  Due to limited evidence and complex causes of regional climate change, the
confidence in predicting fluvial floods remains low. Understanding the
fundamental mechanisms intrinsic to geo-spatiotemporal information is crucial
to improve the prediction accuracy. This paper demonstrates a hybrid neural
network architecture - HydroDeep, that couples a process-based hydro-ecological
model with a combination of Deep Convolutional Neural Network (CNN) and Long
Short-Term Memory (LSTM) Network. HydroDeep outperforms the independent CNN's
and LSTM's performance by 1.6% and 10.5% respectively in Nash-Sutcliffe
efficiency. Also, we show that HydroDeep pre-trained in one region is adept at
passing on its knowledge to distant places via unique transfer learning
approaches that minimize HydroDeep's training duration for a new region by
learning its regional geo-spatiotemporal features in a reduced number of
iterations.
