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INTRODUCTION
Rainfall–runoff modeling is a versatile tool for water resource planning and management, city planning, flood, land use, etc. (Ghumman et al. 2011). It also helps in minimizing the effect of drought-related issues on water resources. Owing to change in climatic conditions in recent decades because of global warming, the hydrological cycle in India has been adversely affected (Sonali & Nagesh Kumar 2016a) and due to anthropogenic activities there is an increase in global surface temperature which is clear from various evidence (Sonali & Nagesh Kumar 2016b). Many hydrological models have been developed since 1850, ranging from mathematical relations between them, empirical models, conceptual ones in which the physical processes are based on physical laws existing in nature and physical models which are small-scale prototypes of the models (Todini 2007). Conceptual and physical models account for all the physical processes involved in the catchment but they are very data-intensive and time-consuming (Sušanj et al. 2016). These types of models are not suitable for areas which suffer from data scarcity and poorly managed data sites. General time series models such as auto regressive integrated moving average (ARIMA) are popularly used for hydrological time series forecasting (Nourani et al. 2009), but Mujumdar & Kumar (1990) suggested that ARIMA should be avoided due to an increase in variance continuously on differencing the series. Also, these models are linear in nature and do not account for the non-stationarities and non-linearities in hydrologic time series data. ARIMA is commonly used for the hydrological time series data which have periodicity over time (Zhang et al. 2011). Moreover, seasonal variability is also one of the climatic factors for runoff variation (Bekele et al. 2021).
Nowadays, ANN models are popularly used to develop the rainfall–runoff (RR) relationship and they are black-box models which are data-driven and give the relation between rainfall and runoff without considering the physical explanation of processes involved (Todini 2007). These are mathematical models with repetitive iterations which help in the development of some non-linear relationships between two hydrological phenomena, rainfall and runoff (Poonia & Tiwari 2020). They also do not require prior knowledge of physical processes and morphometry of the basin for prediction. ANN consists of three layers: input layer, hidden layer, and output layer (Figure 1). The input...