Accurate and reliable prediction and estimation of hydrological andmeteorological at different time and space scales are important for sustainable water and environmental planning and management, as well as all water-related disasters. Furthermore, in order to achieve proper decisions and measures, trustworthy information and data are required. Therefore, data and information can be considered the “life blood” of appropriate water decisions and measures (Jahanddideh-Tehrani et al., 2021). Modeling a natural phenomenon is challenging for researchers due to the complex and nonlinear relationships between its components. Over the years, data-driven models have become popular among researchers. Such models contain mathematical equations that have been derived from the analysis of concurrent input and output time series not derived from the physical process of the basin (Solomatine, 2005; Solomatine & Ostfeld, 2008).
