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Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting

Author

Listed:
  • Quoc Bao Pham

    (Duy Tan University
    Duy Tan University)

  • Tao-Chang Yang

    (National Cheng Kung University)

  • Chen-Min Kuo

    (National Cheng Kung University)

  • Hung-Wei Tseng

    (National Cheng Kung University)

  • Pao-Shan Yu

    (National Cheng Kung University)

Abstract

The study proposed a Standardized Precipitation Index (SPI) drought forecasting model based on singular spectrum analysis (SSA) and single least square support vector machine (LSSVM) with a twofold investigation: (Beguería et al. Int J Climatol, 34(10): 3001–3023, 2014) the forecasting performance of the LSSVM-based model with or without coupling SSA and (Belayneh et al. J Hydrol, 508: 418–429, 2014) the model performances by using different inputs (i.e., antecedent SPIs and antecedent accumulated monthly rainfall) preprocessed by SSA. For the first part investigation, the LSSVM-based model using antecedent SPI as input (LSSVM1) and the LSSVM-based model coupling with SSA using antecedent SPI as input (SSA-LSSVM2) were developed. For the second part of investigation, the SSA-LSSVM-based model using antecedent accumulated monthly rainfall as input (SSA-LSSVM3) was developed and compared to SSA-LSSVM2. The drought indices (SPI3 and SPI6) were chosen as the outputs of the SPI drought forecasting models. The Tseng-Wen reservoir catchment in southern Taiwan was selected to test the aforementioned models. The results show that the forecasting performance of SSA-LSSVM2 is better than that of LSSVM1, which means the input data preprocessed by SSA can significantly increase the accuracy of the SPI drought forecasting. In addition, the performance comparison between SSA-LSSVM2 and SSA-LSSVM3 indicates that using antecedent accumulated monthly rainfalls (i.e., 3-month and 6-month accumulated rainfalls) as input of SSA-LSSVM3 are much better than using antecedent SPIs (i.e., SPI3 and SPI6) as input of SSA-LSSVM2. SSA-LSSVM3 is found to be the most appropriate model for SPI drought forecasting in the case study.

Suggested Citation

  • Quoc Bao Pham & Tao-Chang Yang & Chen-Min Kuo & Hung-Wei Tseng & Pao-Shan Yu, 2021. "Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 847-868, February.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:3:d:10.1007_s11269-020-02746-7
    DOI: 10.1007/s11269-020-02746-7
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    References listed on IDEAS

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    2. Hadeel E. Khairan & Salah L. Zubaidi & Syed Fawad Raza & Maysoun Hameed & Nadhir Al-Ansari & Hussein Mohammed Ridha, 2023. "Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating," Sustainability, MDPI, vol. 15(19), pages 1-22, September.
    3. Neda Khanmohammadi & Hossein Rezaie & Javad Behmanesh, 2022. "Investigation of Drought Trend on the Basis of the Best Obtained Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1355-1375, March.
    4. Kiyoumars Roushangar & Roghayeh Ghasempour & Farhad Alizadeh, 2022. "Uncertainty Assessment of the Integrated Hybrid Data Processing Techniques for Short to Long Term Drought Forecasting in Different Climate Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 273-296, January.

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