Abstract
Objectives: The main objective of this research is to determine the Hydrological (Rainfall-Runoff) model by using Artificial Neural Networks (ANNs) for the study area. Methods: The ANNs model was applied to the relative impact of different climatic variables such as Rainfall, Temperature, Cloud Cover, Potential Evapotranspiration, and Relative Humidity for the district of Bankura located on Lower Gangetic Plain (Zone no-III) in India. This study has also developed runoff hydrograph using various Slope, Rainfall Intensity, and Roughness over the catchment. The researcher has collected the Real-Time data series of 116 years(1901-2016) for the six meteorological stations of district Bankura from India Meteorological Department, Pune. For estimating runoff values, the study has been used Kothyari and Garde equation in which the most important factor i.e. the Vegetal Cover Factor (Fv) was considered. For developing the ANNs model, the available data were separated as 70% for training, 15% for testing, and 15% for validation. Findings: The Predicted values using ANNs model are more useful for better estimation of water resources management than previous researches. The model performance was with better efficiency (Nash-Sutcliffe Efficiency) and it was greater than 97%. Novelty: First time, this research established the Hydrological (Rainfall-Runoff) model by using Artificial Neural Networks(ANNs) for the study area. Keywords: Artificial neural networks; climate change; meteorological data; water resources management; runoff; district Bankura
Highlights
Rainfall-Runoff plays a major role in Hydrology and Water Resources
The water resources management using Artificial Neural Networks (ANNs) which involves the estimation of Rainfall-Runoff event, river discharge forecasting, Climate Change, river inflow modeling and estimation of groundwater, etc [9,10,11]
The ANNs were developed to an observed daily runoff as a function of daily rainfall, temperature, cloud cover, potential evapotranspiration, and relative humidity
Summary
ANNs are a physical-based and black box model which is a useful tool in hydrology [1,2]. Artificial Neural Networks (ANNs) have become one of the vital tools for modeling of complex hydrological processes. The water resources management using ANNs which involves the estimation of Rainfall-Runoff event, river discharge forecasting, Climate Change, river inflow modeling and estimation of groundwater, etc [9,10,11]. The ANNs were developed to an observed daily runoff as a function of daily rainfall, temperature, cloud cover, potential evapotranspiration, and relative humidity. This model is accurately predicting the basin response to rainfall. The physiological catchment character such as ground slope, vegetal cover factor (land use), and roughness is considered in this model
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