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

Water Resources Management volume 35, pages847–868(2021)Cite this article

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.

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Data Availability

The data and code that are available from the fist author (phambaoquoc@duytan.edu.vn) upon reasonable request.

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Affiliations

  1. Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam

    Quoc Bao Pham

  2. Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang, 550000, Vietnam

    Quoc Bao Pham

  3. Department of Hydraulic and Ocean Engineering, National Cheng Kung University, No. 1, University Rd., Tainan, 701, Taiwan

    Tao-Chang Yang, Chen-Min Kuo, Hung-Wei Tseng & Pao-Shan Yu

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Appendix

Appendix

Results of LSSVM1 without data pre-processing

Fig. 10
figure10

Observed vs. forecasted SPI3 values by LSSVM1 during the calibration and validation periods

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Fig. 11
figure11

Scatter plots of observed vs. forecasted SPI3 values by LSSVM1 during the calibration and validation periods

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Results of SSA-LSSVM hybrid models (SSA-LSSVM2 and SSA-LSSVM3)

Fig. 12
figure12

Observed vs. forecasted SPI3 values by SSA-LSSVM2 model during the calibration and validation periods

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Fig. 13
figure13

Scatter plots of observed and forecasted SPI3 values by SSA-LSSVM2 model during the calibration and validation periods

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Fig. 14
figure14

Observed vs. forecasted SPI3 values by SSA-LSSVM3 model during the calibration and validation periods

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Fig. 15
figure15

Scatter plots of observed and forecasted SPI3 values by SSA-LSSVM3 model during the calibration and validation periods

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Pham, Q.B., Yang, TC., Kuo, CM. et al. Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting. Water Resour Manage 35, 847–868 (2021). https://doi.org/10.1007/s11269-020-02746-7

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Keywords

  • Standardized precipitation index
  • Least square support vector machine
  • Drought forecasting
  • Singular spectrum analysis