# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 1 Introduction
Nevertheless, there is no study of RF-based models with a data preprocessing technique for monthly rainfall forecasts, which prompts this current study to introduce a hybrid model coupling RF with a data preprocessing technique. Today, data preprocessing approaches have been applied to break down observed data into subseries so as to boost the accuracy of time series forecasting. Efficient forecast outcomes can be enhanced with independent modeling of subseries. The most extensively applied approaches of preprocessing data can be found in wavelet transformation [56], singular spectrum analysis (SSA) [57], ensemble empirical mode decomposition [58], and principal component analysis [59]. SSA has been applied to many different areas such as medical engineering [60], economics [61] and hydrology [62]. The SSA strategy comprises two phases: decomposition and reconstruction. In this study, only decomposition was implemented. Decomposition into different principle components (PCs) of the initial time series including trend patterns, oscillating components and noise [57]. It is used to conduct a spectrum analysis of the input data, remove high-frequency components, and invert the remaining components to produce a 'filter' time series. In SSA, different parts of a time series can be extracted [63; 64]. For data pretreatment and modeling, SSA has shown that it is a successful algorithm [65; 66]. [PERSON] [17] introduced SSA in order to forecast rainfall at an Indian weather station. The results demonstrate that the SSA technique can separate the different components of the time series efficiently and allow forecasting day-to-day precipitation for a long period, such as one year in a single cycle, with fair precision. [PERSON] [67] compare wavelet analysis with SSA. The results indicate that SSA performs better than WA. [PERSON] [68] propose a hybrid model of an SSA adaptive noise reduction algorithm and a traditional feed-forward neural network forecasting, with the goal of significantly improving short- and long-duration time series forecasting. The findings indicate that the coupled model has superior performance as compared with the forecasts provided directly on raw data by the same network and is therefore well suited for forecasting short and noisy time series with the intrinsic deterministic method of data output. Ref [69] used SSA for decomposing and reconstructing water consumption with six climate variables so as to build a time series for seasonal forecasting. The SSA model is an efficient tool that can split the original time series down into a quantity of separate components, including trend patterns, oscillating components, and noise. Ref [62] applied SSA to annual precipitation, monthly runoff, and hourly water temperature time series to evaluate its capacity and forecasting ability to distinguish significant information from those series. The research concludes that the SSA has the ability to obtain and provide excellent predictions for significant hydrological time series components with distinctive uneven behaviors, such as precipitation and runoff series. Ref [70] introduced SSA to extract a trend. The study showed that SSA is an attractive trend extraction technique, since it needs no model description of time series and trend, extracts noisy time series trends with uncertain oscillations in time series, and is sensitive to outliers. The aforementioned literature shows the significance of using a suitable data preprocessing method (SSA) to raw input signals to supply high quality data before being implemented as a model input. Although a large number of previous studies have been conducted to explore the advantage of coupled data preprocessing and machine leaning, to the best of our knowledge, this is the first time that SSA coupled with LSSVM and RF has been used for monthly rainfall forecasting. Subsequently, these two coupled models (i.e., SSA-LSSVM and SSA-RF) are compared to find a better model for each reservoir watershed.