Analysis of Key Parameters in Nearshore Current Using Artificial Neural Networks

Publish Year: 1385
نوع سند: مقاله کنفرانسی
زبان: English
View: 2,618

This Paper With 5 Page And PDF Format Ready To Download

  • Certificate
  • من نویسنده این مقاله هستم

این Paper در بخشهای موضوعی زیر دسته بندی شده است:

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این Paper:

شناسه ملی سند علمی:

ICOPMAS07_114

تاریخ نمایه سازی: 14 فروردین 1385

Abstract:

Design of port and harbor facilities highly depends on the nearshore hydrodynamics. Usually, the significant wave characteristics along with the most severe condition of the nearshore currents based on the field measurements is considered for the design purpose. On the other hand, optimal measurement cost and accurate numerical estimation depends on some key parameters of current velocity. The main objective of present paper is to describe an approach to more accurate and effective prediction of current velocity through key parameterization of observed data based on Root Mean Square (RMS). The procedure has significantly improved by using artificial neural networks due to ANN's capability in high functioning with rapid computation to solve the high nonlinearity and multi-variables systems.

Keywords:

Artificial Neural Network (ANN) , Feed Forward (FF) , Root Mean Square (RMS)

Authors

Abbass Yeganeh Bakhtiary

Department of Civil Engineering, Iran University of Science & Technology, Tehran, Narmak, Iran

Majid Zeinali

Department of Civil Engineering, Iran University of Science & Technology, Tehran, Narmak, Iran

Reza Valipour

Department of Civil Engineering, Iran University of Science & Technology, Tehran, Narmak, Iran

Tarkao Yamashita

RCDE, Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan

مراجع و منابع این Paper:

لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :
  • ASCE Task Committee. 2000. Artificial neural networks in hydrology. I: ...
  • BECKER, A. and KUNDZEWICH, Z.W., 1987. Non linear flood routing ...
  • B HATTA CHARYA, B., PRICE, R.K. and SoLOMATINE, D.P., 2005. ...
  • CHEN, M. T., HUANGA, C. C., PAUMANNB, U., WAELB ROECKC, ...
  • CIGIZOGLU, H.K. and OZGUR, K., 2006. Methods to improve the ...
  • DEO, M.C., GONDANE, D.C., and KUMAR, V.S., 2002. Analysis of ...
  • HSU, K., GUPTA, H.V. and SOROOSHIAN, S., 1995. Artificial neural ...
  • RANJITHAN, S., EHEART, J.W. and GARRETT, J.H., 1993. Neural network ...
  • SuDHEER, K.P., GOSAIN, A.K. and RAMASASTRI, K.S., 2002. A data-driven ...
  • TH IRUMALAIAH, K. and DEO, M.C., 1998. River stage forecasting ...
  • UDELHOVEN, T. and SCHUTT, B., 2000. Capability of feed-forward neural ...
  • ZHANG, B. and GOVIND ARAJU, R.S., 2000b. Modular neural networks ...
  • نمایش کامل مراجع