Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.


Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations


D. Hammerstrom, “Neural Networks at Work,” IEEE Spectrum, Vol. 30, No. 7, 1993, pp. 46-53. doi:10.1109/6.222230

has been cited by the following article:

  • TITLE: Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

    AUTHORS: Archana Sarkar, Rakesh Kumar

    KEYWORDS: Artificial Neural Networks (ANNs); Event Based Rainfall-Runoff Process; Error Back Propagation; Neural Power

    JOURNAL NAME: Journal of Water Resource and Protection, Vol.4 No.10, October 30, 2012

    ABSTRACT: The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables