  Real-time and accurate water supply forecast is crucial for water plant.
However, most existing methods are likely affected by factors such as weather
and holidays, which lead to a decline in the reliability of water supply
prediction. In this paper, we address a generic artificial neural network,
called Initialized Attention Residual Network (IARN), which is combined with an
attention module and residual modules. Specifically, instead of continuing to
use the recurrent neural network (RNN) in time-series tasks, we try to build a
convolution neural network (CNN)to recede the disturb from other factors,
relieve the limitation of memory size and get a more credible results. Our
method achieves state-of-the-art performance on several data sets, in terms of
accuracy, robustness and generalization ability.
