Recurrent Neural Network for Approximate Earthquake Time and Location Prediction Using Multiple Seismicity Indicators

This paper presents a computational approach for predicting the location and time of occurrence of future moderate-to-large earthquakes in an approximate sense based on neural network modeling and using a vector of 8 seismicity indicators as input. Two different methods are explored. In the first method, a large seismic region is subdivided into several small subregions and the temporal historical earthquake record is divided into a number of small equal time periods. Seismicity indicators are computed for each subregion for each time period and their relationship to the magnitude of the largest earthquake occurring in that subregion during the following time-period is studied using a recurrent neural network. In the second more direct approach, the temporal historical earthquake record is divided into a number of unequal time periods where each period is defined as the time between large earthquakes. Seismicity indicators are computed for each time-period and their relationship to the latitude and longitude of the epicentral location, and time of occurrence of the following major earthquake is studied using a recurrent neural network.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/10939687
• Authors:
  • Panakkat, Ashif
  • Adeli, Hojjat
• Publication Date: 2009-5

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 280-292
• Serial:
  • Computer-Aided Civil and Infrastructure Engineering
  • Volume: 24
  • Issue Number: 4
  • Publisher: Blackwell Publishing
  • ISSN: 1093-9687
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8667

Subject/Index Terms

• TRT Terms: Bridge foundations; Earthquake engineering; Earthquakes; Neural networks; Seismicity; Vibration
• Subject Areas: Bridges and other structures; Highways; I24: Design of Bridges and Retaining Walls;

Filing Info

• Accession Number: 01126264
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 13 2009 2:58PM