# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 1 Introduction  
In hydrological research, timely rainfall forecasts are of major concern, and can be used to deliver valuable data for agrarian development, controlling of water resources, and application of crop insurance [1; 2]. Additionally, accurately forecast rainfalls can be used for reservoir operation and flooding prevention [3; 4]. However, the spatial and temporal rainfall distribution has substantial consequences on the water availability on land and thus on agrarian operations. Considering that agricultural operations and crop production rely on the rainfall distribution, monthly rainfall forecasting is critical for agricultural scheduling and flood control. However, accurate forecasting of monthly rainfall remains a major challenge among hydrologists. Monthly rainfall forecasting with an appropriate method is an essential requirement to support water resources management [5; 6]. Therefore, monthly rainfall forecasting is widely applicable in the field of hydrology [7; 8]. For example, in Taiwan, due to industrial expansion and population growth, the demand for water has risen, and the hydrologic variability has increased, possibly because of the impact of climate change [3]. The forecasting of monthly precipitation is then very critical in Taiwan. Several techniques for forecasting time series have been developed on a global scale [9; 10; 11; 12; 13; 14]. Artificial intelligence or machine intelligence (MI) and stochastic models based on data extraction techniques are the most widely used time series modelling approaches for hydrological forecasting. However, MI has been paid more attention in hydrological forecasting, mainly because stochastic models consider that the time series is stationary and have a limited ability to capture highly nonlinear characteristics of rainfall series [15; 16]. Hydrological time series in actual use are typically non-stationary and non-linear, so predicting maximum values is extremely difficult [17]. Ref [18] states that the forecasts by artificial neural networks (ANNs) are more precise than traditional statistical and numerical methods. Ref [19] shows that by deploying suitable contemporary mathematical methods such as ANNs, superior seasonal rainfall forecasts can be accomplished. The problems of linear predictive models paved the way for the enormous use of models based on MI. For these reasons, the current study justifies the need to apply machine intelligence. Methodologies based on data-driven, MI systems (MI) have been extensively and successfully applied in many fields [20]. MI has become a common inductive strategy in rainfall forecasting owing to its extremely nonlinear, flexible, and data-driven model training without first understanding catchment and flow processes [21]. Despite the popularity of MI models for time series forecasting, they are not an efficient tool to forecast long-term rainfall [22]. The most extensively used MI-based models for rainfall forecasting include support vector machine, genetic programming, ANN, and fuzzy logic [23; 24]. Hybrid approaches for addressing different hydrological problems have been favored by water resources scientists. Recently, hybrid MI concepts have been used for effective modelling and forecasting of rainfall [25]. [PERSON] [26] proposes a hybrid system, the Adaptive Neuro-Fuzzy Inference System (ANFIS), to improve long-term rainfall forecasting. The findings demonstrate that the ANFIS is capable of capturing the rainfall data's dynamic behavior and generates satisfactory outcomes. Ref [27] merged ANNs with wavelet analysis (WA) to forecast rainfalls at an Iranian meteorological station and the hybrid model was contrasted with ANFIS. The findings demonstrate that the hybrid model coupling ANNs with WA is more efficient than the ANFIS and is suitable for rainfall forecasting. Ref [5] compares multiple artificial intelligence techniques to forecast the daily and monthly average rainfall in Fukuoka city, Japan. They proposed a hybrid multi-model and contrasted it to its component models.