Deep learning convolutional neural network in rainfall–runoff modelling

Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in
the water cycle among precipitation and evapotranspiration processes, and also geophysical
characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to
difficulties in developing physical and analytical models when traditional statistical methods cannot
simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which
can capture the nonlinear relationship between prediction and predictors, have been rapidly
developed in the last decades and have many applications in the field of water resources. This study
attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with
a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying
two convolutional filters in parallel to separate time series, which allows for the fast processing of
data and the exploitation of the correlation structure between the multivariate time series.
The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho
hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are
compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and
LSTM have better performance than the traditional models, and the statistical performance of the
CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network
is suitable for regression-type problems and can effectively learn dependencies in and between the
series without the need for a long historical time series, is a time-efficient and easy to implement
alternative to recurrent-type networks and tends to outperform linear and recurrent models