  Predicting flood for any location at times of extreme storms is a
longstanding problem that has utmost importance in emergency management.
Conventional methods that aim to predict water levels in streams use advanced
hydrological models still lack of giving accurate forecasts everywhere. This
study aims to explore artificial deep neural networks' performance on flood
prediction. While providing models that can be used in forecasting stream
stage, this paper presents a dataset that focuses on the connectivity of data
points on river networks. It also shows that neural networks can be very
helpful in time-series forecasting as in flood events, and support improving
existing models through data assimilation.
