  We present a machine learning method to predict extreme hydrologic events
from spatially and temporally varying hydrological and meteorological data. We
used a timestep reduction technique to reduce the computational and memory
requirements and trained a bidirection LSTM network to predict soil water and
stream flow from time series data observed and simulated over eighty years in
the Wabash River Watershed. We show that our simple model can be trained much
faster than complex attention networks such as GeoMAN without sacrificing
accuracy. Based on the predicted values of soil water and stream flow, we
predict the occurrence and severity of extreme hydrologic events such as
droughts. We also demonstrate that extreme events can be predicted in
geographical locations separate from locations observed during the training
process. This spatially-inductive setting enables us to predict extreme events
in other areas in the US and other parts of the world using our model trained
with the Wabash Basin data.
