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Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

Author

Listed:
  • R. Venkata Ramana
  • B. Krishna
  • S. Kumar
  • N. Pandey

Abstract

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their time-frequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models. Copyright The Author(s) 2013

Suggested Citation

  • R. Venkata Ramana & B. Krishna & S. Kumar & N. Pandey, 2013. "Monthly Rainfall Prediction Using Wavelet Neural Network Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3697-3711, August.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:10:p:3697-3711
    DOI: 10.1007/s11269-013-0374-4
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    References listed on IDEAS

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