Runoff sequence feature extraction and prediction using an enhanced
sparse autoencoder
Abstract
For the prediction of the runoff sequence, owing to the
non-stationariness and randomness of the sequence, the prediction
accuracy of extreme runoff is not enough. In this study, the sparse
factor of the loss function in a sparse autoencoder was enhanced using
the inverse method of simulated annealing (ESA), and the runoff of the
Kenswat Station in the Manas River Basin in northern Xinjiang, China, at
9:00, 15:00, and 20:00 daily during June, July, and August in 1998–2000
was considered as the study sequence. When the initial values of the
sparse factor β are 5, 10, 15, 20, and 25, the experiment is designed
with 60, 70, 80, 90, and 100 neurons, respectively, in the hidden layer
to explore the relationship between the output characteristics of the
hidden layer and the original runoff sequence after the network is
trained with various sparse factors and different numbers of neurons in
the hidden layer. Meanwhile, the orthogonal experimental groups ESA1,
ESA2, ESA3, ESA4, and ESA5 were designed to predict the daily average
runoff in September 2000 and compared with the prediction results of the
support vector machine (SVM) and the feed forward neural network (FFNN).
The results indicate that after the ESA training, the output of the
hidden layer consists a large number of features of the original runoff
sequence, and the boundaries of these features can reflect the runoff
series with large changes.Meanwhile, the prediction results of the
orthogonal experiment group indicate that when the number of neurons in
the hidden layer is 90 and β0 = 15, the ESA has the best prediction
effect on the sequence. In particular, the fitting effect on the day of
“swelling up” of the runoff is more satisfactory than those of SVM and
FFNN. The results are significant, as they provide a guide for exploring
the evolution of the runoff under drought and flood as well as for
optimally dispatching and managing water resources.