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Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform

Author

Listed:
  • Mohamed Shenify

    (Albaha University)

  • Amir Seyed Danesh

    (University of Malaya)

  • Milan Gocić

    (University of Niš)

  • Ros Surya Taher

    (Universiti Teknologi MARA)

  • Ainuddin Wahid Abdul Wahab

    (University of Malaya)

  • Abdullah Gani

    (University of Malaya)

  • Shahaboddin Shamshirband

    (University of Malaya)

  • Dalibor Petković

    (University of Niš)

Abstract

Precipitation prediction is of dispensable importance in many hydrological applications. In this study, monthly precipitation data sets from Serbia for the period 1946–2012 were used to estimate precipitation. To fulfil this objective, three mathematical techniques named artificial neural network (ANN), genetic programming (GP) and support vector machine with wavelet transform algorithm (WT-SVM) were applied. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2) were used to evaluate the performance of the WT-SVM, GP and ANN models. The achieved results demonstrate that the WT-SVM outperforms the GP and ANN models for estimating monthly precipitation.

Suggested Citation

  • Mohamed Shenify & Amir Seyed Danesh & Milan Gocić & Ros Surya Taher & Ainuddin Wahid Abdul Wahab & Abdullah Gani & Shahaboddin Shamshirband & Dalibor Petković, 2016. "Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 641-652, January.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:2:d:10.1007_s11269-015-1182-9
    DOI: 10.1007/s11269-015-1182-9
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