Towards continuous streamflow monitoring with time-lapse cameras and deep learning

Effective water resources management depends on monitoring the
volume of water flowing through streams and rivers, but collecting
continuous discharge measurements using traditional streamflow
gauges is prohibitively expensive. Time-lapse cameras offer a lowcost
option for streamflow monitoring, but training models for
predicting streamflow directly from images requires streamflow
data to use as labels, which are often unavailable. We address this
data gap by proposing the alternative task of Streamflow Rank Estimation
(SRE), in which the goal is to predict relative measures
of streamflow such as percentile rank rather than absolute flow.
In particular, we use a learning-to-rank framework to train SRE
models using pairs of stream images ranked in order of discharge
by an annotator, obviating the need for discharge training data and
thus facilitating monitoring streamflow conditions at streams without
gauges. We also demonstrate a technique for converting SRE
model predictions to stream discharge estimates given an estimated
streamflow distribution. Using data and images from six small US
streams, we compare the performance of SRE with conventional
regression models trained to predict absolute discharge. Our results
show that SRE performs nearly as well as regression models on
relative flow prediction. Further, we observe that the accuracy of
absolute discharge estimates obtained by mapping SRE model predictions
through a discharge distribution largely depends on how
well the assumed discharge distribution matches the field observed
data.