Accurate and timely monthly rainfall forecasting is essential for reservoir operation and flooding control because it can support water resources planning and management activities. Therefore, construction of monthly rainfall forecasting models in reservoir watersheds is necessary for generating future rainfall amounts as an input to a waterresources-system simulation model to predict water shortage conditions. This research aims to examine the reliability of linking two data pre-processing methods (singular spectrum analysis, SSA and discrete wavelet transform, DWT) with machine learning, least-squares support vector regression (LSSVR), and random forest (RF), for monthly rainfall forecasting in three reservoir watersheds (Shihmen, Deji, and Zengwen Reservoir watersheds) located in northern, middle and southern Taiwan respectively.
Here, data pre-processing (i.e. SSA, DWT) is employed to pre-process the raw input signals to provide data of high quality to LSSVR and RF. The proposed models were alibrated and validated using the watersheds’ observed areal monthly rainfalls which is separated into 70 percent of data for calibration and 30 percent of data for testing.
Model performances were evaluated using two accuracy measures, root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). The results show that LSSVR with data pre-processing techniques such as SSA and DWT is the most favorable technique among all the assessed, which makes (DWT-LSSVR and SSA-LSSVR) a promising alternative method for forecasting monthly rainfall. Although the performances of hybrid RF (i.e. SSA-RF and DWT-RF) are less favorable, they are nevertheless good forecasting approaches. Overall, the hybrid models significantly surpass the standard models for the three studied watersheds, which indicates that the proposed models are a prudent modeling approach that could be employed in the current research regions for monthly rainfall forecasting.

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