with the Reasenberg clustering technique, which is used to remove aftershock events from the catalog prior to processing the data with the NN. In order to evaluate the performance of the resulting framework, the method is applied on the California earthquake catalog. The results show that the produced RBF model can successfully estimate interevent times between significant seismic events, thus resulting to a predictive tool for earthquake occurrence. A comparison with a different NN architecture, namely, multilayer perceptron networks, highlights the superiority of the proposed approach.	en
heal.publisher	IEEE	en
heal.journalName	IEEE Transactions on Geoscience and Remote Sensing	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false