River stage modeling with a Deep Neural Network using long-term rainfall
time series as input data: Application to the Shimanto-River watershed
Abstract
The increasing frequency of devastating floods from heavy rainfall
associated with climate change has made river stage prediction more
important. For steep, forest-covered mountainous watersheds, deep
learning models may improve prediction of river stages from rainfall.
Here we use the framework of multilayer perceptron (MLP) neural networks
to develop such a river stage model. The MLP is constructed for the
Shimanto river, which lies in southwestern Japan under a mild,
rain-heavy climate. Our input for stage estimation, as well as
prediction, is long-term rainfall time series. With a one-year time
series of rainfall, the model estimates the stage with 50 cm RMSE for
about 10 m of stage peaks as well as accurately simulate stage-time
fluctuations. Furthermore, the forecast model can predict the stage
without rainfall forecasts up to three hours ahead. To estimate the base
flow stages as well as flood peaks with high precision we find the
rainfall time series should be at least one year. This indicates that
the use of a long rainfall time series enables one to model the
contributions of ground water and evaporation. Given that the delay
between the arrival time of rainfall at a rain-gauge to the outlet
change is well simulated, the physical concepts of runoff appear to be
soundly embedded in the MLP.