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Stream-Flow Forecasting of Small Rivers Based on LSTM
arXiv - CS - Machine Learning Pub Date : 2020-01-16 , DOI: arxiv-2001.05681 Youchuan Hu, Le Yan, Tingting Hang and Jun Feng
arXiv - CS - Machine Learning Pub Date : 2020-01-16 , DOI: arxiv-2001.05681 Youchuan Hu, Le Yan, Tingting Hang and Jun Feng
Stream-flow forecasting for small rivers has always been of great importance,
yet comparatively challenging due to the special features of rivers with
smaller volume. Artificial Intelligence (AI) methods have been employed in this
area for long, but improvement of forecast quality is still on the way. In this
paper, we tried to provide a new method to do the forecast using the Long-Short
Term Memory (LSTM) deep learning model, which aims in the field of time-series
data. Utilizing LSTM, we collected the stream flow data from one hydrologic
station in Tunxi, China, and precipitation data from 11 rainfall stations
around to forecast the stream flow data from that hydrologic station 6 hours in
the future. We evaluated the prediction results using three criteria: root mean
square error (RMSE), mean absolute error (MAE), and coefficient of
determination (R^2). By comparing LSTM's prediction with predictions of Support
Vector Regression (SVR) and Multilayer Perceptions (MLP) models, we showed that
LSTM has better performance, achieving RMSE of 82.007, MAE of 27.752, and R^2
of 0.970. We also did extended experiments on LSTM model, discussing influence
factors of its performance.
更新日期:2020-01-17