• DocumentCode
    3253691
  • Title

    Rainfall forecasting using neural network: A survey

  • Author

    Darji, Mohini P. ; Dabhi, Vipul K. ; Prajapati, Harshadkumar B.

  • Author_Institution
    Dept. of Inf. Technol., Dharmsinh Desai Univ., Nadiad, India
  • fYear
    2015
  • fDate
    19-20 March 2015
  • Firstpage
    706
  • Lastpage
    713
  • Abstract
    An accurate rainfall forecasting is very important for agriculture dependent countries like India. For analyzing the crop productivity, use of water resources and pre-planning of water resources, rainfall prediction is important. Statistical techniques for rainfall forecasting cannot perform well for long-term rainfall forecasting due to the dynamic nature of climate phenomena. Artificial Neural Networks (ANNs) have become very popular, and prediction using ANN is one of the most widely used techniques for rainfall forecasting. This paper provides a detailed survey and comparison of different neural network architectures used by researchers for rainfall forecasting. The paper also discusses the issues while applying different neural networks for yearly/monthly/daily rainfall forecasting. Moreover, the paper also presents different accuracy measures used by researchers for evaluating performance of ANN.
  • Keywords
    agriculture; crops; geophysics computing; neural nets; rain; statistical analysis; ANN; India; accurate rainfall forecasting; agriculture dependent countries; artificial neural networks; climate phenomena; crop productivity; rainfall prediction; statistical techniques; water resources; Artificial neural networks; Biological neural networks; Computer architecture; Forecasting; Prediction algorithms; Predictive models; Training; ANN; forecasting; meteorological parameters; neural networks; rainfall; rainfall prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Engineering and Applications (ICACEA), 2015 International Conference on Advances in
  • Conference_Location
    Ghaziabad
  • Type

    conf

  • DOI
    10.1109/ICACEA.2015.7164782
  • Filename
    7164782