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Prediction of River Basin-Scale Water Yield Using Artificial Neural Networks

  • Satyawan D. JagdaleEmail author
  • S. S. Kashid
Conference paper

Abstract

River basin yield is influenced by rainfall and rainfall is influenced by various global climatic inputs. In this study, the prediction of river basin scale yield of ‘Upper Bhima River basin’ from the Maharashtra State of India has been attempted. The global climatic inputs, namely El Nino-Southern Oscillation (ENSO) index, and Equatorial Indian Ocean Oscillation (EQUINOO) index were used for the prediction. The Artificial Neural Network (ANN) tool has been used through Matlab for this purpose. Upper Bhima river basin consists of many dams and reservoirs harnessing water for irrigation, hydropower and domestic uses. Inflow, outflow, water use and losses of all reservoirs are taken into consideration while calculating actual yield Upper Bhima River Basin. Four combinations of input variables for predicting reservoir yield were tested to arrive at the best input variable combination for better predictions. From the analysis, it was found that models developed using ANN establish reasonably good relationships between climate variables and basin scale yields The best combination of ENSO and EQUINOO gave correlation coefficient r = 0.712 between observed river basin yield and predicted river basin yield, which appears attractive for such a complex system.

Keywords

River basin scale yield Artificial neural network ENSO EQUINOO 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Walchand Institute of TechnologySolapurIndia

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