An innovated integrated model using Singular Spectrum Analysis and Support Vector Regression optimized by intelligent algorithm for rainfall forecasting

Journal: Journal of Autonomous Intelligence DOI: 10.32629/jai.v2i1.37

Weide Li, Juan Zhang

Lanzhou University


Rainfall forecasting is becoming more and more significant and precipitation anomalies would lead to droughts and floods disasters. However, because of the complexity and non-stationary of rainfall data, it is difficult to forecast. In this paper, a novel hybrid model to forecast rainfall is developed by incorporating singular spectrum analysis (SSA) and dragonfly algorithm (DA) into support vector regression (SVR) method. Firstly, SSA is used for extracting the trend components of the hydrological data. Then, SVR is utilized to deal with the volatility and irregularity of the precipitation series. Finally, the parameter of SVR is optimized by DA. The proposed SSA-DA-SVR method is used to forecast the monthly precipitation for Songbai, Panshui, Lanma and Jiulongchi stations. To validate the efficiency of the method, four compared models, DA-SVR, SSA-GWO-SVR, SSA-PSO-SVR, SSA-CS-SVR are established. The result shows the proposed method has the best performance among all five models, and its prediction has high precision and accuracy.


Prediction ; Precipitation ; Singular Spectrum Analysis ; Support Vector Regression ; Intelligent Algorithm


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