Artificial Neural Network Model to Predict Reservoir Storage

Abstract

The 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 present work cascade forward back propagation and feed forward back propagation network artificial neural network (ANN) models with five different architecture in each by changing the number of hidden layer neurons were developed to predict Harangi reservoir storage, Karnataka, India. The first two years (2010–2012) data on daily basis was used for supervised training and remaining (2013–2014) data on daily basis was used in prediction. The predictive accuracy using the statistical parameters like correlation coefficient (R) and mean absolute percentage error (MAPE) were found within well acceptable limit. Results shows that, compared to the cascade forward back propagation network, feed forward back propagation network architecture of 6-5-1 ANN model with correlation coefficient (R) of 0.9882 and absolute mean percentage error (MAPE) of 5.225%. is best suited for present study.