# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 1 Introduction
The results show that, for the daily rainfall sequence, the hybrid technique generates a more precise prediction than the single models. Ref [22] developed a one-month WA-ANN coupling model for rainfall forecasting in Iran and demonstrated that the coupled model can equally forecast short- and long-term rainfall occurrences. Ref [28] proposed gene expression programming (GEP) and hybrid WA-GEP models for daily sequence forecasting at two weather stations in Turkey. Results show that the WA-GEP model considerably increases the effectiveness of the results of the single GEP. Ref [29] used dyadic wavelets and neural networks as a means of predicting drought in the Conchos Basin, Mexico, and the findings show that the joint system significantly increased the capacity of neural networks to predict drought. Least-square support vector regression (LS-SVR) has recently received considerable attention in various forecasting problems [30; 31; 32]. In the current study, LS-SVR was chosen because it is computationally more appealing than the traditional support vector regression (SVR). Based on the use of quadratic programming with nonlinear equations, the standard SVR has computational difficulties to decide the optimal solution [33; 34; 35; 36; 37]. The hybrid simulation approach based on LS-SVR as a forecasting model delivers excellent outcomes. For example, [38] suggested singular spectrum analysis (SSA) and the SVR for runoff and rainfall forecasting. The findings show a significant improvement in model efficiency compared to the initial SVR model. [PERSON] [39] developed a WA-SVR hybrid flow forecasting technique. The WA-LSSVR is compared to the LS-SVR, wavelet regression (WR) and linear regression (LR). The results show that the WA-LSSVR is more accurate in river flow forecasting than the aforementioned models. [PERSON] [22] developed an SVR and firefly algorithm (SVR-FFA) at two rainfall stations for one-month rainfall forecasting. The findings demonstrate that the hybrid model is better able to capture the nonlinear features of monthly rainfall than the SVR and MultigeneGenetic Programming models. Ref [40] developed a downscaling method by LS-SVR for improving the downscaling of extreme rainfall. Ref [41] used LS-SVR to forecast the reservoir inflows of Demirkopru Dam in Turkey. The findings promote LS-SVR efficiency. Ref [42] forecast multistep-ahead flow by means of LS-SVR, with the everyday river flow changes modelled prior to forecasting. Ref [43] used LS-SVR to calibrate parameters and to develop models. The study shows that the SVR approach is superior to the Box-Jenkins approach. The study concludes that the explanation for SVR's good performance lies in the non-linear characteristic of the captured and used SVR space. More recently, interest in applications of random forest (RF) has extended to a number of areas [44; 45; 46]. However, there are only few applications of RF in hydrology. [PERSON] [40] applied RF and LS-SVR to boost downscaling of intense precipitation. [PERSON] [47] used RF for rainfall forecasting at Best station on the eastern coast of the Malaysian Peninsula. Ref [48] carried out a comprehensive literature review to compare various approaches of machine learning, including SVR and RF. Ref [49] argues that RF offers a good alternative to SVR and has often performed better than SVR. Ref [50] developed an RF-based evaluation model for evaluating regional flood hazards, and [51] suggested a RF-based drought forecast to predict the monthly standardized precipitation index (SPI) time series. Ref [52] forecasted the start of winter rain by RF in Australia. [PERSON] [53] applied RF in Vietnam to forecast the incoming flood of the Hoa Binh reservoir. Ref [54] proposed RF for daily and monthly rainfall forecasting. Ref [55] compared ANN, SVR, and RF performances in general flood applications and RF delivered the best output.