  The objective of this study is to create and test a hybrid deep learning
model, FastGRNN-FCN (Fast, Accurate, Stable and Tiny Gated Recurrent Neural
Network-Fully Convolutional Network), for urban flood prediction and situation
awareness using channel network sensors data. The study used Harris County,
Texas as the testbed, and obtained channel sensor data from three historical
flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day flood, and 2017
Hurricane Harvey Flood) for training and validating the hybrid deep learning
model. The flood data are divided into a multivariate time series and used as
the model input. Each input comprises nine variables, including information of
the studied channel sensor and its predecessor and successor sensors in the
channel network. Precision-recall curve and F-measure are used to identify the
optimal set of model parameters. The optimal model with a weight of 1 and a
critical threshold of 0.59 are obtained through one hundred iterations based on
examining different weights and thresholds. The test accuracy and F-measure
eventually reach 97.8% and 0.792, respectively. The model is then tested in
predicting the 2019 Imelda flood in Houston and the results show an excellent
match with the empirical flood. The results show that the model enables
accurate prediction of the spatial-temporal flood propagation and recession and
provides emergency response officials with a predictive flood warning tool for
prioritizing the flood response and resource allocation strategies.
