## 1 Introduction  
It is widely acknowledged that the number and diversity of water-related challenges are large and are expected to increase in the future. So, hydrological modeling can be efficient in order to analyze, understand, and explore solutions for sustainable water management in order to support decision-makers and operational water managers [1]. In recent years, many water-experts have tried to use a range of methods for predicting hydrological components such as streamflow [2]. The prediction of streamflow is essential in many aspects of water resources management, for example, reservoir operation and allocation. Also, streamflow can directly interfere with floods, droughs, agricultural issues, drinkable water supply, and all water-related systems [3]. Predicting streamflow has been conducted by researches on different scales such as hourly scales [4], daily scales [5], 10 days scales [6; 7; 8], monthly scales [9], and annual scales [10]. It is a difficult and challenging task for modelers to predict streamflow due to its complex (nonlinear) and non-deterministic characteristics. Hence, the execution of all simulation and predicting models is strongly based on the quality of the input data [11]. Data-Driven models (DDMs), which are a combination of two main groups called Artificial Intelligence (AI) and time-series methods, are popular because of the ability to compute, their influential theory, and their implicit process in modeling [12]. Recent reviews have shown that there is a great range of DDMs (such as ELM and CHAID) application for hydrological variables modeling, conducting several types of research in various topics such as daily water temperature [13], daily streamflow [5], dissolved oxygen concentration [14], and salinity data [15] estimation. [PERSON] et al. considered the Online Sequential ELM (OSELM) model for flood forecasting in the case of Neckar River, Germany. They tested the applied model by three evaluation techniques, and their results were compared with commonly used models like Support Vector Machines (SVM), Artificial Neural Networks (ANN), and genetic programming and they found out that the OSELM method can be used as a flood events alerting method rather than the aforementioned methods [16]. [PERSON] et al. conducted the OPELM model for the Tigris River in Iraq, and they used five groupings of inputs with lagged data. However, the results of the OPELM method were compared with the results of two other models, namely Support Vector Regression (SVR) and Generalized Regression Neural Network (GRNN) and it has been found that the OPELM model is superior in comparison to the previously mentioned methods [17]. [PERSON] and [PERSON] conducted three different soft computing methods, including Multilayer Perceptron Neural Network (MLPNN), OPELM, and evolutionary polynomial regression (EPR) as the sole model in forecasting daily streamflow in three stations on the Tajan River of Iran. The outcomes of this study reveal that the EPR method performed better than two other methods [5]. CHAID model is one of the classification based techniques, which is used in determining the linearity or non-linearity of streamflow processes [18; 19; 20; 21]. [PERSON] et al. conducted CHAID and Multivariate Logistic Regression (MLR) models for extracting landslide maps in the case of the Pohang-Kyeong Joo catchment (Korea). The outcomes showed that both models have excellent efficiency in mapping [22]. DDMs have some advantages and disadvantages in estimating the deterministic part of streamflow equations. For example, they are excellent for modeling the deterministic and algebraic parts of these variables, but they are not so useful for modeling the stochastic part of streamflow because of its nonlinear behavior.