Statistical Prediction of Probable Seismic Hazard Zonation of Iran Using Self-organized Artificial Intelligence Model
محل انتشار: ماهنامه بین المللی مهندسی، دوره: 32، شماره: 4
سال انتشار: 1398
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 339
فایل این مقاله در 7 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
این مقاله در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJE-32-4_002
تاریخ نمایه سازی: 10 آذر 1398
چکیده مقاله:
The Iranian plateau has been known as one of the most seismically active regions of the world, and it frequently suffers destructive and catastrophic earthquakes that cause heavy loss of human life and widespread damage. Earthquakes are regularly felt on all sides of the region. Prediction of the occurrence location of the future earthquakes along with determining the probability percentage can be very useful in decreasing the seismic risks. Determining predicted locations causes increasing attention to design, seismic rehabilitation and evaluating the reliability of the present structures in these locations. No exact method has been approved for predicting future earthquake parameters yet. In recent years, more attention is paid to the earthquake magnitude prediction, but no study has been done in the field of probable earthquake occurrence hazard zonation. In this study, locations of future earthquakes in Iran were predicted by self-organized artificial neural networks (ANN). Then probable seismic risk zoning map was drawn by the statistical analyses, and the results indicated that the maps can properly predict future seismic events.
کلیدواژه ها:
Earthquake prediction ، Seismic risk ، Self-organized Artificial Neural Networks ، statistical analysis ، Zonation map
نویسندگان
A. Sivandi-Pour
Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran
E. Noroozinejad Farsangi
Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran