  A reliable forecast of inflows to the reservoir is a key factor in the
optimal operation of reservoirs. Real-time operation of the reservoir based on
forecasts of inflows can lead to substantial economic gains. However, the
forecast of inflow is an intricate task as it has to incorporate the impacts of
climate and hydrological changes. Therefore, the major objective of the present
work is to develop a novel approach based on long short-term memory (LSTM) for
the forecast of inflows. Real-time inflow forecast, in other words, daily
inflow at the reservoir helps in efficient operation of water resources. Also,
daily variations in the release can be monitored efficiently and the
reliability of operation is improved. This work proposes a naive anomaly
detection algorithm baseline based on LSTM. In other words, a strong baseline
to forecast flood and drought for any deep learning-based prediction model. The
practicality of the approach has been demonstrated using the observed daily
data of the past 20 years from Bhakra Dam in India. The results of the
simulations conducted herein clearly indicate the supremacy of the LSTM
approach over the traditional methods of forecasting. Although, experiments are
run on data from Bhakra Dam Reservoir in India, LSTM model, and anomaly
detection algorithm are general purpose and can be applied to any basin with
minimal changes. A distinct practical advantage of the LSTM method presented
herein is that it can adequately simulate non-stationarity and non-linearity in
the historical data.
