A New Spatial Algorithm Based on Adaptive Fuzzy Neural Network for Prediction of Crustal Motion Velocities in Earthquake Research

Tosunoglu N. G., Apaydm A.

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, vol.20, no.5, pp.1656-1670, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 5
  • Publication Date: 2018
  • Doi Number: 10.1007/s40815-018-0483-6
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1656-1670
  • Keywords: Earthquake, Crustal motion velocities, Spatial prediction, Variogram, Kriging, Fuzzy logic, Adaptive fuzzy neural network, IDENTIFICATION, PARAMETERS, REGRESSION, RECORDS, MODEL
  • Gazi University Affiliated: Yes


In earthquake studies, different methods are used in modeling of the crustal motions. In case of obscurity data structure, different approaches are needed in solving motion problems. In this paper, a new spatial algorithm has been developed which is based on adaptive fuzzy neural network (AFNN) approach for the prediction of the crustal motion velocities. In order to find the fuzzy class numbers regarding the network model formed by the fuzzification of the studied area, subtractive clustering algorithm is used. In determining the membership function, utilization of the variogram function which models the relationship that depends on distance among spatial data is proposed. The Marmara Region, Turkey, is used as the case for this study. In order to evaluate the performance of the approach, the kriging method is also utilized in the prediction and the results obtained from both methods are compared based on the mean-square-error criteria. It is observed that the AFNN approach yields results which are as effective as those of kriging. Consequently, it is shown that the AFNN approach will contribute to earthquake studies.