ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, vol.11356, no.023, pp.0-23, 2023 (SCI-Expanded)
Rainfall is a vital process in the hydrological cycle of the globe. Accessing reliable and accurate rainfall data is crucial for water resources operation, flood control, drought warning, irrigation, and drainage. In the present study, the main objective is to develop a predictive model to enhance daily rainfall prediction accuracy with an extended time horizon. In the literature, various methods for the prediction of daily rainfall data for short lead times are presented. However, due to the complex and random nature of rainfall, in general, they yield inaccurate prediction results. Generically, rainfall predictive models require many physical meteorological variables and consist of challenging mathematical processes that require high computational power. Furthermore, due to the nonlinear and chaotic nature of rainfall, observed raw data typically has to be decomposed into its trend cycle, seasonality, and stochastic components before being fed into the predictive model. The present study proposes a novel singular spectrum analysis (SSA)-based approach for decomposing observed raw data into its hierarchically energetic pertinent features. To this end, in addition to the stand-alone fuzzy logic model, preprocessing methods SSA, empirical mode decomposition (EMD), and commonly used discrete wavelet transform (DWT) are incorporated into the fuzzy models which are named as hybrid SSA-fuzzy, EMD-fuzzy, W-fuzzy models, respectively. In this study, fuzzy, hybrid SSA-fuzzy, EMD-fuzzy, and W-fuzzy models are developed to enhance the daily rainfall prediction accuracy and improve the prediction time span up to 3 days via three (3) stations’ data in Turkey. The proposed SSA-fuzzy model is compared with fuzzy, hybrid EMD-fuzzy, and widely used hybrid W-fuzzy models in predicting daily rainfall in three distinctive locations up to a 3-day time horizon. Improved accuracy in predicting daily rainfall is provided by the SSA-fuzzy, W-fuzzy, and EMD-fuzzy models compared to the stand-alone fuzzy model based on mean square error (MSE) and the Nash-Sutcliffe coefficient of efficiency (CE) model assessment metrics. Specifically, the advocated SSA-fuzzy model is found to be superior in accuracy to hybrid EMD-fuzzy and W-fuzzy models in predicting daily rainfall for all time spans. The results reveal that, with its easy-to-use features, the advocated SSA-fuzzy modeling tool in this study is a promising principled method for its possible future implementations not only in hydrological studies but in water resources and hydraulics engineering and all scientific disciplines where future state space prediction of a vague nature and stochastic dynamical system is important.