Satish, P.Ramesh, H.2026-02-082018Water Resources and Environmental Engineering I: Surface and Groundwater, 2018, Vol., , p. 57-6497898113204399789811320446https://doi.org/10.1021/acsami.5c03384https://idr.nitk.ac.in/handle/123456789/33948The rapid growth in population increases water demand thus resulting in scarcity of water which is due to improper management rather than lack of resources. Reservoir is the most important source for surface water. So, reservoir storage plays a crucial role in efficient reservoir management. Artificial neural network (ANN) is capable of simulating reservoir storage capacity. So, in the present work five different feed forward back propagation ANN models by varying number of hidden layer neurons were developed for estimation of Harangi reservoir storage, Karnataka, India. The first 2 years (2010–12) data was used for supervised training and remaining data (2013–14) was used in prediction. The predictive accuracy using the statistical parameters like correlation coefficient (R) and mean absolute percentage error (MAPE) were found within the acceptable limit. Result shows that, ANN model with five hidden neurons (i.e., network architecture of 6-5-1) is performing well compared to all other models for prediction of reservoir storage estimation. © Springer Nature Singapore Pte Ltd. 2019.Artificial neural networkFeed forward back propagationPrediction StorageReservoir