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Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform

Author

Listed:
  • Mohamed Shenify
  • Amir Danesh
  • Milan Gocić
  • Ros Taher
  • Ainuddin Abdul Wahab
  • Abdullah Gani
  • Shahaboddin Shamshirband
  • Dalibor Petković

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 (R 2 ) 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. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Mohamed Shenify & Amir Danesh & Milan Gocić & Ros Taher & Ainuddin 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:p:641-652
    DOI: 10.1007/s11269-015-1182-9
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    Cited by:

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    2. Siriporn Supratid & Thannob Aribarg & Seree Supharatid, 2017. "An Integration of Stationary Wavelet Transform and Nonlinear Autoregressive Neural Network with Exogenous Input for Baseline and Future Forecasting of Reservoir Inflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 4023-4043, September.
    3. P. Biglarbeigi & W. A. Strong & D. Finlay & R. McDermott & P. Griffiths, 2020. "A Hybrid Model-Based Adaptive Framework for the Analysis of Climate Change Impact on Reservoir Performance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4053-4066, October.
    4. Laleh Parviz & Kabir Rasouli & Ali Torabi Haghighi, 2023. "Improving Hybrid Models for Precipitation Forecasting by Combining Nonlinear Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3833-3855, August.
    5. Saeid Mehdizadeh & Javad Behmanesh & Keivan Khalili, 2018. "New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 527-545, January.
    6. Liu, Jinhai & Su, Hanguang & Ma, Yanjuan & Wang, Gang & Wang, Yuan & Zhang, Kun, 2016. "Chaos characteristics and least squares support vector machines based online pipeline small leakages detection," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 656-669.
    7. Madjid Tavana & Salman Nazari-Shirkouhi & Amir Mashayekhi & Saeed Mousakhani, 2022. "An Integrated Data Mining Framework for Organizational Resilience Assessment and Quality Management Optimization in Trauma Centers," SN Operations Research Forum, Springer, vol. 3(1), pages 1-33, March.
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