“…These data‐driven algorithms intend to establish the empirical relationship between the set of predictor variables (e.g., precipitation, temperature, soil moisture, and discharge data) to estimate streamflow (Alexander et al., 2018; Curceac et al., 2020; Kurian et al., 2020; Senent‐Aparicio et al., 2019). Several machine learning algorithms including artificial neural network, support vector machine, adaptive neuro‐fuzzy inference systems, multilayer perceptron, etc (Curceac et al., 2020; Jimeno‐Sáez et al., 2018; Kasiviswanathan et al., 2013, 2016; Kurian et al., 2020; Mosavi et al., 2018) have been investigated. The key advantages of these algorithms are the non‐requirement of detailed catchment‐related information, the ability to solve complex nonlinear problems, and less computation time (Mosavi et al., 2018; Sudheer, 2005).…”