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

Quoc Bao Pham, Tao Chang Yang, Chen Min Kuo, Hung Wei Tseng, Pao Shan Yu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)847-868
Number of pages22
JournalWater Resources Management
Volume35
Issue number3
DOIs
Publication statusPublished - 2021 Feb

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Water Science and Technology

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